From 369f153ab050c8b0db0dac8c6f9405f84bb831de Mon Sep 17 00:00:00 2001 From: James Kerns Date: Thu, 4 Jan 2024 09:38:55 -0600 Subject: [PATCH 1/5] add nuclear module --- docs/source/changelog.rst | 6 + docs/source/images/cor_sine.png | Bin 0 -> 34704 bytes docs/source/images/cor_x_residuals.png | Bin 0 -> 23308 bytes docs/source/images/cor_y_residuals.png | Bin 0 -> 24111 bytes docs/source/images/four_bar_img.png | Bin 0 -> 22957 bytes docs/source/images/four_bar_x.png | Bin 0 -> 33788 bytes docs/source/images/four_bar_y.png | Bin 0 -> 33393 bytes docs/source/images/max_count_rate_plot.png | Bin 0 -> 32299 bytes docs/source/images/planar_uniformity_plot.png | Bin 0 -> 55594 bytes docs/source/images/quadrant_fwhm.png | Bin 0 -> 28655 bytes docs/source/images/quadrant_image.png | Bin 0 -> 176608 bytes docs/source/images/quadrant_mtf.png | Bin 0 -> 27683 bytes docs/source/images/tomo_contrast_graph.png | Bin 0 -> 35001 bytes docs/source/images/tomo_contrast_plot.png | Bin 0 -> 69964 bytes .../images/tomo_contrast_uniformity.png | Bin 0 -> 69930 bytes docs/source/images/tomo_res_x.png | Bin 0 -> 26407 bytes docs/source/images/tomo_uniformity_plot.png | Bin 0 -> 51534 bytes docs/source/index.rst | 1 + docs/source/nuclear.rst | 534 ++++++ docs/source/topics/mtf.rst | 19 + pylinac/core/contrast.py | 2 +- pylinac/core/geometry.py | 27 + pylinac/core/image.py | 38 +- pylinac/core/mtf.py | 138 +- pylinac/core/roi.py | 16 +- pylinac/metrics/image.py | 32 + pylinac/nuclear.py | 1689 +++++++++++++++++ tests_basic/core/test_geometry.py | 28 +- tests_basic/test_nuclear.py | 432 +++++ 29 files changed, 2953 insertions(+), 9 deletions(-) create mode 100644 docs/source/images/cor_sine.png create mode 100644 docs/source/images/cor_x_residuals.png create mode 100644 docs/source/images/cor_y_residuals.png create mode 100644 docs/source/images/four_bar_img.png create mode 100644 docs/source/images/four_bar_x.png create mode 100644 docs/source/images/four_bar_y.png create mode 100644 docs/source/images/max_count_rate_plot.png create mode 100644 docs/source/images/planar_uniformity_plot.png create mode 100644 docs/source/images/quadrant_fwhm.png create mode 100644 docs/source/images/quadrant_image.png create mode 100644 docs/source/images/quadrant_mtf.png create mode 100644 docs/source/images/tomo_contrast_graph.png create mode 100644 docs/source/images/tomo_contrast_plot.png create mode 100644 docs/source/images/tomo_contrast_uniformity.png create mode 100644 docs/source/images/tomo_res_x.png create mode 100644 docs/source/images/tomo_uniformity_plot.png create mode 100644 docs/source/nuclear.rst create mode 100644 pylinac/nuclear.py create mode 100644 tests_basic/test_nuclear.py diff --git a/docs/source/changelog.rst b/docs/source/changelog.rst index 1fec6ee6e..a5da77453 100644 --- a/docs/source/changelog.rst +++ b/docs/source/changelog.rst @@ -11,6 +11,12 @@ Core * The efficient DICOM stack introduced in the last version did not allow for writing images back to the stack (e.g. when manipulating the image). Images can now be written back to efficient stacks. +Nuclear +^^^^^^^ + +* A new module has been created. This module is a Python implementation of the NMQC toolkit for SPECT. + It contains 9 tests that are very similar to the ImageJ toolkit. See :ref:`nuclear` for more. + Profiles ^^^^^^^^ diff --git a/docs/source/images/cor_sine.png b/docs/source/images/cor_sine.png new file mode 100644 index 0000000000000000000000000000000000000000..bdd367c107afecdb67dbcb7902ec61ef0814048b GIT binary patch literal 34704 zcmdqJbyStz*EYHdL6mSyD!pk*5$RGvIur$@JETieKoQu4AfR-Lh$xMebT^2IfOI3$ z-F$QVd*Ao_#u;afbI!lVF&xk1X5Z^xG1rW1UKb(is&Yh^u3thR5JU>{G7k|5Y<~m- zYw98%d~&;gY!3b-;wr1{s^MVa>Tc|8j!-dnb+mPGwY4^3cxLYGV(nmmi$|D;kBh;| z)z#5Ol$Y1;f4;!u;B3izyQuUF3_{>2|HK7>ATh@L#rhc$SXDYJ>SdZ2F|$FG*^b=ZUg+V_%%x&vWF+I!i^n6n&S%_+`{kaC&1f07 zZkes|aG`N`;_WcScQ>-0*#G#T&>eqs_FM1iZ<~S#S&HPJwF-#qD_s^VR?9{!W+TKZ z9H;Mo-Xf7aU=VRxn5%z*Gju8{&cV)(o~ZG@WjoB*n<8fECXAY4vDLY(E%Pg1Unxb@ zja}y}yWMD6mQlmYmC^D@x4*xNY=(HAt&RQR@jg58#Ky+Hc}kE?#6{W}f?&7RRZ@^FA{(;ihR2miFxopT3dses=fXJ?kGI2Wmt&KR?R3 z8$l!Tkd|8P8P`a$d8S6TDq?4SG7Zczi9_R~UJ3cyQh#Rk&JU$Aw;}zx_NaiuLOwd5 zjXS<)hl^zMzsu~Dz%J`f_jE%~iKqpc-Pgv}M!T(du}IY#h*e~r83nIBlx|*lms2Td7*z71WPR;OFuW-xk$2(KpCp_;hF8#`*1_QL% zm}-Ev2RqHS_LtgdNuKU#xFEw`^eKRe!88Hw>;t6WTU+iIitD}+s>^E+`0rR423^gC^N%c1e< z@fV7^vm?&HOXS#7Fm>a@t@*EZqp`495uC=v7FAd5QR)iI0DC z%Y=Ku%*tA9)sKpxms0Ib78y6^)XYU2Q|0Pc#Rk)PEhQF($+KFb(UWjH5{%17iK@o! zALZ!`E{&EK6uJI+INKO_Ij56?Q?uJ@v@Gvwy4q>y%CTeb-DUIP)n~H@ zo3la2RIME`Y_Ihyov-yt9@~a)(~7#bwb-A|!*~Dn^>-$ITf1Oys}oxJ zdLo&cc{+c*R>1=bAWR!y5omGEI#-4-4}PKcM_#*TohC7wMJ+$_+#<&lkMug-!{$7 z%~!5oZG_{__d1)SsLfJ}b6)69_!6t)`&=%9MnP)@6*#S|ZySFS;gXv^lkVB)L;QSUu2qln+MaAC6)-=Tzs<9!adqNMzM z&-Uxivz9aQ`Mh?lw&psT)TBrM)0TEYgZ+#NourYP3>99j(*GQvev-X7~H zSl?pSmpO?8N2I_AKJrn%Ucz)!@GZ|RGa6!i*tEmjY3^B36&)eLUGv!8zWz>5PC2y{ zJbE5~4VvBBlZ1F3IUbxA!0r1{n~rAg zwZEp)lXyEp!dp-&j)#SZC&GEYv-0n(GZ@1&wnngV1+5d)*6?Y+^J99_9(QuIHQyDNza1*S@bL}<%2~F9l$&1MW8-7r<6<*~dYVTM>U>X=Bqs;; z)Ht>J-^+F+-sY@wUsv5e;4G+aYzV-AQsKy$k&&Ss&-VgO2V!jN_c}kn`s$kJl`t19 zK8FeM<)1lCU#$lof(t+K_$;#mcj~LdWNNW_*TX!W+Y?@Yx#^zoJb+{N=^1hy(sy@) zznh($1%=O6bs%c?dau{SnbbDV4QYK;POwQ;li`;tMTA}iL$sd2K3zz1K(On7+fgyh z1cQDR7)zz=OyYYAiT#mAv?QH97$ngZy(U^;Qx}ccf_J%1TZ%#@r05`IU)hg; zb6V)WhI!fk4@;Y&{P_VjDV7^ILibijCwFc)Ha5nC7qI*Nb$Y^keLg2U6$PWOj90}K z)SV@Qr`TSrTAz5SWvc4Q4;D4yF-v4$wJO-y)Rd!JzDC|9wbjmq3Hl#CFvCy?m+!4E zeG+mFFE40({qRD>P6-_6pI>>SkMTRicfQ}l+(XPsJRSU8WZp#q*Y94)RhEZub!&Y@ zApJ3cKrsfJk5EpNOaUvrW%Zo_PAgBKug>St`P;MA&e=$*-qrHS-j*=RgxfY0&#F?| z=)AdPM=C0W;o#P4=ebe7y+3ZYqh`n{cnxe_m%cY(QlD_}Csa?0&2D1mo(W;06+HjD z_bcx{z8EPqz8C6zo>Dxwg$aKG;1jH0y?RB(q7*9tQ?NWan2pqWz3KJwg^26YoBj_9 zq)Y+=KN0yNEZs1MIW8XF^%9F7=XD6bxmZ*XyOg#?GUXx#5ln)Dd!dUG;9NIn!g#Mk zK6xKp=xdDgB_JTUpQlqgc9VsL1epRN9uh4<$X%gI#xWAa<0^@$oxp5wDW?*6F2V0_up{u(5r9nma* zChO|jhIlBD>;Ih}Tagu_;ZNEFi1H}ZjOAMd7%TeV8jC+BjrP4ykpBOZA7kec*T+@r z@etDpOIum73nuj}JA(gXrX_5en(K%myYUF^k2trztby%8iusP2*@r=6SbICsaJd7$ zY$#cgD4`NNRy6^5DE~veOP4QKO>Ya+5XW-s-gk8sxO3-D`HgC21-EySpR;!!NMuDW zb3A{f_Ggx8_wC!aGBDcldWF-WM4e?`g4XNwHvEa&|gbWh4PNlM^l!fTa2`aZ!#Gl7xt}UM@kVhGE&rPBasRpan+mYQ$%n|ER#rgPmee~u_y&= zKXA-0btpc?5_ES;>Pa4Q*uOd@6(bghKt1fnu~L7Bdt{c>&O|B6*0=t5`p@q7Dk0aE ziz%WB_riEH6+Xzyh${t1%kVR1la|?bbg`ukKkS_^_md)|LPSMvoNRCWEqKF(zq52_ zta&n|h}-C0p5VF48KU)dmGeUiQXKC+X&>?K6dO|ypWBJ7J&X)aQiRkN2J7ueDv_&- zZ~Z^27>o7Ah&G6orzDi2(xbDqRX4r`rMj5;| zp7GqfJs~EjzWSwg!O~6pu~My_!qJRptLH0(B*AfpLAB4P{}vd}$e_al3Tj^#)XqF= z7(F;sw^SV<=Ajmd7T@iz_S8`Tb7cB-L7kaYA+?M5CKB6egE_4*_Os9K{;7=bc}T*^ zU_R0IV$*tEl1tTG_a9&1&RFs7ky98Q8croxA(?%_!<#o7$1j`};dWVXu#)^eHHNlw1hBt41xo4)qW zN(NyR3;Vhglo*j^U+`=2gqKt_O%HzaX2hZpiA$uwzz#t&?jen0K+c!^~hx|JxPx zy%_m0v>5^nJmE!`>~7%mgw|WQPEnO#(|NiMlQtg7*05xJe$Yf?6fNF)AkO&JnBw%> zi_6sd@y(%A_kXgx|NJd23+4mQ*RJhYd#v0H^%cG3&q$a-?jo$#sSsx;=psnr@Z?>S zy-+seDrD~<0#vD^FbqPf%c|eNT^x1KbCk}7s zA&#t!eKOoX!%}O~F?V{~3Btqermgugp|T6x3wJW^a)eQE%+$Cn>sv1;9M!A`5m8UQ zz-QnfNECQcWZJ+tHZZOyX~?LTD5b8Dj&2{cMp+>ZzZzkEeQM70N3)EJOUF!gB|Jg> zw!KeOf98-z?(nJ1eLSzzA5X1SQA8Fd$K&qbD{uK6Fz&3qw&~J5;!1jDjGKMKp31@o z^4o>40TU;QvNE|JC}4&rRD4h0Cd|W?CZcZqUAi{Mn=nr7KE6w$CSh*Yt>>HimgTZ? 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diff --git a/docs/source/nuclear.rst b/docs/source/nuclear.rst new file mode 100644 index 000000000..b19bd5977 --- /dev/null +++ b/docs/source/nuclear.rst @@ -0,0 +1,534 @@ +.. nuclear:: + +======= +Nuclear +======= + +This module is a re-implementation of the IAEA NMQC toolkit that was written for ImageJ [1]_, [2]_. +The toolkit itself appears to be based on the IAEA No. 6 QA for SPECT systems publication [3]_. + +The module is designed as a near drop-in replacement for the ImageJ plugin. The algorithms +have been duplicated as closely as possible. + +.. note:: + + This is not designed to compete or replace the ImageJ plugin. It was designed for + RadMachine customers, where Python is available. This should be considered an + alternative for those who prefer Python over ImageJ. + +In the following examples, the sample images given by the NMQC toolkit are used [4]_. + +Max Count Rate +-------------- + +This test is based on the NMQC toolkit and IAEA test 2.3.11.4. +The max count rate will examine each frame of a SPECT image and determine the maximum +count rate, which is the sum of the pixel values. The maximum count rate and max frame +can then be reported. A plot of the count rate vs frame number is also generated. + +.. code-block:: python + + from pylinac.nuclear import MaxCountRate + + path = r"C:\path\to\image.dcm" + mcr = MaxCountRate(path) + mcr.analyze(frame_resolution=0.5) # or whatever resolution it was acquired at + results = mcr.results_data() + print(results) + mcr.max_countrate # the maximum count rate + mcr.max_frame # the frame number of the maximum count rate + mcr.max_time # the time of the maximum count rate + # plot the count rate vs frame number + mcr.plot() + +The plot will look like similar to this: + +.. image:: images/max_count_rate_plot.png + + +Planar Uniformity +----------------- + +The planar uniformity test is based on test 2.3.3 of the IAEA publication. It will +examine each frame of the image and analyze the usable field of view (UFOV) and +center field of view (CFOV). Each frame can be plotted to show the UFOV and CFOV, +the max points of each, and the sliding window that resulted in the +maximum differential uniformity. + +Integral Uniformity +^^^^^^^^^^^^^^^^^^^ + +The integral uniformity uses the same equation as the IAEA, which is the +same as the :ref:`michelson` equation but multiplied by 100. That is: + +.. math:: \frac{I_{max} - I_{min}}{I_{max} + I_{min}} \times 100 + +Differential Uniformity +^^^^^^^^^^^^^^^^^^^^^^^ + +The differential uniformity is the same as the IAEA, which is the same as the +integral uniformity, but applied to a 5x1 subslice of pixels. The equation is the same as +above. + +Example Usage +^^^^^^^^^^^^^ + +.. code-block:: python + + from pylinac.nuclear import PlanarUniformity + + path = r"C:\path\to\image.dcm" + pu = PlanarUniformity(path) + pu.analyze(ufov_ratio=0.95, cfov_ratio=0.75, window_size=5, threshold=0.75) + results = pu.results_data() + print(results) + pu.plot() # plot the UFOV and CFOV of each frame + +This will result in an image like so: + +.. image:: images/planar_uniformity_plot.png + +Note the UFOV and CFOV, the min and max pixel values of each FOV, and a rectangle +showing the sliding window that resulted in the maximum differential uniformity. + +Algorithm +^^^^^^^^^ + +The algorithm is largely the same as the NMQC toolkit but there are a few key differences +which will be noted in the steps below. + +Each step listed here is applied to each frame of the image: + +#. The image is binned to have a pixel size > 4.48mm. I.e. if the pixel size is 1mm, + the image will be binned together in a block of 4x4 pixels. The pixels are summed + when binned together. +#. The image is smoothed using a smoothing kernel. The kernel is described on page 59 + of the IAEA QC document: + + .. math:: + + \begin{equation} + \begin{bmatrix} + 1 & 2 & 1 \\ + 2 & 4 & 2 \\ + 1 & 2 & 1 + \end{bmatrix} + \end{equation} + + The kernel is normalized by dividing each value by the sum of all values in the kernel. + +#. The edge pixels of the image are set to 0. +#. The image is thresholded. The threshold is the threshold ratio (input parameter; default of 0.75) + multiplied by the mean of all non-zero pixels. + + .. note:: + + This appears to be up for interpretation. The text says "set + any pixels at the edge of the UFOV that contain less than 75% of the + mean counts per pixel to zero". A value of 75% of the mean could mean the + entire image; the UFOV is not yet determined at this point. + If so, the mean depends on the total FOV. We interpret this to + mean a value of 75% of the mean of all pixels **that are non-zero**. + + In practice, this won't make a big difference this this only affects edge pixels. + +#. Stray pixels are removed from the image. A stray pixel is defined as a pixel that is surrounded + by a pixel values of 0. The stray pixels are set to 0. +#. The UFOV is determined. The size of the total FOV is found in each dimension (x, y). + The UFOV ratio is then multiplied by the total FOV size. This value is then used to + "erode" the edge pixel values of the image into a smaller sub-image. + E.g. if the total FOV is 22.5cm x 15cm, the UFOV will erode (1-0.95) * 22.5cm = 1.125cm (half of that from each edge). + + .. note:: + + This appears to be an NMQC-specific implementation. We cannot find anything + in the IAEA document that describes this step. The IAEA document only describes + the UFOV as the "usable field of view". The NMQC toolkit appears to differentiate between + the total FOV and the UFOV. + + +#. The CFOV is determined. The size of the CFOV will be a ratio of the UFOV. + E.g. if the UFOV is 20x20cm, the CFOV will be 0.75 * 20cm = 15cm. + This difference (5cm in this case) will be "eroded" from the UFOV. +#. The integral uniformity is calculated for the UFOV and CFOV. +#. The differential uniformity is calculated for the UFOV and CFOV. The differential uniformity + is calculated using a sliding window. The window size is determined by the input parameter + ``window_size``. The window size is the number of pixels in the dimension of interest. E.g. if the window + size is 5, the window will be 1x5 pixels along the x and y dimensions each. The window is slid across each dimension in 1 pixel increments. + The differential uniformity is calculated for each window position. The window position that + results in the maximum differential uniformity is recorded. + +.. note:: + + The NMQC toolkit creates a perfect rectangle for rectangular images. Pylinac will + "erode" the image equally in all directions. This will result in images where the corners + take on the same shape as the total FOV. Note the clipped corners of the image above for the CFOV. + In the case of pylinac, the image shape does not matter and this makes the algorithm easier. + +Center of Rotation +------------------ + +The center of rotation test measures the deviation of the SPECT panel from the center of rotation. +I.e. it measures the deviation from an expected orbit. The test is based on the IAEA test 4.3.6, pg 174. + +.. code-block:: python + + from pylinac.nuclear import CenterOfRotation + + path = r"C:\path\to\image.dcm" + cor = CenterOfRotation(path) + cor.analyze() + results = cor.results_data() + print(results) + cor.plot() # plot the fitted sine x-values, residuals for x and y axis + +This will produce a set of plots like so: + +.. image:: images/cor_sine.png + :align: center + +.. image:: images/cor_x_residuals.png + :align: center + +.. image:: images/cor_y_residuals.png + :align: center + +Tomographic Resolution +---------------------- + +The tomographic resolution test measures the resolution of the SPECT system. The test is based on the IAEA test 4.3.4, pg 169. + +.. code-block:: python + + from pylinac.nuclear import TomographicResolution + + path = r"C:\path\to\image.dcm" + tr = TomographicResolution(path) + tr.analyze() + results = tr.results_data() + print(results) + tr.plot() # plot the 3 axis profiles and the fitted gaussians + +This will produce 3 figures, one for each axis that look like so: + +.. image:: images/tomo_res_x.png + :align: center + +Algorithm +^^^^^^^^^ + +* The weighted centroid of the entire 3D image stack of frames is calculated. +* A 1D profile is created for each axis (x, y, z) at the location of the centroid. +* A gaussian is fitted to each profile. +* The FWHM and FWTM of each gaussian is calculated. + +.. note:: + + The FWHM/FWTM is not empirical like it is in the rest of pylinac. It is + calculated directly from the gaussian fit per the IAEA publication. + +Simple Sensitivity +------------------ + +The simple sensitivity test measures the sensitivity of the SPECT system. The test is based on the IAEA test 2.3.8, pg 73. +The equations are from the IAEA NMQC toolkit with minor modifications. + +.. warning:: + + The IAEA NMQC toolkit appears to use the counts of the *first* frame of the background + image only. In the test dataset, the background has 2 frames. Pylinac will use the + mean background value of all frames. This will give very small differences + in the results if there are multiple frames in the background image. + +No background +^^^^^^^^^^^^^ + +.. code-block:: python + + from pylinac.nuclear import SimpleSensitivity, Nuclide + + phantom_path = r"C:\path\to\image.dcm" + ss = SimpleSensitivity(path) + ss.analyze(activity_mbq=10, nuclide=Nuclide.Tc99m) + results = ss.results_data() + print(results) + +With background +^^^^^^^^^^^^^^^ + +.. code-block:: python + + from pylinac.nuclear import SimpleSensitivity, Nuclide + + phantom_path = r"C:\path\to\image.dcm" + background_path = r"C:\path\to\background.dcm" + ss = SimpleSensitivity(path, background_path=background_path) + ss.analyze(activity_mbq=10, nuclide=Nuclide.Tc99m) + results = ss.results_data() + print(results) + +Four-Bar Spatial Resolution +--------------------------- + +The four-bar spatial resolution test measures the spatial resolution of the SPECT system. The test is based on the IAEA test 2.3.8, Method 2, pg 75 +and on the NMQC toolkit manual 4.5.3. + +.. code-block:: python + + from pylinac.nuclear import FourBarResolution + + path = r"C:\path\to\image.dcm" + fbr = FourBarResolution(path) + fbr.analyze(separation_mm=100, roi_width_mm=10) + results = fbr.results_data() + print(results) + fbr.plot() # plot the image and 2 axis profiles with fitted gaussians + +This will produce a set of plots like so: + +.. image:: images/four_bar_img.png + :align: center + +.. image:: images/four_bar_x.png + :align: center + +.. image:: images/four_bar_y.png + :align: center + + + +Algorithm +^^^^^^^^^ + +.. warning:: + + The 4 lines are assumed to be centered about the image center. + +.. note:: + + The image is assumed to only have 1 frame. If there are multiple frames, only the first frame will be used. + + +* 2 Rectangular ROIs are sampled. The ROIs are centered at the image center. The longest + dimention of the ROI is twice the passed ``separation_mm``. The shortest dimension is the + ``roi_width_mm`` parameter. +* The mean of the profile along the axis of interest is calculated. E.g. for a 100px x 5px + this will take a mean across the 5px to give a 100px x 1px profile. +* For each axis profile, an initial simple peak search is done to create initial guesses for the gaussian fits. +* Each profile is fitted to two gaussian peaks using the initial guesses from the peak search. +* The FWHM and FWTM of each gaussian is calculated. +* Pixel size (mm/px) is determined by: + + .. math:: + + \frac{Stated Separation}{|G2_{peak} - G1_{peak}|} + + where :math:`Stated Separation` is the ``separation_mm`` parameter, :math:`G1_{peak}` is the peak position of the first gaussian fit, + and :math:`G2_{peak}` is the peak position of the second gaussian fit, both in pixels. + + +Four-Quadrant Spatial Resolution +-------------------------------- + +The four-quadrant spatial resolution test measures the spatial resolution of the SPECT system. The test is based on the IAEA test 2.3.8, Method 2, pg 75 +and the NMQC toolkit section 4.5.5. + +.. code-block:: python + + from pylinac.nuclear import QuadrantResolution + + path = r"C:\path\to\image.dcm" + qr = QuadrantResolution(path) + qr.analyze( + bar_widths=(4.23, 3.18, 2.54, 2.12), roi_diameter_mm=70, distance_from_center_mm=130 + ) + results = qr.results_data() + print(results) + qr.plot() # plot the image with quadrant ROIs, the MTF, and the FWHMs. + +This will produce a set of plots like so: + +.. image:: images/quadrant_image.png + :align: center + +.. image:: images/quadrant_mtf.png + :align: center + +.. image:: images/quadrant_fwhm.png + :align: center + +Algorithm +^^^^^^^^^ + +.. warning:: + + The line pair quadrants are assumed to be equidistant from the image center. + +* 4 circular ROIs are sampled. The ROIs are offset from the image center by ``distance_from_center_mm``. The diameter + of the ROI is the ``roi_diameter_mm`` parameter. The ROIs start at -45 and go CCW to match the NMQC toolkit. +* The mean and standard deviation of each ROI is calculated. +* For each ROI, the MTF and FWHM are calculated from Equation 8 and A8 of Hander et al [5]_: + + .. math:: + :label: mtf + + MTF = \frac{\sqrt{2 * (\sigma^{2} - \mu)}}{\mu} + + + .. math:: + :label: fwhm + + FWHM = 1.058 \times width \times \sqrt{\ln\left(\frac{\mu}{\sqrt{2 \times (\sigma^{2} - \mu)}}\right)} + + where :math:`\mu` is the mean pixel value of the ROI and :math:`\sigma` is the standard deviation of the ROI pixel values. + + +Tomographic Uniformity +---------------------- + +The tomographic uniformity test measures the uniformity of the SPECT system. The test is based on the IAEA test 4.3.3, pg 165. +It is very similar to the Planar Uniformity test. + +.. code-block:: python + + from pylinac.nuclear import TomographicUniformity + + path = r"C:\path\to\image.dcm" + tu = TomographicUniformity(path) + tu.analyze( + ufov_ratio=0.95, + cfov_ratio=0.75, + center_ratio=0.4, + window_size=5, + threshold=0.75, + window_size=5, + ) + results = tu.results_data() + print(results) + tu.plot() # plot the uniformity image, the UFOV, CFOV, and Center ROI, and the sliding windows that resulted in the maximum differential uniformity. + +This will result in a plot like so: + +.. image:: images/tomo_uniformity_plot.png + :align: center + +Algorithm +^^^^^^^^^ + +.. note:: + + Compared to the NMQC toolkit, the UFOV, CFOV, and Center ROI are based on ratios of the phantom size and + are not absolute. E.g. in the NMQC manual the UFOV is the phantom radius - 2cm and the center ROI is + a 6cm circle. These absolute values are not used in pylinac. Instead, ratios are used as + are done in the Planar Uniformity test. To get the same results as the NMQC toolkit, + basic math from the known phantom size can be used. Further, the default ratios are + close to the NMQC toolkit values for the Jaszczak phantom. + +* The 3D array is sliced to the frames passed by the user. +* The mean of the frames of interest are taken along the z-axis (in/out of viewing plane) to create + a single 2D image. +* From here, the algorithm is the same as the Planar Uniformity test. +* In addition to the UFOV and CFOV, a "center" FOV is also sampled based on the ``center_ratio``. +* The integral and differential uniformity are calculated for the UFOV, CFOV, and Center ROI. +* The center-to-edge ratio is calculated by dividing the mean of the center ROI by the + mean value of the pixels between the UFOV and CFOV. + +Tomographic Contrast +-------------------- + +The tomographic contrast test measures the contrast of the SPECT system. The test is based on the IAEA test 4.3.9, pg 182. + +.. code-block:: python + + from pylinac.nuclear import TomographicContrast + + path = r"C:\path\to\image.dcm" + tc = TomographicContrast(path) + tc.analyze( + sphere_diameters_mm=(38, 31.8, ...), + sphere_angles=(-10, -70, ...), + ufov_ratio=0.95, + search_window_px=5, + search_slices=3, + ) + results = tc.results_data() + print(results) + tc.plot() # plot the image with the ROIs and the contrast values. + +This will result in a set of plots like so: + +.. image:: images/tomo_contrast_plot.png + :align: center + +.. image:: images/tomo_contrast_uniformity.png + :align: center + +.. image:: images/tomo_contrast_graph.png + :align: center + +Algorithm +^^^^^^^^^ + +.. note:: + + Pylinac will automatically identify the uniformity slice and the "sphere" slice. There is no manual option. + +Uniformity Frame +################ + +* The uniformity frame is found by iterating over each frame: + + * The frame is thresholded to 10% of the global max pixel value. + * The largest remaining ROI is selected. + * The ROI is eroded by the ``ufov_ratio`` parameter (this is similar to what is done for the Planar Uniformity test). + * The uniformity is evaluated for this ROI as well as the ROI diameter, center point, and mean pixel value. + +* Any frame with an ROI size less than the median - std of all ROI sizes is rejected; this mostly means + edge frames are rejected. +* The frame with the lowest uniformity is selected as the uniformity frame. + +Sphere Frame +############ + +The **initial** sphere frame is selected by finding the frame with the highest uniformity (i.e. non-uniformity). +This will usually find the slice at or near the spheres as the cold sphere will create non-uniformity. + +From this initial sphere frame, each ROI is found by: + +* Starting at the position indicated by the ``sphere_angles`` and ``sphere_diameters_mm`` parameters, + a minimization is performed to find the best x,y,z coordinates that maximizes the contrast. The + search is bounded from the initial position to the ``search_window_px`` and ``search_slices`` parameters. + + .. note:: + + The search is in 3D space. However, for ease of plotting, all the sphere ROIs will be shown + on one plot when running ``.plot()``. The slice shown will be the most common slice the + final sphere locations are determined upon. + + + + +References +---------- + +.. [1] `NMQC Toolkit `__ +.. [2] `NMQC User Manual `__ +.. [3] `IAEA No. 6 QA for SPECT systems `__ +.. [4] `NMQC Sample Images `__ +.. [5] `Hander et al. `__ "Rapid objective measurement of gamma camera resolution using statistical moments" (1998). + + +API +--- + +.. autoclass:: pylinac.nuclear.MaxCountRate + :members: + +.. autoclass:: pylinac.nuclear.MaxCountRateResults + :members: + +.. autoclass:: pylinac.nuclear.PlanarUniformity + :members: + +.. autoclass:: pylinac.nuclear.PlanarUniformityResults + :members: diff --git a/docs/source/topics/mtf.rst b/docs/source/topics/mtf.rst index 06696a0c0..92c3e6cc5 100644 --- a/docs/source/topics/mtf.rst +++ b/docs/source/topics/mtf.rst @@ -4,6 +4,9 @@ Modulation Transfer Function ---------------------------- +Peak-Valley MTF +~~~~~~~~~~~~~~~ + The modulation transfer function (MTF) is used in CBCT and planar imaging metrics to describe high-contrast characteristics of the imaging system. An excellent introduction is `here `__. In pylinac, MTF is calculated using equation 3 of the above reference, which is also the :ref:`Michelson ` contrast definition. @@ -19,3 +22,19 @@ However, it's important to know what :math:`I_{max}` and :math:`I_{min}` means h is one contrast value. Thus, one contrast value is calculated for each bar/space combo. For phantoms with areas of the same spacing (e.g. the Leeds), all bars and spaces are the same and thus we can use an area-based ROI for the input to the contrast equation. + +Moments-based MTF +~~~~~~~~~~~~~~~~~ + +The MTF can also be calculated using the moments of the line pair spread function (LPSF). +This algorithm is based on the work of Hander et al [1]_. Specifically, equation 8: + +.. math:: + :label: mtf + + MTF = \frac{\sqrt{2 * (\sigma^{2} - \mu)}}{\mu} + +where :math:`\mu` is the mean pixel value of the ROI and :math:`\sigma` is the standard deviation of the ROI pixel values. + + +.. [1] `Hander et al. `__ "Rapid objective measurement of gamma camera resolution using statistical moments" (1998). diff --git a/pylinac/core/contrast.py b/pylinac/core/contrast.py index 0b9c31f0a..0fce5396b 100644 --- a/pylinac/core/contrast.py +++ b/pylinac/core/contrast.py @@ -112,7 +112,7 @@ def michelson(array: np.ndarray) -> float: https://en.wikipedia.org/wiki/Contrast_(vision)#Michelson_contrast """ - l_max, l_min = array.max(), array.min() + l_max, l_min = np.nanmax(array), np.nanmin(array) return (l_max - l_min) / (l_max + l_min) diff --git a/pylinac/core/geometry.py b/pylinac/core/geometry.py index d9a4b0cb5..a28296f9d 100644 --- a/pylinac/core/geometry.py +++ b/pylinac/core/geometry.py @@ -35,6 +35,33 @@ def sin(degrees: float) -> float: return math.sin(math.radians(degrees)) +def direction_to_coords( + start_x: float, start_y: float, distance: float, angle_degrees: float +) -> (float, float): + """Calculate destination coordinates given a start position, distance, and angle. + + The 0-angle position is pointing to the right (i.e. unit circle) + + Parameters + ---------- + start_x : float + Starting x position + start_y : float + Starting y position + distance : float + Distance to travel + angle_degrees : float + Angle to travel in degrees. + """ + # Convert angle from degrees to radians + angle_radians = math.radians(angle_degrees) + + # Calculate destination coordinates + end_x = start_x + distance * math.cos(angle_radians) + end_y = start_y + distance * math.sin(angle_radians) + return end_x, end_y + + class Point: """A geometric point with x, y, and z coordinates/attributes.""" diff --git a/pylinac/core/image.py b/pylinac/core/image.py index d845d049e..ca884b09a 100644 --- a/pylinac/core/image.py +++ b/pylinac/core/image.py @@ -1561,7 +1561,7 @@ class LazyDicomImageStack: def __init__( self, - folder: str | Path, + folder: str | Path | Sequence[str | Path], dtype: np.dtype | None = None, min_number: int = 39, check_uid: bool = True, @@ -1758,6 +1758,42 @@ def __len__(self): return len(self.images) +class NMImageStack: + """A class of frames of a nuclear medicine image. + A single image can have N frames. For our purposes, we + can treat this as a stack of images.""" + + metadata: Dataset + path: str | Path + frames: list[DicomImage] + + def __init__(self, path: str | Path): + """Load a single NM image with N frames.""" + self.path = path + self.frames = [] + ds = pydicom.dcmread(path, force=True, stop_before_pixels=True) + if ds.Modality != "NM": + raise TypeError("The file is not a NM image") + self.metadata = ds + full_ds = pydicom.dcmread(path, force=True) + for i in range(full_ds.NumberOfFrames): + full_array = full_ds.pixel_array + if full_array.ndim == 2: + array = full_array + else: + array = full_array[i] + img = DicomImage(self.path) + img.array = array + self.frames.append(img) + + def as_3d_array(self) -> np.ndarray: + """Return the frames as a 3D array.""" + return np.stack([i.array for i in self.frames], axis=0) + + def __len__(self): + return len(self.frames) + + def tiff_to_dicom( tiff_file: str | Path | BytesIO, sid: float, diff --git a/pylinac/core/mtf.py b/pylinac/core/mtf.py index ded3993fd..40c7427ae 100644 --- a/pylinac/core/mtf.py +++ b/pylinac/core/mtf.py @@ -1,5 +1,6 @@ from __future__ import annotations +import math import warnings from typing import Sequence @@ -34,6 +35,14 @@ def __init__( self.spacings = lp_spacings self.maximums = lp_maximums self.minimums = lp_minimums + if len(lp_spacings) != len(lp_maximums) != len(lp_minimums): + raise ValueError( + "The number of MTF spacings, maximums, and minimums must be equal." + ) + if len(lp_spacings) < 2 or len(lp_maximums) < 2 or len(lp_minimums) < 2: + raise ValueError( + "The number of MTF spacings, maximums, and minimums must be greater than 1." + ) self.mtfs = {} self.norm_mtfs = {} for spacing, max, min in zip(lp_spacings, lp_maximums, lp_minimums): @@ -94,7 +103,7 @@ def plot( margins: float = 0.05, marker: str = "o", label: str = "rMTF", - ) -> tuple: + ) -> list[plt.Line2D]: """Plot the Relative MTF. Parameters @@ -115,4 +124,131 @@ def plot( axis.set_xlabel(x_label) axis.set_ylabel(y_label) axis.set_title(title) + spacing_ax = axis.secondary_xaxis( + "top", functions=(lambda x: 1 / x, lambda x: 1 / x) + ) + spacing_ax.set_xlabel("Pair Distance (mm)") + plt.tight_layout() return points + + +class PeakValleyMTF(MTF): + pass + + +def moments_mtf(mean: float, std: float) -> float: + """The moments-based MTF based on Hander et al 1997 Equation 8. + + See Also + -------- + https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1118/1.597928 + """ + return math.sqrt(2 * (std**2 - mean)) / mean + + +def moments_fwhm(width: float, mean: float, std: float) -> float: + """The moments-based FWHM based on Hander et al 1997 Equation A8. + + Parameters + ---------- + width : float + The bar width in mm + mean : float + The mean of the ROI. + std : float + The standard deviation of the ROI. + + See Also + -------- + https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1118/1.597928 + """ + return 1.058 * width * math.sqrt(np.log(mean / (math.sqrt(2 * (std**2 - mean))))) + + +class MomentMTF: + """A moments-based MTF. Based on the work of Hander et al 1997. + + Parameters + ---------- + lpmms : sequence of floats + The line pairs per mm. + means : sequence of floats + The means of the ROIs. + stds : sequence of floats + The standard deviations of the ROIs. + + See Also + -------- + https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1118/1.597928 + """ + + mtfs: dict[float, float] + fwhms: dict[float, float] + + def __init__( + self, lpmms: Sequence[float], means: Sequence[float], stds: Sequence[float] + ): + self.mtfs = {} + self.fwhms = {} + for lpmm, mean, std in zip(lpmms, means, stds): + bar_width = 1 / (2 * lpmm) # lp is 2 bars + self.mtfs[lpmm] = moments_mtf(mean, std) + self.fwhms[lpmm] = moments_fwhm(bar_width, mean, std) + + @classmethod + def from_high_contrast_diskset( + cls, lpmms: Sequence[float], diskset: Sequence[HighContrastDiskROI] + ) -> MomentMTF: + """Construct the MTF using high contrast disks from the ROI module.""" + means = [roi.mean for roi in diskset] + stds = [roi.std for roi in diskset] + return cls(lpmms, means, stds) + + def plot( + self, + axis: plt.Axes | None = None, + marker: str = "o", + ) -> plt.Axes: + """Plot the Relative MTF. + + Parameters + ---------- + axis : None, matplotlib.Axes + The axis to plot the MTF on. If None, will create a new figure. + """ + if axis is None: + fig, axis = plt.subplots() + axis.plot( + list(self.mtfs.keys()), + list(self.mtfs.values()), + marker=marker, + ) + axis.grid(True) + axis.set_xlabel("Line pairs / mm") + axis.set_ylabel("MTF") + axis.set_title("Moments-based MTF") + spacing_ax = axis.secondary_xaxis( + "top", functions=(lambda x: 1 / x, lambda x: 1 / x) + ) + spacing_ax.set_xlabel("Pair Distance (mm)") + plt.tight_layout() + return axis + + def plot_fwhms(self, axis: plt.Axes | None = None, marker: str = "o") -> plt.Axes: + if axis is None: + fig, axis = plt.subplots() + axis.plot( + list(self.fwhms.keys()), + list(self.fwhms.values()), + marker=marker, + ) + axis.grid(True) + axis.set_xlabel("Line pairs / mm") + axis.set_ylabel("FWHM") + axis.set_title("Moments-based FWHM") + spacing_ax = axis.secondary_xaxis( + "top", functions=(lambda x: 1 / x, lambda x: 1 / x) + ) + spacing_ax.set_xlabel("Pair Distance (mm)") + plt.tight_layout() + return axis diff --git a/pylinac/core/roi.py b/pylinac/core/roi.py index da4b5cc77..16d4c36f7 100644 --- a/pylinac/core/roi.py +++ b/pylinac/core/roi.py @@ -120,7 +120,7 @@ def plot2axes( if axes is None: fig, axes = plt.subplots() axes.imshow(self._array) - super().plot2axes(axes, edgecolor=edgecolor, text=text, fontsize=fontsize) + super().plot2axes(axes, edgecolor=edgecolor, text=str(text), fontsize=fontsize) def as_dict(self) -> dict: """Convert to dict. Useful for dataclasses/Result""" @@ -326,16 +326,22 @@ def __repr__(self): return f"High-Contrast Disk; max pixel: {self.max}, min pixel: {self.min}" @cached_property - def max(self) -> np.ndarray: + def max(self) -> float: """The max pixel value of the ROI.""" masked_img = self.circle_mask() - return np.max(masked_img) + return float(np.max(masked_img)) @cached_property - def min(self) -> np.ndarray: + def min(self) -> float: """The min pixel value of the ROI.""" masked_img = self.circle_mask() - return np.min(masked_img) + return float(np.min(masked_img)) + + @cached_property + def mean(self) -> float: + """The mean pixel value of the ROI.""" + masked_img = self.circle_mask() + return float(np.mean(masked_img)) class RectangleROI(Rectangle): diff --git a/pylinac/metrics/image.py b/pylinac/metrics/image.py index 62135f5cc..0c33cc5c2 100644 --- a/pylinac/metrics/image.py +++ b/pylinac/metrics/image.py @@ -711,3 +711,35 @@ def from_physical( "This method is not implemented for global field-finding. Use the " "standard initializer instead." ) + + +class WeightedCentroid(MetricBase): + def __init__(self, name: str = "Weighted Centroid"): + self.name = name + + def calculate(self) -> Point: + """Calculate the weighted centroid of the image.""" + arr = self.image.array + if np.sum(arr) == 0: + raise ValueError("Image is blank; cannot calculate weighted centroid") + + # Get the indices of all elements + y_indices, x_indices = np.indices(arr.shape) + + # Calculate the sum of weights (total weight) + total_weight = np.sum(arr) + + # Calculate the weighted sum of indices + x_weighted_sum = np.sum(x_indices * arr) + y_weighted_sum = np.sum(y_indices * arr) + + # Calculate the centroid + centroid_x = x_weighted_sum / total_weight + centroid_y = y_weighted_sum / total_weight + + return Point(centroid_x, centroid_y) + + def plot(self, axis: plt.Axes, **kwargs) -> None: + """Plot the weighted centroid of the image.""" + centroid = self.calculate() + plt.plot(centroid.x, centroid.y, "o", color="red", markersize=10) diff --git a/pylinac/nuclear.py b/pylinac/nuclear.py new file mode 100644 index 000000000..b749b444b --- /dev/null +++ b/pylinac/nuclear.py @@ -0,0 +1,1689 @@ +from __future__ import annotations + +import math +from dataclasses import asdict, dataclass +from functools import cached_property +from pathlib import Path +from typing import Sequence, TypedDict + +import numpy as np +from matplotlib import pyplot as plt +from matplotlib.axes import Axes +from matplotlib.figure import Figure +from matplotlib.patches import Rectangle +from numpy.lib.stride_tricks import sliding_window_view +from scipy.optimize import curve_fit, minimize +from scipy.signal import convolve2d +from skimage.measure import block_reduce, label, regionprops +from skimage.morphology import ( + isotropic_erosion, + remove_small_holes, + remove_small_objects, +) +from skimage.segmentation import find_boundaries + +from pylinac.core.contrast import michelson +from pylinac.core.geometry import Circle, Point, direction_to_coords +from pylinac.core.image import DicomImage, NMImageStack +from pylinac.core.mtf import MomentMTF +from pylinac.core.profile import find_peaks +from pylinac.core.roi import DiskROI, HighContrastDiskROI, RectangleROI +from pylinac.metrics.image import WeightedCentroid + + +@dataclass +class MaxCountRateResults: + max_countrate: float #: + max_frame: int #: + frame_duration: float #: + sums: dict[int, float] #: + + +class MaxCountRate: + """Calculate the maximum countrate of a gamma camera. + + Reimplementation of the NMQC toolkit's MaxCountRate test (4.2) + + Parameters + ---------- + path : str | Path + The path to the DICOM file. + + See Also + -------- + https://humanhealth.iaea.org/HHW/MedicalPhysics/NuclearMedicine/QualityAssurance/NMQC-Plugins/OperatorManual-2017-10-20.pdf + """ + + stack: NMImageStack + frame_duration: float + max_countrate: float + sums: dict[int, float] + + def __init__(self, path: str | Path) -> None: + self.stack = NMImageStack(path) + + def analyze(self, frame_duration: float = 1.0) -> float: + """Analyze the DICOM file and return the maximum countrate. + + Parameters + ---------- + frame_duration : float + The duration of each frame in seconds. + """ + self.frame_duration = frame_duration + self.sums = {} + for idx, img in enumerate(self.stack.frames): + self.sums[idx] = img.array.sum() / frame_duration + + @property + def max_countrate(self) -> float: + """The maximum countrate in counts/second.""" + return max(self.sums.values()) + + @property + def max_frame(self) -> int: + """The frame number that had the maximum countrate.""" + return max(self.sums, key=self.sums.get) + + @property + def max_time(self) -> float: + """The time of the maximum countrate.""" + return self.max_frame * self.frame_duration + + def plot(self, show: bool = True) -> None: + """Plot the countrate over time.""" + fig, ax = plt.subplots() + ax.plot( + np.asarray(list(self.sums.keys())) * self.frame_duration, self.sums.values() + ) + ax.grid(True) + ax.set_xlabel("Time (s)") + ax.set_ylabel("Count Rate (cps)") + # show the frames on a second x-axis + ax2 = ax.twiny() + ax2.set_xlabel("Frame") + ax2.set_xlim(np.asarray(ax.get_xlim()) / self.frame_duration) + plt.tight_layout() + # plot the max point + ax.plot(self.max_time, self.max_countrate, "ro") + if show: + plt.show() + + def results(self) -> str: + """Return a string representation of the results.""" + return ( + f"Max countrate: {self.max_countrate:.0f} counts/second\n" + f"Frame duration: {self.frame_duration:.2f} seconds\n" + f"Max frame: {self.max_frame} out of {len(self.stack.frames)}\n" + ) + + def results_data(self, as_dict: bool = False) -> dict | MaxCountRateResults: + """Return the results as a dict or dataclass.""" + d = MaxCountRateResults( + max_countrate=self.max_countrate, + frame_duration=self.frame_duration, + max_frame=self.max_frame, + sums=self.sums, + ) + if as_dict: + return asdict(d) + else: + return d + + +@dataclass +class PlanarUniformityResults: + ufov_integral_uniformity: float #: + ufov_differential_uniformity: float #: + cfov_integral_uniformity: float #: + cfov_differential_uniformity: float #: + + +@dataclass +class FOV: + """Represents a field of view (FOV) of a gamma camera.""" + + name: str + fov: np.ndarray + boundary_x: np.ndarray + boundary_y: np.ndarray + window_size: int + + @property + def integral_uniformity(self) -> float: + """The integral uniformity of the FOV.""" + # non-zero values only + return integral_uniformity(self.fov[self.fov > 0]) + + @cached_property + def _differential_uniformities( + self, + ) -> (dict[tuple[int, int], float], dict[tuple[int, int], float]): + """Helper to get the differential uniformity arrays of the FOV.""" + non_zero = np.where(self.fov > 0, self.fov, np.nan) + y_view = sliding_window_view(non_zero, window_shape=self.window_size, axis=0) + x_view = sliding_window_view(non_zero, window_shape=self.window_size, axis=1) + y_diffs = {} + for i in range(y_view.shape[0]): + for j in range(y_view.shape[1]): + # only add the differential uniformity if the integral uniformity is valid + unif = integral_uniformity(y_view[i, j]) + if not np.isnan(unif): + y_diffs[(i, j)] = unif + # same for x + x_diffs = {} + for i in range(x_view.shape[0]): + for j in range(x_view.shape[1]): + # only add the differential uniformity if the integral uniformity is valid + unif = integral_uniformity(x_view[i, j]) + if not np.isnan(unif): + x_diffs[(i, j)] = unif + return y_diffs, x_diffs + + @property + def differential_uniformity(self) -> float: + """The maximum differential uniformity of the FOV.""" + max_x = max(self._differential_uniformities[1].values()) + max_y = max(self._differential_uniformities[0].values()) + return max(max_x, max_y) + + @property + def max_point(self) -> tuple[int, int]: + """The point where the maximum value is located.""" + nan_array = np.where(self.fov == 0, np.nan, self.fov) + local_point = np.unravel_index(np.nanargmax(nan_array), self.fov.shape) + return int(local_point[0]), int(local_point[1]) + + @property + def min_point(self) -> tuple[int, int]: + """The point where the minimum value is located.""" + nan_array = np.where(self.fov == 0, np.nan, self.fov) + local_point = np.unravel_index(np.nanargmin(nan_array), self.fov.shape) + return int(local_point[0]), int(local_point[1]) + + def plot_to(self, axis: Axes, color: str) -> None: + """Plot the FOV and it's related metrics to an axis""" + axis.scatter( + self.boundary_x, + self.boundary_y, + color=color, + label=f"{self.name} Boundary", + marker=".", + ) + axis.scatter( + self.max_point[1], + self.max_point[0], + color=color, + marker="s", + label=f"{self.name} Max", + ) + axis.scatter( + self.min_point[1], + self.min_point[0], + color=color, + marker="x", + label=f"{self.name} Min", + ) + # plot the max differential uniformity 5x1 box + max_x = max(self._differential_uniformities[1].values()) + max_y = max(self._differential_uniformities[0].values()) + if max_x > max_y: + max_point = max( + self._differential_uniformities[1], + key=self._differential_uniformities[1].get, + ) + width = self.window_size + height = 1 + else: + max_point = max( + self._differential_uniformities[0], + key=self._differential_uniformities[0].get, + ) + width = 1 + height = self.window_size + # offset by half pixel so we're centered about the target pixel + rect = Rectangle( + (max_point[1] - 0.5, max_point[0] - 0.5), + width, + height, + linewidth=1, + edgecolor=color, + facecolor="none", + label=f"{self.name} Max Diff. Window", + ) + axis.add_patch(rect) + axis.legend() + + +class PlanarUniformity: + """Analyzes an image for its integral and differential uniformity.""" + + stack: NMImageStack + frame_results: dict[str, dict[str, FOV | np.ndarray]] + + def __init__(self, path: str | Path) -> None: + self.stack = NMImageStack(path) + self.path = Path(path) + + def analyze( + self, + ufov_ratio: float = 0.95, + cfov_ratio: float = 0.75, + window_size: int = 5, + threshold: float = 0.75, + ) -> None: + """Analyze the field for the UFOV and CFOV's integral and differential uniformity. + + Parameters + ---------- + ufov_ratio : float + The ratio of the useful field of view (UFOV) to the detected size of the field of view. + cfov_ratio : float + The ratio of the central field of view (CFOV) to the UFOV. E.g. if this is 0.75 and the UFOV ratio is 0.95, + then the CFOV will be 0.95*0.75=0.7125 of the total FOV. + window_size : int + The size of the window in pixels to use for the differential uniformity calculation. + threshold : float + The threshold to use for removing low-value pixels. This is a fraction of the mean value of the pixels + that are > 0. + """ + self.frame_results = {} + for idx, frame in enumerate(self.stack.frames): + # we need to block-reduce until the pixel size is within NEMA range + cleaned_frame, _ = self.preprocess(frame, threshold=threshold) + ufov_array, ufov_x, ufov_y = get_fov(cleaned_frame, ufov_ratio) + ufov = FOV( + name="UFOV", + fov=ufov_array, + boundary_x=ufov_x, + boundary_y=ufov_y, + window_size=window_size, + ) + cfov_array, cfov_x, cfov_y = get_fov(cleaned_frame, cfov_ratio * ufov_ratio) + cfov = FOV( + name="CFOV", + fov=cfov_array, + boundary_x=cfov_x, + boundary_y=cfov_y, + window_size=window_size, + ) + self.frame_results[str(idx + 1)] = { + "ufov": ufov, + "cfov": cfov, + "binned_frame": cleaned_frame, + } + + def results(self) -> str: + """Return a string representation of the results.""" + s = [] + for key, result in self.frame_results.items(): + s.append(f"Frame {key}:\n") + s.append( + f"UFOV integral uniformity: {result['ufov'].integral_uniformity:.2f}%\n" + ) + s.append( + f"UFOV differential uniformity {result['ufov'].differential_uniformity:.2f}%\n" + ) + s.append( + f"CFOV integral uniformity: {result['cfov'].integral_uniformity:.2f}%\n" + ) + s.append( + f"CFOV differential uniformity {result['cfov'].differential_uniformity:.2f}%\n" + ) + s.append("\n") + return "".join(s) + + def results_data(self, as_dict: bool = False) -> dict: + data = {} + for key, result in self.frame_results.items(): + r = PlanarUniformityResults( + ufov_integral_uniformity=result["ufov"].integral_uniformity, + ufov_differential_uniformity=result["ufov"].differential_uniformity, + cfov_integral_uniformity=result["cfov"].integral_uniformity, + cfov_differential_uniformity=result["cfov"].differential_uniformity, + ) + if as_dict: + # make the key 1-based for ease of understanding by the user and match + # dicom labeling + data[f"Frame {key}"] = asdict(r) + else: + data[f"Frame {key}"] = r + return data + + def plot(self, show: bool = True, cmap: str = "gray") -> (list[Figure], list[Axes]): + """Plot each frame with the UFOV and CFOV boundaries, max points, and max differential uniformity window.""" + figs = [] + axes = [] + for key, result in self.frame_results.items(): + fig, axis = plt.subplots() + nan_array = np.where( + result["binned_frame"] == 0, np.nan, result["binned_frame"] + ) + axis.imshow( + result["binned_frame"], + cmap=cmap, + vmin=np.nanmin(nan_array), + vmax=np.nanmax(nan_array), + ) + result["ufov"].plot_to(axis, color="y") + result["cfov"].plot_to(axis, color="r") + axis.legend(loc="upper right") + fig.suptitle(f"Frame {key}") + figs.append(fig) + axes.append(axis) + if show: + plt.show() + return figs, axes + + @staticmethod + def preprocess(frame: DicomImage, threshold: float) -> (np.ndarray, int): + array = np.copy(frame.array) + pixel_size = frame.metadata.PixelSpacing[0] + bin_size = determine_binning(pixel_size) + array = block_reduce(array, block_size=(bin_size, bin_size), func=np.sum) + # filter image + # kernel based on NEMA/IAEA + # see https://www-pub.iaea.org/MTCD/Publications/PDF/Pub1394_web.pdf + # pg 59/71 + kernel = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]], dtype=float) + kernel /= kernel.sum() + array = convolve2d(array, kernel, mode="same") + # clean the edges + array[0, :] = 0 + array[-1, :] = 0 + array[:, 0] = 0 + array[:, -1] = 0 + # remove pixels that are <75% of mean of "meaningful" pixels + # meaningful pixels are those > 10% of max + # this helps remove the background + threshold = array[array > np.max(array) * 0.10].mean() * threshold + array[array < threshold] = 0 + # remove stray pixels + binary_frame = array > 0 + remove_small_objects(binary_frame, min_size=2, out=binary_frame) + remove_small_holes(binary_frame, area_threshold=2, out=binary_frame) + array[binary_frame == 0] = 0 + return array, bin_size + + +def get_fov(array: np.ndarray, size: float) -> (np.ndarray, np.ndarray, np.ndarray): + """Get the boundary of the FOV, either CFOV or UFOV. + + This will also return the boundaries of the FOV for plotting purposes. + + Parameters + ---------- + array : np.ndarray + The array to find the FOV of. + size : float + The size of the FOV as a ratio of the largest dimension (0-1). + """ + binary_frame = array > 0 + labeled_frame, num_labels = label(binary_frame, connectivity=1, return_num=True) + rois = regionprops(labeled_frame, intensity_image=array) + largest_roi = max(rois, key=lambda x: x.area) + # find the largest dimension, x or y + longest_dim = max(largest_roi.image.shape) + erosion = int(round((1 - size) * longest_dim)) + eroded_binary = isotropic_erosion(binary_frame, radius=erosion / 2) + boundary = find_boundaries(eroded_binary, connectivity=1, mode="inner") + boundary_y, boundary_x = np.nonzero(boundary) + fov_array = np.where(eroded_binary, array, 0) + # get the offset of the fov array from the original array + return fov_array, boundary_x, boundary_y + + +def integral_uniformity(array: np.ndarray) -> float: + """Calculate the integral uniformity of a FOV as defined by IAEA pg 7: + https://humanhealth.iaea.org/HHW/MedicalPhysics/NuclearMedicine/QualityAssurance/NMQC-Plugins/OperatorManual-2017-10-20.pdf + """ + return michelson(array) * 100 + + +def determine_binning(pixel_size: float) -> int: + """Determine the binning factor for a given pixel size. + + We want to bin the image until the pixel size is within the NEMA range of 4.48-8.32mm. + """ + binning = 1 + while pixel_size < 4.48: + pixel_size *= 2 + binning *= 2 + return binning + + +@dataclass +class CenterOfRotationResults: + x_deviation_mm: float #: + y_deviation_mm: float #: + + +class CenterOfRotation: + """Analyze the center of rotation deviation of a gamma camera.""" + + centroids: dict[float, Point] + cor_x: dict[str, float | np.ndarray] + cor_y: dict[str, float | np.ndarray] + + def __init__(self, path: str | Path): + self.path = Path(path) + self.stack = NMImageStack(path) + + def analyze(self) -> None: + """Analyze the DICOM file to determine the deviation from the center of rotation.""" + # find the weighted centroids of all the images + rot_dir = self.stack.metadata.RotationInformationSequence[0].RotationDirection + rot_sign = -1 if rot_dir == "CW" else 1 + start_angle = self.stack.metadata.RotationInformationSequence[0].StartAngle + step_size = self.stack.metadata.RotationInformationSequence[0].AngularStep + centroids = {} + for idx, frame in enumerate(self.stack.frames): + centroid = frame.compute(WeightedCentroid()) + angle = start_angle + rot_sign * idx * step_size + centroids[angle] = centroid + + x_values = np.radians(list(centroids.keys())) + # the data is offset by half a pixel due to physical + # pixel size. I.e. index 0 is really half a pixel from the edge + half_pixel = self.stack.metadata.PixelSpacing[0] * 0.5 + # x-fit + y_values = ( + np.asarray([p.x for p in centroids.values()]) + * self.stack.metadata.PixelSpacing[0] + + half_pixel + ) + params, _ = curve_fit( + sinusoidal_fit, x_values, y_values, p0=[np.mean(y_values), 1, 1, 1] + ) + fitted_y_data = sinusoidal_fit( + x_values, params[0], params[1], params[2], params[3] + ) + residuals = y_values - fitted_y_data + self.cor_x = { + "x_values": x_values, + "y_values": y_values, + "a": params[0], + "b": params[1], + "c": params[2], + "phi": params[3], + "fitted_y_values": fitted_y_data, + "residuals": residuals, + } + # y-data + y_values = ( + np.asarray([p.y for p in centroids.values()]) + * self.stack.metadata.PixelSpacing[0] + + half_pixel + ) + residuals = y_values - np.mean(y_values) + self.cor_y = { + "x_values": x_values, + "residuals": residuals, + } + + @property + def x_cor_deviation_mm(self) -> float: + """The deviation of the center of rotation from the center of the matrix in mm.""" + return np.max(np.abs(self.cor_x["residuals"])) + + @property + def y_cor_deviation_mm(self) -> float: + """The deviation of the center of rotation from the center of the matrix in mm.""" + return np.max(np.abs(self.cor_y["residuals"])) + + def results(self) -> str: + """Return a string representation of the results.""" + return ( + f"Center of Rotation results for {self.path.name}\n" + f"X-axis center of rotation deviation (mm): {self.x_cor_deviation_mm:.3f}\n" + f"Y-axis center of rotation deviation (mm): {self.y_cor_deviation_mm:.3f}\n" + ) + + def results_data( + self, as_dict: bool = False + ) -> dict[str, float] | CenterOfRotationResults: + """Return the results as a structure.""" + r = CenterOfRotationResults( + x_deviation_mm=self.x_cor_deviation_mm, + y_deviation_mm=self.y_cor_deviation_mm, + ) + if as_dict: + return asdict(r) + else: + return r + + def plot(self, show: bool = True) -> (list[Figure], list[Axes]): + figs = [] + axes = [] + # plot the x-axis sine fit + fig, ax = plt.subplots() + ax.plot(self.cor_x["x_values"], self.cor_x["y_values"], "bo") + ax.plot( + self.cor_x["x_values"], + self.cor_x["fitted_y_values"], + "r-", + label=f'{self.cor_x["a"]:2.2f}{self.cor_x["b"]:+2.3f}*sin({self.cor_x["c"]:2.2f}*\N{GREEK SMALL LETTER THETA}{self.cor_x["phi"]:+2.2f})', + ) + ax.legend() + ax.set_xlabel("Angle (radians)") + ax.set_ylabel("Position (mm)") + ax.grid(True) + fig.suptitle("Sine fit (X-axis)") + figs.append(fig) + axes.append(ax) + + # plot the x and y-axis residuals + for cor, axis in zip([self.cor_x, self.cor_y], ["X-axis", "Y-axis"]): + fig, ax = plt.subplots() + ax.plot(cor["x_values"], cor["residuals"], "bo") + ax.set_xlabel("Angle (radians)") + ax.set_ylabel("Residual Error (mm)") + ax.grid(True) + fig.suptitle(f"Residual error ({axis})") + figs.append(fig) + axes.append(ax) + + if show: + plt.show() + return figs, axes + + +def sinusoidal_fit(theta: float, a: float, b: float, c: float, phi: float) -> float: + """Return a fitted sinusoidal function. See IAEA pg176, Method B (2).""" + return a + b * np.sin(c * theta + phi) + + +def weighted_centroid_3d(arr: np.ndarray) -> tuple[float, float, float] | None: + if np.sum(arr) == 0: + return None # Avoid division by zero + + # Get the indices of all elements in 3D + z_indices, y_indices, x_indices = np.indices(arr.shape) + + # Calculate the sum of weights (total weight) + total_weight = np.sum(arr) + + # Calculate the weighted sum of indices for each dimension + x_weighted_sum = np.sum(x_indices * arr) + y_weighted_sum = np.sum(y_indices * arr) + z_weighted_sum = np.sum(z_indices * arr) + + # Calculate the centroid for each dimension + centroid_x = x_weighted_sum / total_weight + centroid_y = y_weighted_sum / total_weight + centroid_z = z_weighted_sum / total_weight + + return centroid_x, centroid_y, centroid_z + + +@dataclass +class TomographicResolutionResults: + x_fwhm: float #: + y_fwhm: float #: + z_fwhm: float #: + x_fwtm: float #: + y_fwtm: float #: + z_fwtm: float #: + + +@dataclass +class TomographicResolutionAxisData: + axis: str #: + profile_array: np.ndarray #: + pixel_size: float #: + + def __post_init__(self): + xs = np.arange(len(self.profile_array)) * self.pixel_size + self.popt, _ = curve_fit( + gaussian_fit, + xs, + self.profile_array, + p0=[np.max(self.profile_array), np.mean(xs), self.pixel_size], + ) + + @property + def fwhm(self) -> float: + """The full width at half maximum of the x-profile.""" + return fwhm_from_gaussian(self.popt[2]) + + @property + def fwtm(self) -> float: + """The full width at tenth maximum of the x-profile.""" + return fwtm_from_gaussian(self.popt[2]) + + def plot(self) -> (Figure, Axes): + fig, ax = plt.subplots() + xs = np.arange(len(self.profile_array)) * self.pixel_size + x_interpolated = ( + np.linspace(0, len(self.profile_array), num=len(self.profile_array) * 20) + * self.pixel_size + ) + fitted_y = gaussian_fit(x_interpolated, *self.popt) + # plot raw + ax.plot(xs, self.profile_array, "bo", label="Raw Data") + # close limits to around the peak + ax.set_xlim( + (self.popt[1] - 10 * self.popt[2]), (self.popt[1] + 10 * self.popt[2]) + ) + # plot fitted gaussian + ax.plot(x_interpolated, fitted_y, "r-", label="Gaussian Fit") + ax.grid(True) + ax.set_xlabel("Distance (mm)") + ax.set_ylabel("Counts") + fig.suptitle(f"{self.axis}-axis profile") + return fig, ax + + +class TomographicResolution: + """Analyze a tomographic resolution image for its x/y/z resolution. Based on IAEA test 4.3.4, pg 169 + + Parameters + ---------- + path : str | Path + The path to the DICOM file. + """ + + x_axis: TomographicResolutionAxisData + y_axis: TomographicResolutionAxisData + z_axis: TomographicResolutionAxisData + + def __init__(self, path: str | Path) -> None: + self.stack = NMImageStack(path) + self.path = Path(path) + + def analyze(self) -> None: + """Analyze the frames by finding the 3D weighted centroid and then taking a profile of the x/y/z axes + and that position""" + # find the weighted centroid of the stack in 3D + array_3d = self.stack.as_3d_array() + x, y, z = weighted_centroid_3d(array_3d) + # the slice at the z-location will give the x/y resolution + xy_frame = self.stack.frames[int(round(z))] + p = xy_frame.compute(WeightedCentroid()) + # take a profile of the x/y resolution + x_profile = xy_frame.array[int(round(p.y)), :] + self.x_axis = TomographicResolutionAxisData( + "X", x_profile, self.stack.metadata.PixelSpacing[0] + ) + y_profile = xy_frame.array[:, int(round(p.x))] + self.y_axis = TomographicResolutionAxisData( + "Y", y_profile, self.stack.metadata.PixelSpacing[0] + ) + # z axis. resolution is the spacing between slices. + z_profile = array_3d[:, int(round(p.y)), int(round(p.x))] + dpmm = abs(self.stack.metadata.SpacingBetweenSlices) + self.z_axis = TomographicResolutionAxisData("Z", z_profile, dpmm) + + def results(self) -> str: + """Return a string representation of the results.""" + return ( + f"Tomographic Resolution results for {self.path.name}\n" + f"X-axis FWHM (mm): {self.x_axis.fwhm:.3f}\n" + f"Y-axis FWHM (mm): {self.y_axis.fwhm:.3f}\n" + f"Z-axis FWHM (mm): {self.z_axis.fwhm:.3f}\n" + f"X-axis FWTM (mm): {self.x_axis.fwtm:.3f}\n" + f"Y-axis FWTM (mm): {self.y_axis.fwtm:.3f}\n" + f"Z-axis FWTM (mm): {self.z_axis.fwtm:.3f}\n" + ) + + def results_data( + self, as_dict: bool = False + ) -> dict[str, float] | TomographicResolutionResults: + """Return the results as a structure.""" + r = TomographicResolutionResults( + x_fwhm=self.x_axis.fwhm, + y_fwhm=self.y_axis.fwhm, + z_fwhm=self.z_axis.fwhm, + x_fwtm=self.x_axis.fwtm, + y_fwtm=self.y_axis.fwtm, + z_fwtm=self.z_axis.fwtm, + ) + if as_dict: + return asdict(r) + else: + return r + + def plot(self) -> (list[Figure], list[Axes]): + """Plot the x/y/z profiles and their gaussian fits.""" + figs = [] + axes = [] + # plot each axis: x, y, z + for axis in (self.x_axis, self.y_axis, self.z_axis): + fig, ax = axis.plot() + figs.append(fig) + axes.append(ax) + return figs, axes + + +def fwhm_from_gaussian(std: float) -> float: + """Return the FWHM of a gaussian given its standard deviation.""" + return 2 * math.sqrt(2 * math.log(2)) * std + + +def fwtm_from_gaussian(std: float) -> float: + """Return the FWTM of a gaussian given its standard deviation.""" + return 2 * math.sqrt(2 * math.log(10)) * std + + +def gaussian_fit( + x: np.ndarray, amplitude: float, mean: float, stddev: float +) -> np.ndarray: + return amplitude * np.exp(-((x - mean) ** 2) / (2 * (stddev**2))) + + +def two_peak_gaussian_fit( + x: np.ndarray, + amplitude1: float, + mean1: float, + stddev1: float, + amplitude2: float, + mean2: float, + stddev2: float, +) -> np.ndarray: + return amplitude1 * np.exp( + -((x - mean1) ** 2) / (2 * (stddev1**2)) + ) + amplitude2 * np.exp(-((x - mean2) ** 2) / (2 * (stddev2**2))) + + +class Nuclide: + Tc99m = { + "half_life_s": 6.0067 * 60 * 60 + } # 6.0067 hours https://web.archive.org/web/20160804083942/http://www.nucleide.org/DDEP_WG/Nuclides/Tc-99m_tables.pdf + Y90 = {"half_life_s": 64.1 * 60 * 60} # 64.1 hours https://www.nndc.bnl.gov/nudat3/ + I131 = { + "half_life_s": 8.019 * 24 * 60 * 60 + } # 8.019 days https://www.nndc.bnl.gov/nudat3/ + Ga67 = { + "half_life_s": 3.261 * 24 * 60 * 60 + } # 3.261 days https://www.nndc.bnl.gov/nudat3/ + In111 = { + "half_life_s": 2.804 * 24 * 60 * 60 + } # 2.804 days https://www.nndc.bnl.gov/nudat3/ + Lu177 = { + "half_life_s": 6.647 * 24 * 60 * 60 + } # 6.647 days https://www.nndc.bnl.gov/nudat3/ + + +@dataclass +class SimpleSensitivityResults: + phantom_cps: float #: + background_cps: float #: + half_life_s: float #: + duration_s: float #: + decay_correction: float #: + sensitivity_mbq: float #: + sensitivity_uci: float #: + + +class SimpleSensitivity: + """The 'simple' sensitivity test as defined by IAEA 2.3.9. Equations come from the IAEA NMQC toolkit.""" + + half_life_s: float + activity_mbq: float + + def __init__( + self, phantom_path: str | Path, background_path: str | Path | None = None + ): + self.phantom_path = Path(phantom_path) + self.background_path = ( + Path(background_path) if background_path is not None else None + ) + + @property + def phantom_cps(self) -> float: + """The counts per second of the phantom.""" + phantom_img = DicomImage(self.phantom_path, raw_pixels=True) + counts = phantom_img.array.sum() + return counts / self.duration_s + + @property + def duration_s(self) -> float: + """The duration of the phantom image.""" + phantom_img = DicomImage(self.phantom_path, raw_pixels=True) + return phantom_img.metadata.ActualFrameDuration / 1000 + + @property + def background_cps(self) -> float: + """The counts per second of the background.""" + if self.background_path is None: + return 0 + else: + background_stack = NMImageStack(self.background_path) + duration_s = background_stack.metadata.ActualFrameDuration / 1000 + # mean background + avg_count = background_stack.as_3d_array().mean(axis=0).sum() + return avg_count / duration_s + + def analyze(self, activity_mbq: float, nuclide: type[Nuclide]): + self.half_life_s = nuclide["half_life_s"] + self.activity_mbq = activity_mbq + + def results(self) -> str: + """Return a string representation of the results.""" + return ( + f"Simple Sensitivity results for {self.phantom_path.name}\n" + f"Phantom c/s: {self.phantom_cps:.0f}\n" + f"Background c/p: {self.background_cps:.0f}\n" + f"Half-life: {self.half_life_s:.0f}\n" + f"Duration: {self.duration_s:.0f}\n" + f"Decay Correction: {self.decay_correction:.3f}\n" + f"Sensitivity (MBq): {self.sensitivity_mbq:.3f}\n" + f"Sensitivity (uCi): {self.sensitivity_uci:.3f}\n" + ) + + def results_data( + self, as_dict: bool = False + ) -> dict[str, float] | SimpleSensitivityResults: + """Return the results as a structure.""" + r = SimpleSensitivityResults( + phantom_cps=self.phantom_cps, + background_cps=self.background_cps, + half_life_s=self.half_life_s, + duration_s=self.duration_s, + decay_correction=self.decay_correction, + sensitivity_mbq=self.sensitivity_mbq, + sensitivity_uci=self.sensitivity_uci, + ) + if as_dict: + return asdict(r) + else: + return r + + @property + def decay_correction(self) -> float: + """The decay correction factor.""" + x = np.log(2) * self.duration_s / self.half_life_s + return 1 / x * (1 - np.exp(-x)) + + @property + def sensitivity_mbq(self) -> float: + """The sensitivity in MBq.""" + return ( + self.phantom_cps / self.decay_correction - self.background_cps + ) / self.activity_mbq + + @property + def sensitivity_uci(self) -> float: + """The sensitivity in uCi.""" + mqb_to_uci = 27.02702702702703 # 1 MBq = 27.02702702702703 uCi + cpm = 60 + return self.sensitivity_mbq * cpm / mqb_to_uci + + +@dataclass +class DoubleGaussianProfile: + axis: str #: + profile_array: np.ndarray #: + pixel_size: float #: + separation_mm: float #: + + def __post_init__(self): + xs = np.arange(len(self.profile_array)) * self.pixel_size + peak_idxs, other = find_peaks(self.profile_array, max_number=2, threshold=0.1) + self.popt, _ = curve_fit( + two_peak_gaussian_fit, + xs, + self.profile_array, + p0=[ + np.max(self.profile_array), + peak_idxs[0], + self.pixel_size, + np.max(self.profile_array), + peak_idxs[1], + self.pixel_size, + ], + ) + + @property + def fwhm(self) -> float: + """The full width at half maximum of the x-profile.""" + fwhm1 = fwhm_from_gaussian(self.popt[2]) + fwhm2 = fwhm_from_gaussian(self.popt[5]) + return (fwhm1 + fwhm2) / 2 + + @property + def fwtm(self) -> float: + """The full width at tenth maximum of the x-profile.""" + fwtm1 = fwtm_from_gaussian(self.popt[2]) + fwtm2 = fwtm_from_gaussian(self.popt[5]) + return (fwtm1 + fwtm2) / 2 + + @property + def measured_pixel_size(self) -> float: + """The measured pixel size of the image.""" + # the separation is already in physical units; we have to go back to pixels + # to get the mm/px, which is what we're after + separation_px = abs(self.popt[4] - self.popt[1]) / self.pixel_size + return self.separation_mm / separation_px + + @property + def pixel_size_difference(self) -> float: + """The difference between the measured pixel size and the expected pixel size.""" + return (self.measured_pixel_size - self.pixel_size) / self.pixel_size * 100 + + def plot(self) -> (Figure, Axes): + fig, ax = plt.subplots() + xs = np.arange(len(self.profile_array)) * self.pixel_size + x_interpolated = ( + np.linspace(0, len(self.profile_array), num=len(self.profile_array) * 20) + * self.pixel_size + ) + fitted_y = two_peak_gaussian_fit(x_interpolated, *self.popt) + # plot raw + ax.plot(xs, self.profile_array, "bo", label="Raw Data") + # plot fitted gaussian + ax.plot(x_interpolated, fitted_y, "r-", label="Gaussian Fit") + ax.grid(True) + ax.legend() + ax.set_xlabel("Distance (mm)") + ax.set_ylabel("Counts") + fig.suptitle(f"{self.axis}-axis profile") + return fig, ax + + +@dataclass +class FourBarResolutionResults: + x_fwhm: float + y_fwhm: float + x_fwtm: float + y_fwtm: float + x_measured_pixel_size: float + y_measured_pixel_size: float + x_pixel_size_difference: float + y_pixel_size_difference: float + + +class FourBarResolution: + """Spatial resolution in the X and Y direction as measured by a 'four-bar' phantom. + + Parameters + ---------- + path : str | Path + The path to the DICOM file. + """ + + y_prof: RectangleROI + x_prof: RectangleROI + y_axis: DoubleGaussianProfile + x_axis: DoubleGaussianProfile + + def __init__(self, path: str | Path): + self.stack = NMImageStack(path) + self.path = Path(path) + + def analyze(self, separation_mm: float = 100, roi_width_mm: float = 10) -> None: + """Take a vertical and horizontal profile about the center of the image. + + Fit two gaussians to find the two peaks in each direction. + + Parameters + ---------- + separation_mm : float + The distance between the two peaks in mm. The length of the ROI to sample + will be 2x the separation **perpendicular to the sample lines**. + roi_width_mm : float + The width of the ROI. This is the width **in the direction** of the sample lines. + """ + pixel_size = self.stack.metadata.PixelSpacing[0] + width_px = roi_width_mm / self.stack.metadata.PixelSpacing[0] + height_px = separation_mm * 2 / self.stack.metadata.PixelSpacing[0] + center = Point(self.stack.metadata.Rows / 2, self.stack.metadata.Columns / 2) + self.y_prof = RectangleROI( + self.stack.frames[0], + width=width_px, + height=height_px, + angle=0, + dist_from_center=0, + phantom_center=center, + ) + v_array = self.y_prof.pixel_array.mean(axis=-1) + self.y_axis = DoubleGaussianProfile( + "Y/Vertical", v_array, pixel_size, separation_mm + ) + self.x_prof = RectangleROI( + self.stack.frames[0], + width=height_px, + height=width_px, + angle=0, + dist_from_center=0, + phantom_center=center, + ) + h_array = self.x_prof.pixel_array.mean(axis=0) + self.x_axis = DoubleGaussianProfile( + "X/Horizontal", h_array, pixel_size, separation_mm + ) + + def results(self) -> str: + """Return a string representation of the results.""" + return ( + f"Four Bar Resolution results for {self.path.name}\n" + f"X-axis FWHM (mm): {self.x_axis.fwhm:.3f}\n" + f"X-axis FWTM (mm): {self.x_axis.fwtm:.3f}\n" + f"X-axis Measured Pixel size (mm): {self.x_axis.measured_pixel_size:.3f}\n" + f"X-axis Pixel size difference (%): {self.x_axis.pixel_size_difference:.2f}\n" + f"Y-axis FWHM (mm): {self.y_axis.fwhm:.3f}\n" + f"Y-axis FWTM (mm): {self.y_axis.fwtm:.3f}\n" + f"Y-axis Measured Pixel size (mm): {self.y_axis.measured_pixel_size:.3f}\n" + f"Y-axis Pixel size difference (%): {self.y_axis.pixel_size_difference:.2f}\n" + ) + + def results_data(self, as_dict: bool = False) -> dict | FourBarResolutionResults: + """Return the results as a structure.""" + r = FourBarResolutionResults( + x_fwhm=self.x_axis.fwhm, + y_fwhm=self.y_axis.fwhm, + x_fwtm=self.x_axis.fwtm, + y_fwtm=self.y_axis.fwtm, + x_measured_pixel_size=self.x_axis.measured_pixel_size, + y_measured_pixel_size=self.y_axis.measured_pixel_size, + x_pixel_size_difference=self.x_axis.pixel_size_difference, + y_pixel_size_difference=self.y_axis.pixel_size_difference, + ) + if as_dict: + return asdict(r) + else: + return r + + def plot(self, show: bool = True) -> (list[Figure], list[Axes]): + """Plot the image with the sample ROIs and the x/y profiles with gaussian fits""" + figs = [] + axes = [] + fig, ax = plt.subplots() + figs.append(fig) + axes.append(ax) + ax.imshow(self.stack.frames[0].array, cmap="gray") + self.x_prof.plot2axes(ax, edgecolor="y") + self.y_prof.plot2axes(ax, edgecolor="y") + fig.suptitle(f"Four Bar Resolution for {self.path.name}") + fig, ax = self.x_axis.plot() + figs.append(fig) + axes.append(ax) + fig, ax = self.y_axis.plot() + figs.append(fig) + axes.append(ax) + if show: + plt.show() + + return figs, axes + + +@dataclass +class QuadrantResolutionResults: + quadrants: dict[ + str, dict[str, float] + ] #: quadrant idx: {'mtf': mtf, 'fwhm': fwhm, 'lpmm': lpmm} + + +class QuadrantResolution: + """Analyze a 4-quadrant image of high-contrast line pairs to determine MTF and FWHM. + + Parameters + ---------- + path : str | Path + The path to the DICOM file. + """ + + rois: dict[str, HighContrastDiskROI] + mtf: MomentMTF + + def __init__(self, path: str | Path) -> None: + self.stack = NMImageStack(path) + self.path = Path(path) + + def analyze( + self, + bar_widths: Sequence[float], + roi_diameter_mm: float = 70, + distance_from_center_mm: float = 130, + ) -> None: + """Analyze the image to determine the MTF resolution and FWHMs. + + Parameters + ---------- + bar_widths : list + The bar widths in mm. A line pair will be 2x this value. + roi_diameter_mm : float + The diameter of the ROI in mm. + distance_from_center_mm : float + The distance from the center of the image to the center of the ROI in mm. + """ + if len(bar_widths) != 4: + raise ValueError("Must have 4 bar widths") + lpmm = 1 / (2 * np.asarray(bar_widths)) + self.rois = {} + img_center = Point( + self.stack.metadata.Rows / 2, self.stack.metadata.Columns / 2 + ) + angles = (45, -45, -135, 135) + for angle, spacing in zip(angles, bar_widths): + roi = HighContrastDiskROI( + self.stack.frames[0], + angle=angle, + roi_radius=roi_diameter_mm, + dist_from_center=distance_from_center_mm, + phantom_center=img_center, + contrast_threshold=0, + ) + self.rois[spacing] = roi + + self.mtf = MomentMTF.from_high_contrast_diskset(lpmm, list(self.rois.values())) + + def results(self) -> str: + """Return a string representation of the results.""" + s = f"Quadrant Resolution results for {self.path.name}\n" + for quadrant, ((lpmm, mtf), fwhm) in enumerate( + zip(self.mtf.mtfs.items(), self.mtf.fwhms.values()) + ): + spacing = 1 / (lpmm * 2) + s += f"Quadrant {quadrant+1}; Bar width: {spacing:.2f}mm; FWHM: {fwhm:.3f}mm; MTF: {mtf:.3f}\n" + return s + + def results_data(self, as_dict: bool = False) -> dict | QuadrantResolutionResults: + """Return the results as a structure.""" + r = QuadrantResolutionResults( + quadrants={ + f"{idx+1}": { + "mtf": mtf, + "fwhm": fwhm, + "lpmm": lpmm, + "spacing": 1 / (lpmm * 2), + } + for idx, ((lpmm, mtf), fwhm) in enumerate( + zip(self.mtf.mtfs.items(), self.mtf.fwhms.values()) + ) + } + ) + if as_dict: + return asdict(r) + else: + return r + + def plot(self, show: bool = True) -> (list[Figure], list[Axes]): + """Plot the image, the MTF, and the FWHMs.""" + figs = [] + axes = [] + fig, ax = plt.subplots() + figs.append(fig) + axes.append(ax) + ax.imshow(self.stack.frames[0].array, cmap="gray") + for idx, (spacing, roi) in enumerate(self.rois.items()): + roi.plot2axes(ax, edgecolor="y", text=f"{idx+1}: {spacing:.2f}mm") + fig.suptitle(f"Quadrant Resolution for {self.path.name}") + # plot MTFs + fig, ax = plt.subplots() + figs.append(fig) + axes.append(ax) + self.mtf.plot(ax) + # plot FWHMs + fig, ax = plt.subplots() + figs.append(fig) + axes.append(ax) + self.mtf.plot_fwhms(ax) + if show: + plt.show() + + return figs, axes + + +@dataclass +class TomographicUniformityResults: + cfov_integral_uniformity: float + cfov_differential_uniformity: float + ufov_integral_uniformity: float + ufov_differential_uniformity: float + center_border_ratio: float + first_frame: int + last_frame: int + + +class TomographicUniformity(PlanarUniformity): + """Evaluation of the tomographic uniformity of a SPECT image. Typically, a Jaszczak phantom or similar. + This is similar to the Planar Uniformity test.""" + + center_ratio: float + first_frame: int + last_frame: int + threshold: float + + @property + def frame_result(self) -> dict: + """We always have a single result""" + return self.frame_results[self.frame_key] + + @property + def frame_key(self) -> str: + """The key for the single frame result""" + return f"{self.first_frame}:{self.last_frame}" + + def center_border_ratio(self, center_ratio: float, window_size: int) -> float: + """The center-to-border ratio as defined by the NMQC toolkit. + + The center ROI is a 6cm diameter circle in the center of the phantom. + The border ROI is the subtraction of the CFOV from the UFOV. + """ + cleaned_frame, _ = self.preprocess(self.stack.frames[0], self.threshold) + center_array, center_x, center_y = get_fov(cleaned_frame, size=center_ratio) + center_fov = FOV( + name="Center", + fov=center_array, + boundary_x=center_x, + boundary_y=center_y, + window_size=window_size, + ) + self.frame_result["center_fov"] = center_fov + # now get the UFOV - CFOV array + mask = self.frame_result["cfov"].fov != 0 + ring = np.copy(self.frame_result["ufov"].fov) + ring[mask] = np.nan + ring[ring == 0] = np.nan + center_array[center_array == 0] = np.nan + return np.nanmean(center_array) / np.nanmean(ring) + + def analyze( + self, + first_frame: int = 0, + last_frame: int = -1, + ufov_ratio: float = 0.8, + cfov_ratio: float = 0.75, + center_ratio: float = 0.4, + threshold: float = 0.75, + window_size: int = 5, + ) -> None: + """Analyze the image to determine the uniformity. + This will take a mean of pixel values for frames between the first and last stated frame. + + Parameters + ---------- + first_frame : int + The index of the first frame to analyze. + last_frame : int + The index of the last frame to analyze. + ufov_ratio : float + The ratio of the UFOV to the phantom. + cfov_ratio : float + The ratio of the central FOV to the UFOV. + center_ratio : float + The ratio of the center ROI to the phantom. + threshold : float + The threshold to use for the image. + """ + # create a new single composite frame + self.threshold = threshold + array = self.stack.as_3d_array() + if first_frame < 0: + raise ValueError( + "The first frame index is outside the array bounds. Increase the first frame index." + ) + if last_frame < 0: + last_frame += array.shape[0] + if last_frame >= array.shape[0]: + raise ValueError( + "The last frame index is outside the array bounds. Decrease the last frame index." + ) + if 0 < last_frame <= first_frame: + raise ValueError( + "The first frame index must be less than the last frame index." + ) + # create the frankenstein frame + new_array = array[first_frame:last_frame, :, :].mean(axis=0) + new_frame = self.stack.frames[0] + new_frame.array = new_array + self.stack.frames = [ + new_frame, + ] + # we offset the indices by 1 since the first frame is 1, not 0 + self.first_frame = first_frame + 1 + self.last_frame = last_frame + 1 + super().analyze( + ufov_ratio=ufov_ratio, + threshold=threshold, + cfov_ratio=cfov_ratio, + window_size=window_size, + ) + # rename from frame 1 to a dynamic name + self.frame_results[self.frame_key] = self.frame_results.pop("1") + self.center_ratio = self.center_border_ratio( + center_ratio=center_ratio * ufov_ratio, window_size=window_size + ) + + def results_data( + self, as_dict: bool = False + ) -> dict | TomographicUniformityResults: + """Return the results as a structure.""" + r = TomographicUniformityResults( + cfov_integral_uniformity=self.frame_result["cfov"].integral_uniformity, + cfov_differential_uniformity=self.frame_result[ + "cfov" + ].differential_uniformity, + ufov_integral_uniformity=self.frame_result["ufov"].integral_uniformity, + ufov_differential_uniformity=self.frame_result[ + "ufov" + ].differential_uniformity, + center_border_ratio=self.center_ratio, + first_frame=self.first_frame, + last_frame=self.last_frame, + ) + if as_dict: + return asdict(r) + else: + return r + + def results(self) -> str: + """Return a string representation of the results.""" + return ( + f"Tomographic Uniformity results for {self.path.name}\n" + f"Frames: {self.first_frame}:{self.last_frame}\n" + f"CFOV Integral Uniformity: {self.frame_result['cfov'].integral_uniformity:.3f}%\n" + f"CFOV Differential Uniformity: {self.frame_result['cfov'].differential_uniformity:.3f}%\n" + f"UFOV Integral Uniformity: {self.frame_result['ufov'].integral_uniformity:.3f}%\n" + f"UFOV Differential Uniformity: {self.frame_result['ufov'].differential_uniformity:.3f}%\n" + f"Center-to-Border ratio: {self.center_ratio:.3f}\n" + ) + + def plot(self, show: bool = True, cmap: str = "gray") -> (list[Figure], list[Axes]): + """Plot the image with the sample ROIs""" + figs, axes = super().plot(show=False, cmap=cmap) + axis = axes[0] + # plot the center ROI + self.frame_result["center_fov"].plot_to(axis, color="b") + if show: + plt.show() + + +@dataclass +class TomographicROI: + array3d: np.ndarray + uniformity_baseline: float + x: float + y: float + z: float + radius: float + number: str | int + + def __post_init__(self): + self.sphere_array = ( + sample_sphere( + self.array3d, col=self.x, row=self.y, zed=self.z, radius=self.radius + ), + ) + + @property + def mean_value(self) -> float: + return float(np.nanmean(self.sphere_array)) + + @property + def min_value(self) -> float: + return float(np.nanmin(self.sphere_array)) + + @property + def mean_contrast(self) -> float: + return michelson(np.asarray([self.mean_value, self.uniformity_baseline])) * 100 + + @property + def max_contrast(self) -> float: + return michelson(np.asarray([self.min_value, self.uniformity_baseline])) * 100 + + def plot_to(self, axis: Axes): + """Plot the ROI to the axis.""" + d = DiskROI( + array=self.sphere_array, + angle=0, + roi_radius=self.radius, + dist_from_center=0, + phantom_center=Point(self.x, self.y), + ) + d.plot2axes(axes=axis, edgecolor="r", text=str(self.number)) + + +class TomgraphicSphere(TypedDict): + x: float + y: float + z: float + radius: float + mean: float + mean_contrast: float + max_contrast: float + + +@dataclass +class TomographicContrastResults: + uniformity_baseline: float + spheres: dict[str, TomgraphicSphere] + + +class TomographicContrast: + rois: dict[str, TomographicROI] + + def __init__(self, path: str | Path): + self.stack = NMImageStack(path) + self.path = Path(path) + + @cached_property + def slice_data(self) -> dict[str, dict[str, float | Point]]: + uniformities = {} + array3d = self.stack.as_3d_array() + global_max = array3d.max() + for idx, frame in enumerate(self.stack.frames): + # remove pixels that are <75% of mean of "meaningful" pixels + # meaningful pixels are those > 10% of max + # this helps remove the background + arr = np.copy(frame.array) + threshold = global_max * 0.10 + arr[arr < threshold] = 0 + binary_frame = arr > 0 + labeled_frame, num_labels = label( + binary_frame, connectivity=1, return_num=True + ) + if num_labels < 1: + continue + rois = regionprops(labeled_frame, intensity_image=arr) + largest_roi = max(rois, key=lambda x: x.area) + longest_dim = max(largest_roi.image.shape) + erosion = int(round((1 - self.ufov_ratio) * longest_dim)) + eroded_binary = isotropic_erosion(binary_frame, radius=erosion / 2) + fov_array = np.where(eroded_binary, arr, np.nan) + u = michelson(fov_array) + uniformities[str(idx + 1)] = { + "fov diameter": longest_dim - erosion, + "center": Point(x=largest_roi.centroid[1], y=largest_roi.centroid[0]), + "area": np.count_nonzero(eroded_binary), + "uniformity": u, + "value": np.nanmean(fov_array), + } + # drop any frames where the area was significantly smaller than the median area + # this mostly leads to dropping the edge frames, for good reason. + median_area = np.median([v["area"] for v in uniformities.values()]) + std_area = np.std([v["area"] for v in uniformities.values()]) + return { + k: v for k, v in uniformities.items() if v["area"] > median_area - std_area + } + + @property + def uniformity_frame(self) -> str: + """The frame with the most uniformity.""" + return min(self.slice_data, key=lambda x: self.slice_data.get(x)["uniformity"]) + + @property + def uniformity_value(self) -> float: + return self.slice_data[self.uniformity_frame]["value"] + + def analyze( + self, + sphere_diameters_mm: Sequence[float] = (38, 31.8, 25.4, 19.1, 15.9, 12.7), + sphere_angles: Sequence[float] = (-10, -70, -130, -190, 110, 50), + ufov_ratio: float = 0.8, + search_window_px: int = 5, + search_slices: int = 3, + ) -> None: + """Analyze the image to determine the contrast. + + Parameters + ---------- + sphere_diameters_mm : list + The diameters of the spheres in mm. + sphere_angles : list + + """ + self.ufov_ratio = ufov_ratio + uniformities = self.slice_data + if len(sphere_diameters_mm) != len(sphere_angles): + raise ValueError( + "The number of sphere diameters and angles must be the same." + ) + + # find the most non-uniform slice + # this will usually be near the sphere slice + # from this starting point, sample the spheres + # optimize the location based on best contrast value + max_uniformity_frame = max( + uniformities, key=lambda x: uniformities[x]["uniformity"] + ) + unif = uniformities[max_uniformity_frame] + unif_z = int(max_uniformity_frame) - 1 + + array3d = self.stack.as_3d_array() + rois = {} + for idx, (angle, diameter) in enumerate( + zip(sphere_angles, sphere_diameters_mm) + ): + distance = math.sqrt(unif["area"] / math.pi) * 0.65 + radius = diameter / (2 * self.stack.metadata.PixelSpacing[0]) + col_x, row_y = direction_to_coords( + unif["center"].x, unif["center"].y, distance, angle + ) + # quicker but less accurate at the smallest ROIs + res = minimize( + contrast_f, + x0=(col_x, row_y, unif_z), + args=(array3d, radius, self.uniformity_value), + method="Nelder-Mead", + bounds=[ + (col_x - search_window_px, col_x + search_window_px), + (row_y - search_window_px, row_y + search_window_px), + (unif_z - search_slices, unif_z + search_slices), + ], + ) + # alternatives left for future potential work + # res = brute(contrast_f, ranges=[(col_x - search_window_px, col_x + search_window_px), (row_y - search_window_px, row_y + search_window_px), (unif_z - search_slices, unif_z + search_slices)], args=(array3d, radius, self.uniformity_value), Ns=search_window_px*2, full_output=False, finish=None) + # res = differential_evolution(contrast_f, bounds=[(col_x - search_window_px, col_x + search_window_px), (row_y - search_window_px, row_y + search_window_px), (unif_z - search_slices, unif_z + search_slices)], args=(array3d, radius, self.uniformity_value), polish=False, x0=(col_x, row_y, unif_z), seed=1234) + col, row, zed = res.x + roi = TomographicROI( + array3d=array3d, + x=col, + y=row, + z=zed, + radius=radius, + uniformity_baseline=self.uniformity_value, + number=idx + 1, + ) + rois[str(idx + 1)] = roi + self.rois = rois + + def results(self) -> str: + """Return a string representation of the results.""" + s = f"Tomographic Contrast results for {self.path.name}\n" + s += f"Uniformity baseline: {self.uniformity_value:.1f}\n" + for idx, roi in self.rois.items(): + s += f"Sphere {idx}: X={roi.x:.2f},Y={roi.y:.2f},Z={roi.z:.2f} Mean: {roi.mean_value:.2f}; Mean Contrast: {roi.mean_contrast:.2f}; Max Contrast: {roi.max_contrast:.2f}\n" + return s + + def results_data(self, as_dict: bool = False) -> dict | TomographicContrastResults: + """Return the results as a structure.""" + r = TomographicContrastResults( + uniformity_baseline=self.uniformity_value, + spheres={ + idx: { + "x": roi.x, + "y": roi.y, + "z": roi.z, + "mean": roi.mean_value, + "mean_contrast": roi.mean_contrast, + "max_contrast": roi.max_contrast, + } + for idx, roi in self.rois.items() + }, + ) + if as_dict: + return asdict(r) + else: + return r + + def plot(self, show: bool = True) -> (list[Figure], list[Axes]): + """Plot the uniformity frame, sphere ROI frame, and contrast vs sphere number.""" + # plot the ROIs + roi_fig, roi_ax = plt.subplots() + # show all ROIs on the most common sphere slice for simplicity + median_slice = int(round(np.median([roi.z for roi in self.rois.values()]))) + roi_ax.imshow(self.stack.frames[median_slice].array, cmap="gray") + for roi in self.rois.values(): + roi.plot_to(roi_ax) + roi_ax.set_title(f"Sphere frame ({median_slice+1})") + # plot the uniformity slice + unif_fig, unif_ax = plt.subplots() + unif_ax.imshow( + self.stack.frames[int(self.uniformity_frame) - 1].array, cmap="gray" + ) + un_data = self.slice_data[self.uniformity_frame] + Circle( + (un_data["center"].x, un_data["center"].y), + radius=un_data["fov diameter"] / 2, + ).plot2axes(unif_ax, edgecolor="b") + unif_ax.set_title(f"Uniformity frame ({self.uniformity_frame})") + # plot the contrast vs sphere number + cont_fig, cont_ax = plt.subplots() + cont_ax.plot( + [int(idx) for idx in self.rois.keys()], + [roi.mean_contrast for roi in self.rois.values()], + color="b", + marker="o", + label="Mean Contrast", + ) + cont_ax.plot( + [int(idx) for idx in self.rois.keys()], + [roi.max_contrast for roi in self.rois.values()], + color="r", + marker="o", + label="Max Contrast", + ) + cont_ax.set_xlabel("Sphere Number") + cont_ax.set_ylabel("Contrast (Michelson * 100)") + cont_ax.legend() + cont_ax.grid(True) + cont_ax.set_title("Contrast vs Sphere Number") + if show: + plt.show() + return (roi_fig, unif_fig, cont_fig), (roi_ax, unif_ax, cont_ax) + + +def create_sphere_mask( + array_shape: tuple[float, float, float], + row: float, + col: float, + zed: float, + radius: float, +) -> np.ndarray: + """Create a mask of a sphere in an array.""" + z, y, x = np.ogrid[: array_shape[0], : array_shape[1], : array_shape[2]] + mask = (x - col) ** 2 + (y - row) ** 2 + (z - zed) ** 2 <= radius**2 + return mask + + +def sample_sphere( + array: np.ndarray, row: float, col: float, zed: float, radius: float +) -> np.ndarray: + """Sample out a sphere from an array. Uses a mask.""" + sphere_mask = create_sphere_mask( + array.shape, row=row, col=col, zed=zed, radius=radius + ) + sphere_sample = np.full(array.shape, np.nan) + sphere_sample[sphere_mask] = array[sphere_mask] + return sphere_sample + + +def contrast_f( + coords: np.ndarray, array: np.ndarray, radius: float, uniformity_baseline: float +) -> float: + """The function to minimize for the sphere sampling.""" + col, row, zed = coords + sample = sample_sphere(array, col=col, row=row, zed=zed, radius=radius) + return -michelson(np.asarray([np.nanmean(sample), uniformity_baseline])) * 100 diff --git a/tests_basic/core/test_geometry.py b/tests_basic/core/test_geometry.py index 8086953bc..30a2f3f20 100644 --- a/tests_basic/core/test_geometry.py +++ b/tests_basic/core/test_geometry.py @@ -2,7 +2,7 @@ import math import unittest -from pylinac.core.geometry import Circle, Line, Point, Rectangle +from pylinac.core.geometry import Circle, Line, Point, Rectangle, direction_to_coords from tests_basic.utils import point_equality_validation @@ -172,3 +172,29 @@ def test_corners(self): point_equality_validation(rect.br_corner, self.br_corner) point_equality_validation(rect.tr_corner, self.tr_corner) point_equality_validation(rect.tl_corner, self.tl_corner) + + +class TestDestinationCoordinates(unittest.TestCase): + def test_45_degrees(self): + self.assertAlmostEqual(direction_to_coords(0, 0, 10, 45)[0], 7.071, places=3) + self.assertAlmostEqual(direction_to_coords(0, 0, 10, 45)[1], 7.071, places=3) + + def test_90_degrees(self): + self.assertAlmostEqual(direction_to_coords(0, 0, 10, 90)[0], 0, places=3) + self.assertAlmostEqual(direction_to_coords(0, 0, 10, 90)[1], 10, places=3) + + def test_180_degrees(self): + self.assertAlmostEqual(direction_to_coords(0, 0, 10, 180)[0], -10, places=3) + self.assertAlmostEqual(direction_to_coords(0, 0, 10, 180)[1], 0, places=3) + + def test_270_degrees(self): + self.assertAlmostEqual(direction_to_coords(0, 0, 10, 270)[0], 0, places=3) + self.assertAlmostEqual(direction_to_coords(0, 0, 10, 270)[1], -10, places=3) + + def test_negative_angle(self): + self.assertAlmostEqual(direction_to_coords(0, 0, 10, -45)[0], 7.071, places=3) + self.assertAlmostEqual(direction_to_coords(0, 0, 10, -45)[1], -7.071, places=3) + + def test_starting_position(self): + self.assertAlmostEqual(direction_to_coords(5, 5, 10, 0)[0], 15, places=3) + self.assertAlmostEqual(direction_to_coords(5, 5, 10, 0)[1], 5, places=3) diff --git a/tests_basic/test_nuclear.py b/tests_basic/test_nuclear.py new file mode 100644 index 000000000..8ff743a98 --- /dev/null +++ b/tests_basic/test_nuclear.py @@ -0,0 +1,432 @@ +from pathlib import Path +from unittest import TestCase + +import numpy as np +from matplotlib import pyplot as plt + +from pylinac.nuclear import ( + CenterOfRotation, + CenterOfRotationResults, + FourBarResolution, + MaxCountRate, + Nuclide, + PlanarUniformity, + PlanarUniformityResults, + QuadrantResolution, + QuadrantResolutionResults, + SimpleSensitivity, + SimpleSensitivityResults, + TomographicContrast, + TomographicContrastResults, + TomographicResolution, + TomographicUniformity, + TomographicUniformityResults, + determine_binning, + integral_uniformity, +) +from tests_basic.utils import get_file_from_cloud_test_repo + +TEST_DIR = Path("nuclear") + + +class TestMaxCountRate(TestCase): + def setUp(self) -> None: + plt.close("all") + p = get_file_from_cloud_test_repo([TEST_DIR, "MaxCountRate.dcm"]) + self.m = MaxCountRate(p) + + def test_max_count_rate(self): + self.m.analyze() + # known value from NMQC toolkit + self.assertAlmostEqual(self.m.max_countrate, 358437, delta=1) + + def test_max_frame(self): + self.m.analyze() + self.assertEqual(self.m.max_frame, 15) + + def test_frame_duration_is_accounted_for(self): + # the same frame in half the time should have twice the countrate + self.m.analyze(frame_duration=0.5) + self.assertAlmostEqual(self.m.max_countrate, 358437 * 2, delta=1) + + def test_plot(self): + # should not raise + self.m.analyze() + self.m.plot() + self.m.plot(show=False) + + +class TestPlanarUniformityUtils(TestCase): + def test_binning(self): + binning = determine_binning(2) + self.assertEqual(binning, 4) + + binning = determine_binning(4.1) + self.assertEqual(binning, 2) + + binning = determine_binning(4.9) + self.assertEqual(binning, 1) + + def test_integral_uniformity(self): + # double check of michelson + u = integral_uniformity(np.asarray([1, 1])) + self.assertEqual(u, 0) + + u = integral_uniformity(np.asarray([10, 20])) + self.assertAlmostEqual(u, 10 / 30 * 100) + + +class TestPlanarUniformity(TestCase): + def setUp(self) -> None: + p = get_file_from_cloud_test_repo([TEST_DIR, "PlanarUniformity.dcm"]) + self.m = PlanarUniformity(p) + + def test_results(self): + self.m.analyze() + self.assertEqual(len(self.m.frame_results), 1) + self.assertAlmostEqual( + self.m.frame_results["1"]["ufov"].differential_uniformity, 1.728, delta=0.15 + ) # value from NMQC + self.assertAlmostEqual( + self.m.frame_results["1"]["ufov"].integral_uniformity, 2.38, delta=0.01 + ) + self.assertAlmostEqual( + self.m.frame_results["1"]["cfov"].differential_uniformity, 1.728, delta=0.01 + ) + self.assertAlmostEqual( + self.m.frame_results["1"]["cfov"].integral_uniformity, 2.38, delta=0.01 + ) + self.assertEqual(self.m.frame_results["1"]["binned_frame"].shape, (64, 64)) + + def test_results_data(self): + self.m.analyze() + data = self.m.results_data() + self.assertIsInstance(data, dict) + self.assertIsInstance(data["Frame 1"], PlanarUniformityResults) + d = self.m.results_data(as_dict=True) + self.assertIsInstance(d, dict) + self.assertIsInstance(d["Frame 1"], dict) + + def test_changing_window_size_affects_results(self): + self.m.analyze(window_size=5) + self.assertAlmostEqual( + self.m.frame_results["1"]["ufov"].differential_uniformity, 1.728, delta=0.15 + ) # default + self.m.analyze(window_size=2) + self.assertAlmostEqual( + self.m.frame_results["1"]["ufov"].differential_uniformity, 1.01, delta=0.01 + ) + + +class TestTwoFramePlanarUniformity(TestCase): + def setUp(self) -> None: + plt.close("all") + p = get_file_from_cloud_test_repo([TEST_DIR, "TwoFrameUniformity.dcm"]) + self.m = PlanarUniformity(p) + + def test_results(self): + self.m.analyze() + self.assertEqual(len(self.m.frame_results), 2) + # the NMQC toolkit gives a strange circular ROI for this, so values are just from pylinac + # and quite different from NMQC + self.assertAlmostEqual( + self.m.frame_results["1"]["ufov"].differential_uniformity, 4.23, delta=0.01 + ) + self.assertAlmostEqual( + self.m.frame_results["1"]["ufov"].integral_uniformity, 27.6, delta=0.01 + ) + self.assertAlmostEqual( + self.m.frame_results["1"]["cfov"].differential_uniformity, 3.11, delta=0.01 + ) + self.assertAlmostEqual( + self.m.frame_results["1"]["cfov"].integral_uniformity, 17.15, delta=0.01 + ) + self.assertEqual(self.m.frame_results["1"]["binned_frame"].shape, (128, 128)) + + self.assertAlmostEqual( + self.m.frame_results["2"]["ufov"].differential_uniformity, 4.05, delta=0.01 + ) + self.assertAlmostEqual( + self.m.frame_results["2"]["ufov"].integral_uniformity, 28.16, delta=0.01 + ) + self.assertAlmostEqual( + self.m.frame_results["2"]["cfov"].differential_uniformity, 3.33, delta=0.01 + ) + self.assertAlmostEqual( + self.m.frame_results["2"]["cfov"].integral_uniformity, 17.67, delta=0.01 + ) + self.assertEqual(self.m.frame_results["2"]["binned_frame"].shape, (128, 128)) + + +class TestCenterOfRotation102(TestCase): + @classmethod + def setUpClass(cls) -> None: + plt.close("all") + p = get_file_from_cloud_test_repo([TEST_DIR, "COR_102.dcm"]) + cls.m = CenterOfRotation(p) + cls.m.analyze() + + def test_x_cor_deviation(self): + self.assertAlmostEqual(self.m.x_cor_deviation_mm, 0.138, delta=0.01) + + def test_y_cor_deviation(self): + self.assertAlmostEqual(self.m.y_cor_deviation_mm, 0.129, delta=0.01) + + def test_results_data(self): + data = self.m.results_data() + self.assertIsInstance(data, CenterOfRotationResults) + self.assertAlmostEqual(data.x_deviation_mm, 0.138, delta=0.01) + self.assertAlmostEqual(data.y_deviation_mm, 0.129, delta=0.01) + + def test_plot(self): + # shouldn't raise + figs, axes = self.m.plot() + self.assertEqual(len(figs), 3) + self.assertEqual(len(axes), 3) + + +class TestCenterOfRotation180(TestCase): + @classmethod + def setUpClass(cls) -> None: + plt.close("all") + p = get_file_from_cloud_test_repo([TEST_DIR, "COR_180.dcm"]) + cls.m = CenterOfRotation(p) + cls.m.analyze() + + def test_x_cor_deviation(self): + self.assertAlmostEqual(self.m.x_cor_deviation_mm, 0.170, delta=0.01) + + def test_y_cor_deviation(self): + self.assertAlmostEqual(self.m.y_cor_deviation_mm, 0.207, delta=0.01) + + +class TestTomographicResolution(TestCase): + def setUp(self) -> None: + plt.close("all") + p = get_file_from_cloud_test_repo([TEST_DIR, "TomoResolution.dcm"]) + self.m = TomographicResolution(p) + self.m.analyze() + + def test_x_fwhm(self): + self.assertAlmostEqual(self.m.x_axis.fwhm, 22.31, delta=0.01) + + def test_x_fwtm(self): + self.assertAlmostEqual(self.m.x_axis.fwtm, 40.66, delta=0.01) + + def test_y_fwhm(self): + self.assertAlmostEqual(self.m.y_axis.fwhm, 22.33, delta=0.01) + + def test_y_fwtm(self): + self.assertAlmostEqual(self.m.y_axis.fwtm, 40.69, delta=0.01) + + def test_z_fwhm(self): + self.assertAlmostEqual(self.m.z_axis.fwhm, 22.47, delta=0.01) + + def test_z_fwtm(self): + self.assertAlmostEqual(self.m.z_axis.fwtm, 40.96, delta=0.01) + + +class TestSimpleSensitivityNoBackground(TestCase): + def setUp(self) -> None: + plt.close("all") + p = get_file_from_cloud_test_repo([TEST_DIR, "Sensitivity_Phantom.dcm"]) + self.m = SimpleSensitivity(p) + self.m.analyze(activity_mbq=10, nuclide=Nuclide.Tc99m) + + def test_duration(self): + self.assertAlmostEqual(self.m.duration_s, 300, delta=1) + + def test_sensitivity_mbq(self): + self.assertAlmostEqual( + self.m.sensitivity_mbq, 245.83, delta=0.05 + ) # value from NMQC + + def test_sensitivity_uci(self): + self.assertAlmostEqual( + self.m.sensitivity_uci, 545.817, delta=0.05 + ) # value from NMQC + + def test_results_data(self): + data = self.m.results_data() + self.assertIsInstance(data, SimpleSensitivityResults) + self.assertAlmostEqual(data.duration_s, 300, delta=0.01) + self.assertAlmostEqual(data.sensitivity_mbq, 245.83, delta=0.05) + + def test_results(self): + r = self.m.results() + self.assertIsInstance(r, str) + self.assertIn("Duration", r) + + +class TestSimpleSensitivityWithBackground(TestCase): + @classmethod + def setUp(cls) -> None: + plt.close("all") + p = get_file_from_cloud_test_repo([TEST_DIR, "Sensitivity_Phantom.dcm"]) + b = get_file_from_cloud_test_repo([TEST_DIR, "Sensitivity_Background.dcm"]) + cls.m = SimpleSensitivity(p, background_path=b) + cls.m.analyze(activity_mbq=10, nuclide=Nuclide.Tc99m) + + def test_duration(self): + self.assertAlmostEqual(self.m.duration_s, 300, delta=1) + + def test_sensitivity_mbq(self): + self.assertAlmostEqual(self.m.sensitivity_mbq, 243.912, delta=0.2) + + def test_sensitivity_uci(self): + self.assertAlmostEqual(self.m.sensitivity_uci, 541.484, delta=0.5) + + +class TestFourBar(TestCase): + def setUp(self) -> None: + p = get_file_from_cloud_test_repo([TEST_DIR, "FourBar.dcm"]) + self.m = FourBarResolution(p) + self.m.analyze(separation_mm=100, roi_width_mm=10) + + # all reference values from NMQC + + def test_x_fwhm(self): + self.assertAlmostEqual(self.m.x_axis.fwhm, 7.567, delta=0.1) + + def test_y_fwhm(self): + self.assertAlmostEqual(self.m.y_axis.fwhm, 7.53, delta=0.1) + + def test_x_measured_pixel_size(self): + self.assertAlmostEqual(self.m.x_axis.measured_pixel_size, 1.040, delta=0.01) + + def test_y_measured_pixel_size(self): + self.assertAlmostEqual(self.m.y_axis.measured_pixel_size, 1.043, delta=0.01) + + def test_x_measured_difference(self): + self.assertAlmostEqual(self.m.x_axis.pixel_size_difference, 1.065, delta=0.02) + + def test_y_measured_difference(self): + self.assertAlmostEqual(self.m.y_axis.pixel_size_difference, 0.6899, delta=0.03) + + +class TestQuadrantResolution(TestCase): + def setUp(self) -> None: + p = get_file_from_cloud_test_repo([TEST_DIR, "QuadrantBar.dcm"]) + self.m = QuadrantResolution(p) + self.m.analyze(bar_widths=(4.23, 3.18, 2.54, 2.12)) + + def test_wrong_bar_lengths(self): + with self.assertRaises(ValueError): + self.m.analyze(bar_widths=(4.23, 3.18, 2.54)) + + def test_mtfs(self): + known_mtfs = (0.64, 0.462, 0.30, 0.202) # from NMQC + for known_mtf, (lpmm, mtf) in zip(known_mtfs, self.m.mtf.mtfs.items()): + self.assertAlmostEqual(mtf, known_mtf, delta=0.01) + + def test_fwhms(self): + known_fwhms = (2.995, 2.962, 2.954, 2.843) + for known_fwhm, (lpmm, fwhm) in zip(known_fwhms, self.m.mtf.fwhms.items()): + self.assertAlmostEqual(fwhm, known_fwhm, delta=0.05) + + def test_results_data(self): + data = self.m.results_data(as_dict=True) + self.assertIsInstance(data, dict) + data = self.m.results_data() + self.assertIsInstance(data, QuadrantResolutionResults) + self.assertEqual(len(data.quadrants), 4) + self.assertEqual(data.quadrants["1"]["spacing"], 4.23) + self.assertAlmostEqual(data.quadrants["1"]["lpmm"], 0.118, delta=0.001) + + def test_plot(self): + # shouldn't raise + self.m.plot() + self.m.plot(show=False) + + +class TestTomographicUniformity(TestCase): + @classmethod + def setUp(cls) -> None: + p = get_file_from_cloud_test_repo([TEST_DIR, "Jaszczak.dcm"]) + cls.m = TomographicUniformity(p) + + def test_results(self): + self.m.analyze() + results = self.m.results() + self.assertIsInstance(results, str) + + def test_plot(self): + self.m.analyze() + # shouldn't raise + self.m.plot() + self.m.plot(show=False) + + def test_results_data(self): + self.m.analyze() + data = self.m.results_data(as_dict=True) + self.assertIsInstance(data, dict) + data = self.m.results_data() + self.assertIsInstance(data, TomographicUniformityResults) + + def test_first_frame_lower_than_0_raises(self): + with self.assertRaises(ValueError): + self.m.analyze(first_frame=-3) + + def test_last_frame_rolls_over_to_positive(self): + # 45 frames + 1-index based - 3 = 43 + self.m.analyze(last_frame=-3) + self.assertEqual(self.m.last_frame, 43) + + def test_last_frame_outside_bounds(self): + with self.assertRaises(ValueError): + self.m.analyze(last_frame=100) + + def test_last_frame_less_than_first_frame(self): + with self.assertRaises(ValueError): + self.m.analyze(first_frame=10, last_frame=5) + + def test_center_ratio(self): + self.m.analyze() + self.assertAlmostEqual( + self.m.center_ratio, 1.004, delta=0.005 + ) # value from NMQC + + def test_cfov_integral_uniformity(self): + self.m.analyze() + r = self.m.results_data() + self.assertAlmostEqual(r.cfov_integral_uniformity, 13.031, delta=0.3) + + def test_cfov_differential_uniformity(self): + self.m.analyze() + r = self.m.results_data() + self.assertAlmostEqual(r.cfov_differential_uniformity, 9.605, delta=0.2) + + +class TestTomographicContrast(TestCase): + @classmethod + def setUpClass(cls) -> None: + p = get_file_from_cloud_test_repo([TEST_DIR, "Jaszczak.dcm"]) + cls.m = TomographicContrast(p) + cls.m.analyze() + + def test_results(self): + results = self.m.results() + self.assertIsInstance(results, str) + self.assertIn("Contrast", results) + + def test_results_data(self): + data = self.m.results_data() + self.assertIsInstance(data, TomographicContrastResults) + self.assertAlmostEqual( + data.uniformity_baseline, 1331, delta=12 + ) # value from NMQC + self.assertAlmostEqual(data.spheres["1"]["max_contrast"], 72.1, delta=0.2) + self.assertAlmostEqual(data.spheres["1"]["mean_contrast"], 34.2, delta=1.7) + self.assertAlmostEqual(data.spheres["6"]["max_contrast"], 18.3, delta=0.4) + self.assertAlmostEqual( + data.spheres["6"]["mean_contrast"], 13.65, delta=4 + ) # the notable exception in difference + + def plot(self): + # shouldn't raise + self.m.plot() + self.m.plot(show=False) + + def test_different_diameter_vs_angle_lengths_error(self): + with self.assertRaises(ValueError): + self.m.analyze(sphere_diameters_mm=(10, 13), sphere_angles=(0, 90, 180)) From 9e0af159549500e69c576bcbf5ba9f58d5ff3134 Mon Sep 17 00:00:00 2001 From: James Kerns Date: Thu, 4 Jan 2024 09:45:40 -0600 Subject: [PATCH 2/5] fix docs --- .gitignore | 2 ++ docs/source/nuclear.rst | 3 ++- docs/source/topics/mtf.rst | 1 - 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 3a8c5ce2f..842938e2d 100644 --- a/.gitignore +++ b/.gitignore @@ -242,3 +242,5 @@ $RECYCLE.BIN/ # Windows shortcuts *.lnk + +tests_basic/test_files diff --git a/docs/source/nuclear.rst b/docs/source/nuclear.rst index b19bd5977..cfa91a51e 100644 --- a/docs/source/nuclear.rst +++ b/docs/source/nuclear.rst @@ -1,4 +1,5 @@ -.. nuclear:: + +.. _nuclear: ======= Nuclear diff --git a/docs/source/topics/mtf.rst b/docs/source/topics/mtf.rst index 92c3e6cc5..0e70ce0f9 100644 --- a/docs/source/topics/mtf.rst +++ b/docs/source/topics/mtf.rst @@ -30,7 +30,6 @@ The MTF can also be calculated using the moments of the line pair spread functio This algorithm is based on the work of Hander et al [1]_. Specifically, equation 8: .. math:: - :label: mtf MTF = \frac{\sqrt{2 * (\sigma^{2} - \mu)}}{\mu} From 8b830e56847a853fc48cadbb24c5cf0888eb6232 Mon Sep 17 00:00:00 2001 From: James Kerns Date: Thu, 4 Jan 2024 09:54:51 -0600 Subject: [PATCH 3/5] add api classes --- docs/source/nuclear.rst | 47 +++++++++++++++++++++++++++++++++++++++-- 1 file changed, 45 insertions(+), 2 deletions(-) diff --git a/docs/source/nuclear.rst b/docs/source/nuclear.rst index cfa91a51e..f870cde58 100644 --- a/docs/source/nuclear.rst +++ b/docs/source/nuclear.rst @@ -507,8 +507,6 @@ From this initial sphere frame, each ROI is found by: final sphere locations are determined upon. - - References ---------- @@ -533,3 +531,48 @@ API .. autoclass:: pylinac.nuclear.PlanarUniformityResults :members: + +.. autoclass:: pylinac.nuclear.CenterOfRotation + :members: + +.. autoclass:: pylinac.nuclear.CenterOfRotationResults + :members: + +.. autoclass:: pylinac.nuclear.TomographicResolution + :members: + +.. autoclass:: pylinac.nuclear.TomographicResolutionResults + :members: + +.. autoclass:: pylinac.nuclear.SimpleSensitivity + :members: + +.. autoclass:: pylinac.nuclear.SimpleSensitivityResults + :members: + +.. autoclass:: pylinac.nuclear.FourBarResolution + :members: + +.. autoclass:: pylinac.nuclear.FourBarResolutionResults + :members: + +.. autoclass:: pylinac.nuclear.QuadrantResolution + :members: + +.. autoclass:: pylinac.nuclear.QuadrantResolutionResults + :members: + +.. autoclass:: pylinac.nuclear.TomographicUniformity + :members: + +.. autoclass:: pylinac.nuclear.TomographicUniformityResults + :members: + +.. autoclass:: pylinac.nuclear.TomographicContrast + :members: + +.. autoclass:: pylinac.nuclear.TomographicContrastResults + :members: + +.. autoclass:: pylinac.nuclear.Nuclide + :members: From ac4e0cbbb408cc451d86cb393f15c88630fab95c Mon Sep 17 00:00:00 2001 From: James Kerns Date: Thu, 4 Jan 2024 14:27:45 -0600 Subject: [PATCH 4/5] fix docstring --- pylinac/nuclear.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pylinac/nuclear.py b/pylinac/nuclear.py index b749b444b..9fc2438cc 100644 --- a/pylinac/nuclear.py +++ b/pylinac/nuclear.py @@ -1522,7 +1522,7 @@ def analyze( sphere_diameters_mm : list The diameters of the spheres in mm. sphere_angles : list - + The angles of the spheres in degrees. """ self.ufov_ratio = ufov_ratio uniformities = self.slice_data From af096de54b886d1b8607424d2240fe01406b1bb5 Mon Sep 17 00:00:00 2001 From: James Kerns Date: Tue, 9 Jan 2024 10:46:15 -0600 Subject: [PATCH 5/5] fix docs per PR comments --- docs/source/nuclear.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/nuclear.rst b/docs/source/nuclear.rst index f870cde58..045960090 100644 --- a/docs/source/nuclear.rst +++ b/docs/source/nuclear.rst @@ -131,10 +131,10 @@ Each step listed here is applied to each frame of the image: If so, the mean depends on the total FOV. We interpret this to mean a value of 75% of the mean of all pixels **that are non-zero**. - In practice, this won't make a big difference this this only affects edge pixels. + In practice, this won't make a big difference since this only affects edge pixels. #. Stray pixels are removed from the image. A stray pixel is defined as a pixel that is surrounded - by a pixel values of 0. The stray pixels are set to 0. + ony by pixels of value 0. The stray pixels are set to 0. #. The UFOV is determined. The size of the total FOV is found in each dimension (x, y). The UFOV ratio is then multiplied by the total FOV size. This value is then used to "erode" the edge pixel values of the image into a smaller sub-image.