From 67f31a8d69cb80c23d935dcee962857d7c3eff08 Mon Sep 17 00:00:00 2001 From: Jason Kai Date: Fri, 2 Apr 2021 09:27:20 -0400 Subject: [PATCH 1/7] Clear notebook output, fix image links --- .../diffusion_tensor_imaging.ipynb | 8 +- .../diffusion_tensor_imaging_solutions.ipynb | 126 ++---------------- 2 files changed, 18 insertions(+), 116 deletions(-) diff --git a/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb b/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb index e27dc6df..1e11debb 100644 --- a/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb +++ b/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb @@ -23,7 +23,7 @@ "\n", "Tensors are represented by ellipsoids characterized by calculated eigenvalues ($\\lambda_1, \\lambda_2, \\lambda_3$) and eigenvectors ($\\epsilon_1, \\epsilon_2, \\epsilon_3$) from the previously described matrix. Eigenvalues and eigenvectors are normally sorted in descending magnitude.\n", "\n", - "![Diffusion Tensor](DiffusionTensor.png)
\n", + "![Diffusion Tensor](../../fig/diffusion_tensor_imaging/DiffusionTensor.png)
\n", "Adapated from Jellison _et al._, 2004\n", "\n", "In the following example, we show how to model your diffusion datasets. It should be noted that there are a number of diffusion models and many of these are implemented in `Dipy`. However, for the purposes of this tutorial, we will be focus on the tensor model.\n", @@ -436,7 +436,7 @@ "source": [ "Another way of viewing the tensors is to visualize the diffusion tensor in each imaging voxel with colour encoding (we will refer you to the [`Dipy` documentation](https://dipy.org/tutorials/) for the steps to perform this type of visualization as it can be memory intensive). Below is an example image of such tensor visualization.\n", "\n", - "![Tensor Visualization](TensorViz.png)" + "![Tensor Visualization](../../fig/diffusion_tensor_imaging/TensorViz.png)" ] }, { @@ -447,7 +447,7 @@ "\n", "DTI is only one of many models and is one of the simplest models available for modelling diffusion. While it is used for many studies, there are also some drawbacks (eg. ability to distinguish multiple fibre orientations in one imaging voxel). Some examples can be seen below! \n", "\n", - "![fiber_configurations](FiberConfigurations.png)\n", + "![fiber_configurations](../../fig/diffusion_tensor_imaging/FiberConfigurations.png)\n", "\n", "Sourced from: Sotiropolous and Zalewsky. (2017). Building connectomes using diffusion MRI: why, how, and but. NMR in Biomedicine. 4(32). e3752. 10.1002/nbm.3752. \n", "\n", @@ -476,4 +476,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb b/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb index f29461cb..5ca68bde 100644 --- a/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb +++ b/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb @@ -23,7 +23,7 @@ "\n", "Tensors are represented by ellipsoids characterized by calculated eigenvalues ($\\lambda_1, \\lambda_2, \\lambda_3$) and eigenvectors ($\\epsilon_1, \\epsilon_2, \\epsilon_3$) from the previously described matrix. The computed eigenvalues and eigenvectors are normally sorted in descending magnitude (i.e. $\\lambda_1 \\ge \\lambda_2$ ). Eigenvalues are always strictly positive in the context of dMRI and are measured in mm^2/s. In the DTI model, the largest eigenvalue gives the principal direction of the diffusion tensor, and the other two eigenvectors span the orthogonal plane to the former direction.\n", "\n", - "![Diffusion Tensor](images/DiffusionTensor.png)
\n", + "![Diffusion Tensor](../../../fig/diffusion_tensor_imaging/DiffusionTensor.png)
\n", "Adapated from Jellison _et al._, 2004\n", "\n", "In the following example, we show how to model your diffusion datasets. It should be noted that there are a number of diffusion models and many of these are implemented in `DIPY`. However, for the purposes of this tutorial, we will be focus on the tensor model.\n", @@ -62,17 +62,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/home/ROBARTS/tkai/khan/users/tkai/projects/SDC-BIDS-dMRI/.dipyvenv/lib/python3.7/site-packages/bids/layout/models.py:152: FutureWarning: The 'extension' entity currently excludes the leading dot ('.'). As of version 0.14.0, it will include the leading dot. To suppress this warning and include the leading dot, use `bids.config.set_option('extension_initial_dot', True)`.\n FutureWarning)\n" - ] - } - ], + "outputs": [], "source": [ "from bids.layout import BIDSLayout\n", "from dipy.io.gradients import read_bvals_bvecs\n", @@ -107,17 +99,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/home/ROBARTS/tkai/khan/users/tkai/projects/SDC-BIDS-dMRI/.dipyvenv/lib/python3.7/site-packages/ipykernel_launcher.py:4: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).\n\n* deprecated from version: 3.0\n* Will raise as of version: 5.0\n after removing the cwd from sys.path.\n" - ] - } - ], + "outputs": [], "source": [ "import dipy.reconst.dti as dti\n", "from dipy.segment.mask import median_otsu\n", @@ -153,29 +137,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 23 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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\n" - }, - "metadata": {} - } - ], + "outputs": [], "source": [ "%matplotlib inline \n", "\n", @@ -209,29 +173,9 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 43 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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\n" - }, - "metadata": {} - } - ], + "outputs": [], "source": [ "%matplotlib inline \n", "\n", @@ -252,30 +196,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "%matplotlib inline \n", "\n", @@ -391,4 +293,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file From 68ef3d147a820c35cfd62064ab6896af13924d52 Mon Sep 17 00:00:00 2001 From: Jason Kai Date: Mon, 5 Apr 2021 17:07:24 -0400 Subject: [PATCH 2/7] fix minor spaces in markdown --- _episodes/diffusion_tensor_imaging.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_episodes/diffusion_tensor_imaging.md b/_episodes/diffusion_tensor_imaging.md index e300269d..14118223 100644 --- a/_episodes/diffusion_tensor_imaging.md +++ b/_episodes/diffusion_tensor_imaging.md @@ -56,7 +56,7 @@ The reconst module contains implementations of the following models The different algorithms implemented in the module all share a similar conceptual structure: -* ReconstModel objects (e.g., TensorModel) carry the parameters that are required in order to fit a model. For example, the directions and magnitudes of the gradients that were applied in the experiment. The objects all have a fit method, which takes in data, and emites a ReconstFit object. This is where a lot of the heavy lifting of the processing will take place. +* ReconstModel objects (e.g. TensorModel) carry the parameters that are required in order to fit a model. For example, the directions and magnitudes of the gradients that were applied in the experiment. The objects all have a fit method, which takes in data, and emites a ReconstFit object. This is where a lot of the heavy lifting of the processing will take place. * ReconstFit objects carry the model that was used to generate the object. They also include the parameters that were estimated during fitting of the data. They have methods to calculate derived statistics, which can differ from model to model. All objects also have an orientation distribution function (odf), and most (but not all) contain a predict method, which enables the prediction of another dataset based on the current gradient table. @@ -115,7 +115,7 @@ Mathematically, FA is defined as the normalized variance of the eigenvalues of t ![FA equation]({{ relative_root_path }}/fig/diffusion_tensor_imaging/fa_eqn.png){:class="img-responsive"} -Values of FA vary between 0 and 1 (unitless). In the cases of perfect, isotropic diffusion, ![Isotropic diffusion eigenvalues]({{ relative_root_path }}/fig/diffusion_tensor_imaging/fa_iso.png), the diffusion tensor is a sphere and FA = 0. If the first two eigenvalues are equal the tensor will be oblate or planar, whereas if the first eigenvalue is larger than the other two, it will have the mentioned ellipsoid shape: as diffusion progressively becomes more anisotropic, eigenvalues become more unequal, causing the tensor to be elongated, with FA approaching 1. Note that FA should be interpreted carefully. It may be an indication of the density of packing fibers in a voxel and the amount of myelin wrapped around those axons, but it is not always a measure of "tissue integrity". +Values of FA vary between 0 and 1 (unitless). In the cases of perfect, isotropic diffusion, ![Isotropic diffusion eigenvalues]({{ relative_root_path }}/fig/diffusion_tensor_imaging/fa_iso.png), the diffusion tensor is a sphere and FA = 0. If the first two eigenvalues are equal the tensor will be oblate or planar, whereas if the first eigenvalue is larger than the other two, it will have the mentioned ellipsoid shape: as diffusion progressively becomes more anisotropic, eigenvalues become more unequal, causing the tensor to be elongated, with FA approaching 1. Note that FA should be interpreted carefully. It may be an indication of the density of packing fibers in a voxel and the amount of myelin wrapped around those axons, but it is not always a measure of "tissue integrity". Let's take a look at what the FA map looks like! An FA map is a gray-scale image, where higher intensities reflect more anisotropic diffuse regions. From 7103bc485a8cd11525a154abe1a82cc901b5cbff Mon Sep 17 00:00:00 2001 From: Jason Kai Date: Mon, 5 Apr 2021 17:14:55 -0400 Subject: [PATCH 3/7] Fix columns per line --- _episodes/diffusion_tensor_imaging.md | 145 +++++++++++++++++++++----- 1 file changed, 118 insertions(+), 27 deletions(-) diff --git a/_episodes/diffusion_tensor_imaging.md b/_episodes/diffusion_tensor_imaging.md index 14118223..e3984569 100644 --- a/_episodes/diffusion_tensor_imaging.md +++ b/_episodes/diffusion_tensor_imaging.md @@ -17,25 +17,53 @@ keypoints: ## Diffusion Tensor Imaging (DTI) -Diffusion tensor imaging or "DTI" refers to images describing diffusion with a tensor model. DTI is derived from preprocessed diffusion weighted imaging (DWI) data. First proposed by Basser and colleagues ([Basser, 1994](https://www.ncbi.nlm.nih.gov/pubmed/8130344)), the diffusion tensor model describes diffusion characteristics within an imaging voxel. This model has been very influential in demonstrating the utility of the diffusion MRI in characterizing the microstructure of white matter and the biophysical properties (inferred from local diffusion properties). The DTI model is still a commonly used model to investigate white matter. +Diffusion tensor imaging or "DTI" refers to images describing diffusion with a tensor model. +DTI is derived from preprocessed diffusion weighted imaging (DWI) data. First proposed by Basser +and colleagues ([Basser, 1994](https://www.ncbi.nlm.nih.gov/pubmed/8130344)), the diffusion tensor +model describes diffusion characteristics within an imaging voxel. This model has been very +influential in demonstrating the utility of the diffusion MRI in characterizing the microstructure +of white matter and the biophysical properties (inferred from local diffusion properties). +The DTI model is still a commonly used model to investigate white matter. The tensor models the diffusion signal mathematically as: ![Diffusion signal equation]({{ relative_root_path }}/fig/diffusion_tensor_imaging/diffusion_eqn.png){:class="img-responsive"} -Where ![Diffusion unit vector]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_unitvector.png) is a unit vector in 3D space indicating the direction of measurement and b are the parameters of the measurement, such as the strength and duration of diffusion-weighting gradient. ![Diffusion signal]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_diffusionsignal.png) is the diffusion-weighted signal measured and ![Non-weighted diffusion signal]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_nondiffsignal.png) is the signal conducted in a measurement with no diffusion weighting. ![Diffusivity]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_diffusionmatrix.png) is a positive-definite quadratic form, which contains six free parameters to be fit. These six parameters are: +Where ![Diffusion unit vector]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_unitvector.png) +is a unit vector in 3D space indicating the direction of measurement and b are the parameters of +the measurement, such as the strength and duration of diffusion-weighting gradient. +![Diffusion signal]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_diffusionsignal.png) +is the diffusion-weighted signal measured and +![Non-weighted diffusion signal]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_nondiffsignal.png) +is the signal conducted in a measurement with no diffusion weighting. +![Diffusivity]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_diffusionmatrix.png) +is a positive-definite quadratic form, which contains six free parameters to be fit. +These six parameters are: ![Diffusivity matrix]({{ relative_root_path }}/fig/diffusion_tensor_imaging/diffusion_matrix.png){:class="img-responsive"} -The diffusion matrix is a variance-covariance matrix of the diffusivity along the three spatial dimensions. Note that we can assume that the diffusivity has antipodal symmetry, so elements across the diagonal of the matrix are equal. For example: ![Symmetry in the diffusivity matrix]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_diagelements.png). This is why there are only 6 free parameters to estimate here. - -Tensors are represented by ellipsoids characterized by calculated eigenvalues (![Diffusivity matrix eigenvalues]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_eigval.png)) and eigenvectors (![Diffusivity matrix eigenvectors]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_eigvec.png)) from the previously described matrix. The computed eigenvalues and eigenvectors are normally sorted in descending magnitude (i.e. ![Diffusivity matrix eigenvalues magnitudes]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_sortedeigvec.png)). Eigenvalues are always strictly positive in the context of dMRI and are measured in mm^2/s. In the DTI model, the largest eigenvalue gives the principal direction of the diffusion tensor, and the other two eigenvectors span the orthogonal plane to the former direction. +The diffusion matrix is a variance-covariance matrix of the diffusivity along the three spatial +dimensions. Note that we can assume that the diffusivity has antipodal symmetry, so elements +across the diagonal of the matrix are equal. For example: +![Symmetry in the diffusivity matrix]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_diagelements.png). +This is why there are only 6 free parameters to estimate here. + +Tensors are represented by ellipsoids characterized by calculated eigenvalues +(![Diffusivity matrix eigenvalues]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_eigval.png)) +and eigenvectors (![Diffusivity matrix eigenvectors]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_eigvec.png)) +from the previously described matrix. +The computed eigenvalues and eigenvectors are normally sorted in descending magnitude (i.e. +![Diffusivity matrix eigenvalues magnitudes]({{ relative_root_path }}/fig/diffusion_tensor_imaging/inline_sortedeigvec.png)). +Eigenvalues are always strictly positive in the context of dMRI and are measured in mm^2/s. +In the DTI model, the largest eigenvalue gives the principal direction of the diffusion tensor, +and the other two eigenvectors span the orthogonal plane to the former direction. ![Diffusion tensor]({{ relative_root_path }}/fig/diffusion_tensor_imaging/DiffusionTensor.png){:class="img-responsive"} _Adapted from Jelison et al., 2004_ -In the following example, we will walk through how to model a diffusion dataset. While there are a number of diffusion models, many of which are implemented in DIPY. However, for the purposes of this lesson, we will focus on the tensor model described above. - +In the following example, we will walk through how to model a diffusion dataset. While there +are a number of diffusion models, many of which are implemented in DIPY. +However, for the purposes of this lesson, we will focus on the tensor model described above. ### Reconstruction with the `dipy.reconst` module @@ -56,9 +84,17 @@ The reconst module contains implementations of the following models The different algorithms implemented in the module all share a similar conceptual structure: -* ReconstModel objects (e.g. TensorModel) carry the parameters that are required in order to fit a model. For example, the directions and magnitudes of the gradients that were applied in the experiment. The objects all have a fit method, which takes in data, and emites a ReconstFit object. This is where a lot of the heavy lifting of the processing will take place. -* ReconstFit objects carry the model that was used to generate the object. They also include the parameters that were estimated during fitting of the data. They have methods to calculate derived statistics, which can differ from model to model. All objects also have an orientation distribution function (odf), and most (but not all) contain a predict method, which enables the prediction of another dataset based on the current gradient table. - +* ReconstModel objects (e.g. TensorModel) carry the parameters that +are required in order to fit a model. For example, the directions and magnitudes of the gradients +that were applied in the experiment. The objects all have a fit method, which takes +in data, and emites a ReconstFit object. This is where a lot of the heavy lifting +of the processing will take place. +* ReconstFit objects carry the model that was used to generate the object. +They also include the parameters that were estimated during fitting of the data. They have +methods to calculate derived statistics, which can differ from model to model. All objects also +have an orientation distribution function (odf), and most (but not all) contain +a predict method, which enables the prediction of another dataset based on the +current gradient table. ### Reconstruction with the DTI Model @@ -90,7 +126,10 @@ gtab = gradient_table(gt_bvals, gt_bvecs) ~~~ {: .language-python} -Next, we will need to create the tensor model using our gradient table, and then fit the model using our data! We start by creating a mask from our data. We then apply this mask to avoid calculating the tensors in the background of the image! This can be done using DIPY's mask module. Then we will fit out data! +Next, we will need to create the tensor model using our gradient table, and then fit the model +using our data! We start by creating a mask from our data. We then apply this mask to avoid +calculating the tensors in the background of the image! This can be done using DIPY's +mask module. Then we will fit out data! ~~~ import dipy.reconst.dti as dti @@ -104,22 +143,38 @@ dti_fit = dti_model.fit(dwi_data, mask=dwi_mask) ~~~ {: .language-python} -The fit method creates a TensorFit object which contains the fitting parameters and other attributes of the model. A number of quantitative scalar metrics can be derived from the eigenvalues! In this tutorial, we will cover fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. Each of these scalar, rotationally invariant metrics were calculated in the previous fitting step! +The fit method creates a TensorFit object which contains the fitting parameters and +other attributes of the model. A number of quantitative scalar metrics can be derived from +the eigenvalues! In this tutorial, we will cover fractional anisotropy, mean diffusivity, +axial diffusivity, and radial diffusivity. Each of these scalar, rotationally invariant metrics +were calculated in the previous fitting step! ### Fractional anisotropy (FA) -Fractional anisotropy (FA) characterizes the degree to which the distribution of diffusion in an imaging voxel is directional. That is, whether there is relatively unrestricted diffusion in a particular direction. +Fractional anisotropy (FA) characterizes the degree to which the distribution of diffusion in an +imaging voxel is directional. That is, whether there is relatively unrestricted diffusion in +a particular direction. Mathematically, FA is defined as the normalized variance of the eigenvalues of the tensor: ![FA equation]({{ relative_root_path }}/fig/diffusion_tensor_imaging/fa_eqn.png){:class="img-responsive"} -Values of FA vary between 0 and 1 (unitless). In the cases of perfect, isotropic diffusion, ![Isotropic diffusion eigenvalues]({{ relative_root_path }}/fig/diffusion_tensor_imaging/fa_iso.png), the diffusion tensor is a sphere and FA = 0. If the first two eigenvalues are equal the tensor will be oblate or planar, whereas if the first eigenvalue is larger than the other two, it will have the mentioned ellipsoid shape: as diffusion progressively becomes more anisotropic, eigenvalues become more unequal, causing the tensor to be elongated, with FA approaching 1. Note that FA should be interpreted carefully. It may be an indication of the density of packing fibers in a voxel and the amount of myelin wrapped around those axons, but it is not always a measure of "tissue integrity". +Values of FA vary between 0 and 1 (unitless). In the cases of perfect, isotropic diffusion, +![Isotropic diffusion eigenvalues]({{ relative_root_path }}/fig/diffusion_tensor_imaging/fa_iso.png), +the diffusion tensor is a sphere and FA = 0. If the first two eigenvalues are equal the tensor +will be oblate or planar, whereas if the first eigenvalue is larger than the other two, it will +have the mentioned ellipsoid shape: as diffusion progressively becomes more anisotropic, +eigenvalues become more unequal, causing the tensor to be elongated, with FA approaching 1. +Note that FA should be interpreted carefully. It may be an indication of the density of packing +fibers in a voxel and the amount of myelin wrapped around those axons, but it is not always a +measure of "tissue integrity". -Let's take a look at what the FA map looks like! An FA map is a gray-scale image, where higher intensities reflect more anisotropic diffuse regions. +Let's take a look at what the FA map looks like! An FA map is a gray-scale image, where higher +intensities reflect more anisotropic diffuse regions. -_Note: we will have to first create the image from the array, making use of the reference anatomical_ +_Note: we will have to first create the image from the array, making use of the reference +anatomical_ ~~~ import matplotlib.pyplot as plt # To enable plotting within notebook @@ -132,17 +187,30 @@ plot.plot_anat(fa_img) ![FA plot]({{ relative_root_path }}/fig/diffusion_tensor_imaging/plot_fa.png){:class="img-responsive"} -Derived from partial volume effects in imaging voxels due to the presence of different tissues, noise in the measurements and numerical errors, the DTI model estimation may yield negative eigenvalues. Such *degenerate* case is not physically meaningful. These values are usually revealed as black or 0-valued pixels in FA maps. +Derived from partial volume effects in imaging voxels due to the presence of different tissues, +noise in the measurements and numerical errors, the DTI model estimation may yield negative +eigenvalues. Such *degenerate* case is not physically meaningful. These values are usually +revealed as black or 0-valued pixels in FA maps. -FA is a central value in dMRI: large FA values imply that the underlying fiber populations have a very coherent orientation, whereas lower FA values point to voxels containing multiple fiber crossings. Lowest FA values are indicative of non-white matter tissue in healthy brains (see, for example, Alexander et al.'s "Diffusion Tensor Imaging of the Brain". Neurotherapeutics 4, 316-329 (2007), and Jeurissen et al.'s "Investigating the Prevalence of Complex Fiber Configurations in White Matter Tissue with Diffusion Magnetic Resonance Imaging". Hum. Brain Mapp. 2012, 34(11) pp. 2747-2766). +FA is a central value in dMRI: large FA values imply that the underlying fiber populations +have a very coherent orientation, whereas lower FA values point to voxels containing +multiple fiber crossings. Lowest FA values are indicative of non-white matter tissue in +healthy brains (see, for example, Alexander et al.'s "Diffusion Tensor Imaging of the Brain". +Neurotherapeutics 4, 316-329 (2007), and Jeurissen et al.'s "Investigating the Prevalence of +Complex Fiber Configurations in White Matter Tissue with Diffusion Magnetic Resonance +maging". Hum. Brain Mapp. 2012, 34(11) pp. 2747-2766). ### Mean diffusivity (MD) -An often used complimentary measure to FA is mean diffusivity (MD). MD is a measure of the degree of diffusion, independent of direction. This is sometimes known as the apparent diffusion coefficient (ADC). Mathematically, MD is computed as the mean eigenvalues of the tensor and is measured in mm^2/s. +An often used complimentary measure to FA is mean diffusivity (MD). MD is a measure of the +degree of diffusion, independent of direction. This is sometimes known as the apparent diffusion +coefficient (ADC). Mathematically, MD is computed as the mean eigenvalues of the tensor and +is measured in mm^2/s. ![MD equation]({{ relative_root_path }}/fig/diffusion_tensor_imaging/md_eqn.png){:class="img-responsive"} -Similar to the previous FA image, let's take a look at what the MD map looks like. Again, higher intensities reflect higher mean diffusivity! +Similar to the previous FA image, let's take a look at what the MD map looks like. Again, higher +intensities reflect higher mean diffusivity! ~~~ md_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.md) @@ -156,20 +224,34 @@ plot.plot_anat(md_img, cut_coords=(0, -29, 20), vmin=0, vmax=0.01) ### Axial and radial diffusivity (AD & RD) -The final two metrics we will discuss are axial diffusivity (AD) and radial diffusivity (RD). Two tensors with different shapes may yield the same FA values, and additional measures such as AD and RD are required to further characterize the tensor. AD describes the diffusion rate along the primary axis of diffusion, along ![Axial diffusivity eigenvalue]({{ relative_root_path }}/fig/diffusion_tensor_imaging/primary_diffusion.png), or parallel to the axon (and hence, some works refer to it as the *parallel diffusivity*). On the other hand, RD reflects the average diffusivity along the other two minor axes (being named as *perpendicular diffusivity* in some works) (![Radial diffusivity eigenvalues]({{ relative_root_path }}/fig/diffusion_tensor_imaging/minor_axes.png)). Both are measured in mm^2/s. +The final two metrics we will discuss are axial diffusivity (AD) and radial diffusivity (RD). +Two tensors with different shapes may yield the same FA values, and additional measures such as +AD and RD are required to further characterize the tensor. AD describes the diffusion rate +along the primary axis of diffusion, along +![Axial diffusivity eigenvalue]({{ relative_root_path }}/fig/diffusion_tensor_imaging/primary_diffusion.png) +, or parallel to the axon (and hence, some works refer to it as the *parallel diffusivity*). +On the other hand, RD reflects the average diffusivity along the other two minor axes +(being named as *perpendicular diffusivity* in some works) +(![Radial diffusivity eigenvalues]({{ relative_root_path }}/fig/diffusion_tensor_imaging/minor_axes.png)) +. Both are measured in mm^2/s. ![Axial and radial diffusivities]({{ relative_root_path }}/fig/diffusion_tensor_imaging/ax_rad_diff.png){:class="img-responsive"} ### Tensor visualizations -There are several ways of visualizing tensors. One way is using an RGB map, which overlays the primary diffusion orientation on an FA map. The colours of this map encodes the diffusion orientation. Note that this map provides no directional information (e.g. whether the diffusion flows from right-to-left or vice-versa). To do this with DIPY, we can use the color_fa function. The colours map to the following orientations: +There are several ways of visualizing tensors. One way is using an RGB map, which overlays the +primary diffusion orientation on an FA map. The colours of this map encodes the diffusion +orientation. Note that this map provides no directional information (e.g. whether the diffusion +flows from right-to-left or vice-versa). To do this with DIPY, we can use the +color_fa function. The colours map to the following orientations: * Red = Left / Right * Green = Anterior / Posterior * Blue = Superior / Inferior -_Note: The plotting functions in nilearn are unable to visualize these RGB maps. However, we can use the matplotlib library to view these images._ +_Note: The plotting functions in nilearn are unable to visualize these RGB maps. +However, we can use the matplotlib library to view these images._ ~~~ @@ -187,20 +269,29 @@ ax[2].imshow(ndimage.rotate(RGB_map[:, :, RGB_map.shape[2]//2, :], 90, reshape=F ![RGB FA map]({{ relative_root_path }}/fig/diffusion_tensor_imaging/plot_fa_rgb.png){:class="img-responsive"} -Another way of visualizing the tensors is to display the diffusion tensor in each imaging voxel with colour encoding (Please refer to the [DIPY documentation](https://dipy.org/tutorials/) for the necessary steps to perform this type of visualization, as it can be memory intensive). Below is an example of one such tensor visualization. +Another way of visualizing the tensors is to display the diffusion tensor in each imaging voxel +with colour encoding +(Please refer to the [DIPY documentation](https://dipy.org/tutorials/) for +the necessary steps to perform this type of visualization, as it can be memory intensive). +Below is an example of one such tensor visualization. ![Tensor visualization]({{ relative_root_path }}/fig/diffusion_tensor_imaging/TensorViz.png){:class="img-responsive"} ### Some notes on DTI -DTI is only one of many models and is one of the simplest models available for modelling diffusion. While it is used for many studies, there are also some drawbacks (e.g. ability to distinguish multiple fibre orientations in an imaging voxel). Examples of this can be seen below! +DTI is only one of many models and is one of the simplest models available for modelling +diffusion. While it is used for many studies, there are also some drawbacks (e.g. ability to +distinguish multiple fibre orientations in an imaging voxel). Examples of this can be seen below! ![DTI drawbacks]({{ relative_root_path }}/fig/diffusion_tensor_imaging/FiberConfigurations.png){:class="img-responsive"} -_Sourced from Sotiropoulos and Zalesky (2017). Building connectomes using diffusion MRI: why, how, and but. NMR in Biomedicine. 4(32). e3752. doi:10.1002/nbm.3752._ +_Sourced from Sotiropoulos and Zalesky (2017). Building connectomes using diffusion MRI: +why, how, and but. NMR in Biomedicine. 4(32). e3752. doi:10.1002/nbm.3752._ -Though other models are outside the scope of this lesson, we recommend looking into some of the pros and cons of each model (listed previously) to choose one best suited for your data! +Though other models are outside the scope of this lesson, we recommend looking into some +of the pros and cons of each model (listed previously) to choose one best suited for your +data! > ## Exercise 1 > From 82fd1243be9f5d2780fcd40a57f0b8b98ae92d05 Mon Sep 17 00:00:00 2001 From: Jason Kai Date: Mon, 5 Apr 2021 17:40:21 -0400 Subject: [PATCH 4/7] run autopep8 through notebook --- .../diffusion_tensor_imaging_solutions.ipynb | 342 +++++++++++++----- 1 file changed, 248 insertions(+), 94 deletions(-) diff --git a/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb b/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb index 5ca68bde..c5e6573c 100644 --- a/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb +++ b/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb @@ -6,65 +6,71 @@ "source": [ "## Diffusion Tensor Imaging (DTI)\n", "\n", - "Diffusion tensor imaging or \"DTI\" refers to images describing diffusion as a tensor model and is derived from preprocessed DWI data. First proposed by Basser and colleagues ([Basser, 1994](https://www.ncbi.nlm.nih.gov/pubmed/8130344)), the diffusion tensor model describes the diffusion within a voxel. This model has been very influential in demonstrating the utility of diffusion MRI in characterizing the microstructure of white matter and the biophysical properties (inferred from local diffusion properties). DTI is still a commonly used.\n", + "Diffusion tensor imaging or \"DTI\" refers to images describing diffusion with a tensor model. DTI is derived from preprocessed diffusion weighted imaging (DWI) data. First proposed by Basser and colleagues ([Basser, 1994](https://www.ncbi.nlm.nih.gov/pubmed/8130344)), the diffusion tensor model describes diffusion characteristics within an imaging voxel. This model has been very influential in demonstrating the utility of the diffusion MRI in characterizing the microstructure of white matter and the biophysical properties (inferred from local diffusion properties). The DTI model is still a commonly used model to investigate white matter.\n", "\n", - "The diffusion tensor models the diffusion signal as:\n", + "The tensor models the diffusion signal mathematically as:\n", "\n", - "$\\frac{S(\\mathbf{g}, b)}{S_0} = e^{-b\\mathbf{g}^T \\mathbf{D} \\mathbf{g}}$\n", + "![Diffusion signal equation](../../../fig/diffusion_tensor_imaging/diffusion_eqn.png)\n", "\n", - "Where $\\mathbf{g}$ is a unit vector in 3D space indicating the direction of measurement and b are the parameters of measurement, such as the strength and duration of diffusion-weighting gradient. $S(\\mathbf{g}, b)$ is the diffusion-weighted signal measured and $S_0$ is the signal conducted in a measurement with no diffusion weighting. $\\mathbf{D}$ is a positive-definite quadratic form, which contains six free parameters to be fit. These six parameters are:\n", + "Where ![Diffusion unit vector](../../../fig/diffusion_tensor_imaging/inline_unitvector.png) is a unit vector in 3D space indicating the direction of measurement and b are the parameters of the measurement, such as the strength and duration of diffusion-weighting gradient. ![Diffusion signal](../../../fig/diffusion_tensor_imaging/inline_diffusionsignal.png) is the diffusion-weighted signal measured and ![Non-weighted diffusion signal](../../..//fig/diffusion_tensor_imaging/inline_nondiffsignal.png) is the signal conducted in a measurement with no diffusion weighting. ![Diffusivity](../../../fig/diffusion_tensor_imaging/inline_diffusionmatrix.png) is a positive-definite quadratic form, which contains six free parameters to be fit. These six parameters are:\n", "\n", - "$\\mathbf{D} = \\begin{pmatrix} D_{xx} & D_{xy} & D_{xz} \\\\\n", - " D_{yx} & D_{yy} & D_{yz} \\\\\n", - " D_{zx} & D_{zy} & D_{zz} \\\\ \n", - " \\end{pmatrix}$\n", + "![Diffusivity matrix](../../../fig/diffusion_tensor_imaging/diffusion_matrix.png)\n", "\n", - "This matrix is a variance/covariance matrix of the diffusivity along the three spatial dimensions. Note that we can assume that diffusivity has antipodal symmetry, so elements across the diagonal are equal. For example: $D_{xy} = D_{yx}$. This is why there are only 6 free parameters to estimate here. \n", + "The diffusion matrix is a variance-covariance matrix of the diffusivity along the three spatial dimensions. Note that we can assume that the diffusivity has antipodal symmetry, so elements across the diagonal of the matrix are equal. For example: ![Symmetry in the diffusivity matrix](../../../fig/diffusion_tensor_imaging/inline_diagelements.png). This is why there are only 6 free parameters to estimate here.\n", "\n", - "Tensors are represented by ellipsoids characterized by calculated eigenvalues ($\\lambda_1, \\lambda_2, \\lambda_3$) and eigenvectors ($\\epsilon_1, \\epsilon_2, \\epsilon_3$) from the previously described matrix. The computed eigenvalues and eigenvectors are normally sorted in descending magnitude (i.e. $\\lambda_1 \\ge \\lambda_2$ ). Eigenvalues are always strictly positive in the context of dMRI and are measured in mm^2/s. In the DTI model, the largest eigenvalue gives the principal direction of the diffusion tensor, and the other two eigenvectors span the orthogonal plane to the former direction.\n", + "Tensors are represented by ellipsoids characterized by calculated eigenvalues (![Diffusivity matrix eigenvalues](../../../fig/diffusion_tensor_imaging/inline_eigval.png)) and eigenvectors (![Diffusivity matrix eigenvectors](../../../fig/diffusion_tensor_imaging/inline_eigvec.png)) from the previously described matrix. The computed eigenvalues and eigenvectors are normally sorted in descending magnitude (i.e. ![Diffusivity matrix eigenvalues magnitudes](../../../fig/diffusion_tensor_imaging/inline_sortedeigvec.png)). Eigenvalues are always strictly positive in the context of dMRI and are measured in mm^2/s. In the DTI model, the largest eigenvalue gives the principal direction of the diffusion tensor, and the other two eigenvectors span the orthogonal plane to the former direction.\n", "\n", - "![Diffusion Tensor](../../../fig/diffusion_tensor_imaging/DiffusionTensor.png)
\n", - "Adapated from Jellison _et al._, 2004\n", + "![Diffusion tensor](../../../fig/diffusion_tensor_imaging/DiffusionTensor.png)\n", + "_Adapted from Jelison et al., 2004_\n", "\n", - "In the following example, we show how to model your diffusion datasets. It should be noted that there are a number of diffusion models and many of these are implemented in `DIPY`. However, for the purposes of this tutorial, we will be focus on the tensor model.\n", + "In the following example, we will walk through how to model a diffusion dataset. While there are a number of diffusion models, many of which are implemented in `DIPY`. However, for the purposes of this lesson, we will focus on the tensor model described above.\n", "\n", "### Reconstruction with the `dipy.reconst` module\n", "\n", - "The `reconst` module contains implementations of the following models: \n", - "\n", - "- Tensor (Basser et al., 1994)\n", - "- Constrained Spherical Deconvolution (Tournier et al. 2007)\n", - "- Diffusion Kurtosis (Jensen et al. 2005)\n", - "- DSI (Wedeen et al. 2008)\n", - "- DSI with deconvolution (Canales-Rodriguez et al. 2010)\n", - "- Generalized Q Imaging (Yeh et al. 2010)\n", - "- MAPMRI (Ozarsalan et al. 2013)\n", - "- SHORE (Ozarsalan et al. 2008)\n", - "- CSA (Aganj et al. 2009)\n", - "- Q ball (Descoteaux et al. 2007)\n", - "- OPDT (Tristan-Vega et al. 2010)\n", - "- Sparse Fascicle Model (Rokem et al. 2015)\n", - "\n", - "The different algorithms implemented in the `reconst` module all share a similar conceptual structure: \n", - "\n", - "- `ReconstModel` objects (e.g, `TensorModel`) carry the parameters that are required in order to fit a model. For example, the directions and intensities of the gradients that were applied in the experiment. The all have a `fit` method, which takes in data, and emits a `ReconstFit` object. This is where a lot of the heavy lifting will take place.\n", - "- `ReconstFit` objects carry the model that was used to generate them. They also carry around the parameters that were estimated during fitting of the data. They have methods to calculate derived statistics, such as FA and MD (for the tensor), which can differ from module to module. The also all have an `odf` , and most of them (but not all) have `predict` methods, which allow you to predict another data-set based on the a gradient table." + "The `reconst` module contains implementations of the following models:\n", + "\n", + "* Tensor (Basser et al., 1994)\n", + "* Constrained Spherical Deconvolution (Tournier et al. 2007)\n", + "* Diffusion Kurtosis (Jensen et al. 2005)\n", + "* DSI (Wedeen et al. 2008)\n", + "* DSI with deconvolution (Canales-Rodriguez et al. 2010)\n", + "* Generalized Q Imaging (Yeh et al. 2010)\n", + "* MAPMRI (Özarslan et al. 2013)\n", + "* SHORE (Özarslan et al. 2008)\n", + "* CSA (Aganj et al. 2009)\n", + "* Q ball (Descoteaux et al. 2007)\n", + "* OPDT (Tristan-Vega et al. 2010)\n", + "* Sparse Fascicle Model (Rokem et al. 2015)\n", + "\n", + "The different algorithms implemented in the module all share a similar conceptual structure:\n", + "\n", + "* `ReconstModel` objects (e.g. `TensorModel`) carry the parameters that are required in order to fit a model. For example, the directions and magnitudes of the gradients that were applied in the experiment. The objects all have a `fit` method, which takes in data, and emites a `ReconstFit` object. This is where a lot of the heavy lifting of the processing will take place.\n", + "* `ReconstFit` objects carry the model that was used to generate the object. They also include the parameters that were estimated during fitting of the data. They have methods to calculate derived statistics, which can differ from model to model. All objects also have an orientation distribution function (`odf`), and most (but not all) contain a `predict` method, which enables the prediction of another dataset based on the current gradient table.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Reconstruction with the Tensor (dti) model\n", + "### Reconstruction with the DTI model\n", "\n", - "Let's get started! First, we will need to grab **pre-processed** dwi files and load them! We will also load in the anatomical image to use as a reference later on! " + "Let's get started! First, we will need to grab **preprocessed** DWI files and load them! We will also load in the anatomical image to use as a reference later on! " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ROBARTS/tkai/.local/lib/python3.6/site-packages/bids/layout/models.py:152: FutureWarning: The 'extension' entity currently excludes the leading dot ('.'). As of version 0.14.0, it will include the leading dot. To suppress this warning and include the leading dot, use `bids.config.set_option('extension_initial_dot', True)`.\n", + " FutureWarning)\n" + ] + } + ], "source": [ "from bids.layout import BIDSLayout\n", "from dipy.io.gradients import read_bvals_bvecs\n", @@ -73,14 +79,16 @@ "import nibabel as nib\n", "\n", "deriv_layout = BIDSLayout(\"../../../data/ds000221/derivatives\", validate=False)\n", - "subj=\"010006\"\n", + "subj = \"010006\"\n", "\n", "# Grab the transformed t1 file for reference\n", - "t1 = deriv_layout.get(subject=subj, space=\"dwi\", extension='nii.gz', return_type='file')[0]\n", + "t1 = deriv_layout.get(subject=subj, space=\"dwi\",\n", + " extension='nii.gz', return_type='file')[0]\n", "\n", "# Recall the preprocessed data is no longer in BIDS - we will directly grab these files\n", "dwi = \"../../../data/ds000221/derivatives/uncorrected_topup_eddy/sub-%s/ses-01/dwi/dwi.nii.gz\" % subj\n", - "bval = \"../../../data/ds000221/sub-%s/ses-01/dwi/sub-%s_ses-01_dwi.bval\" % (subj, subj)\n", + "bval = \"../../../data/ds000221/sub-%s/ses-01/dwi/sub-%s_ses-01_dwi.bval\" % (\n", + " subj, subj)\n", "bvec = \"../../../data/ds000221/derivatives/uncorrected_topup_eddy/sub-%s/ses-01/dwi/dwi.eddy_rotated_bvecs\" % subj\n", "\n", "t1_data = img.load_img(t1)\n", @@ -99,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -117,18 +125,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The fit method creates a TensorFit object which contains the fitting parameters and other attributes of the model. A number of quantitative scalar metrics can be computed using these eigenvalues. In this tutorial we will cover fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity! Each of these scalar metrics were calculated in the previous fitting step!\n", + "The fit method creates a TensorFit object which contains the fitting parameters and other attributes of the model. A number of quantitative scalar metrics can be derived from the eigenvalues! In this tutorial, we will cover fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. Each of these scalar, rotationally invariant metrics were calculated in the previous fitting step!\n", "\n", "### Fractional anisotropy (FA)\n", - "**Fractional anisotropy (FA)** characterizes the degree to which the distribution of diffusion in a voxel is directional. That is, whether there is relatively unrestricted diffusion in one particular direction.\n", "\n", - "Mathematically, FA is defined as the normalized variance of the eigenvalues of the tensor: \n", + "Fractional anisotropy (FA) characterizes the degree to which the distribution of diffusion in an imaging voxel is directional. That is, whether there is relatively unrestricted diffusion in a particular direction.\n", "\n", - "$FA = \\sqrt{\\frac{1}{2}\\frac{(\\lambda_1-\\lambda_2)^2+(\\lambda_1-\n", - " \\lambda_3)^2+(\\lambda_2-\\lambda_3)^2}{\\lambda_1^2+\n", - " \\lambda_2^2+\\lambda_3^2}}$\n", + "Mathematically, FA is defined as the normalized variance of the eigenvalues of the tensor:\n", "\n", - "Values of FA vary between 0 and 1 (unitless). In the cases of perfect, isotropic diffusion, $\\lambda_1 = \\lambda_2 = \\lambda_3$, the diffusion tensor is a sphere and FA = 0. If the first two eigenvalues are equal the tensor will be oblate or planar, whereas if the first eigenvalue is larger than the other two, it will have the mentioned ellipsoid shape: as diffusion progressively becomes more anisotropic, eigenvalues become more unequal, causing the tensor to be elongated, with FA approaching 1. Note that FA should be interpreted carefully. It may be an indication of the density of packing fibers in a voxel and the amount of myelin wrapped around those axons, but it is not always a measure of \"tissue integrity\".\n", + "![FA equation](../../../fig/diffusion_tensor_imaging/fa_eqn.png)\n", + "\n", + "Values of FA vary between 0 and 1 (unitless). In the cases of perfect, isotropic diffusion, ![Isotropic diffusion eigenvalues](../../../fig/diffusion_tensor_imaging/fa_iso.png), the diffusion tensor is a sphere and FA = 0. If the first two eigenvalues are equal the tensor will be oblate or planar, whereas if the first eigenvalue is larger than the other two, it will have the mentioned ellipsoid shape: as diffusion progressively becomes more anisotropic, eigenvalues become more unequal, causing the tensor to be elongated, with FA approaching 1. Note that FA should be interpreted carefully. It may be an indication of the density of packing fibers in a voxel and the amount of myelin wrapped around those axons, but it is not always a measure of \"tissue integrity\".\n", "\n", "Let's take a look at what the FA map looks like! An FA map is a gray-scale image, where higher intensities reflect more anisotropic diffuse regions.\n", "\n", @@ -137,47 +144,89 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "%matplotlib inline \n", - "\n", - "import matplotlib.pyplot as plt # To enable plotting\n", - "from nilearn import plotting as plot \n", + "from nilearn import plotting as plot\n", + "import matplotlib.pyplot as plt # To enable plotting\n", + "%matplotlib inline\n", "\n", "fa_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.fa)\n", "plot.plot_anat(fa_img, cut_coords=(0, -29, 20))" ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Derived from partial volume effects in imaging voxels due to the presence of different tissues, noise in the measurements and numerical errors, the DTI model estimation may yield negative eigenvalues. Such *degenerate* case is not physically meaningful. These values are usually revealed as black or 0-valued pixels in FA maps.\n", "\n", "FA is a central value in dMRI: large FA values imply that the underlying fiber populations have a very coherent orientation, whereas lower FA values point to voxels containing multiple fiber crossings. Lowest FA values are indicative of non-white matter tissue in healthy brains (see, for example, Alexander et al.'s \"Diffusion Tensor Imaging of the Brain\". Neurotherapeutics 4, 316-329 (2007), and Jeurissen et al.'s \"Investigating the Prevalence of Complex Fiber Configurations in White Matter Tissue with Diffusion Magnetic Resonance Imaging\". Hum. Brain Mapp. 2012, 34(11) pp. 2747-2766)." - ], - "cell_type": "markdown", - "metadata": {} + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mean diffusivity (MD)\n", - "An often used complimentary measure to FA is mean diffusivity (MD). MD is a measure of the degree of diffusion, independent of direction. This is sometimes known as the apparent diffusion coefficient (ADC). Mathematically, MD is computed as the mean eigenvalues of the tensor and is measured in mm^2/s\n", "\n", - "$MD = \\frac{\\lambda_1 + \\lambda_2 + \\lambda_3}{3}$ \n", - " \n", - "Similar to the previous FA image, lets take a look at what the MD map looks like! Again, higher intensities reflect reflect higher mean diffusivity!." + "An often used complimentary measure to FA is mean diffusivity (MD). MD is a measure of the degree of diffusion, independent of direction. This is sometimes known as the apparent diffusion coefficient (ADC). Mathematically, MD is computed as the mean eigenvalues of the tensor and is measured in mm^2/s.\n", + "\n", + "![MD equation](../../../fig/diffusion_tensor_imaging/md_eqn.png)\n", + "\n", + "Similar to the previous FA image, let's take a look at what the MD map looks like. Again, higher intensities reflect higher mean diffusivity!\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "%matplotlib inline \n", + "%matplotlib inline\n", "\n", "md_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.md)\n", "# Arbitrarily set min and max of color bar\n", @@ -189,21 +238,10 @@ "metadata": {}, "source": [ "### Axial and radial diffusivity (AD & RD)\n", - "The final two metrics we will discuss are axial diffusivity (AD) and radial diffusivity (RD). Two tensors with different shapes may yield the same FA values, and additional measures such as AD and RD are required to further characterize the tensor. AD describes the diffusion rate along the primary axis of diffusion, along ![](../../../fig/diffusion_tensor_imaging/primary_diffusion.png), or parallel to the axon (and hence, some works refer to it as the *parallel diffusivity*). On the other hand, RD reflects the average diffusivity along the other two minor axes (being named as *perpendicular diffusivity* in some works) (![](../../../fig/diffusion_tensor_imaging/minor_axes.png)). Both are measured in mm^2/s.\n", "\n", - "![Axial and Radial Diffusivities](../../../fig/diffusion_tensor_imaging/ax_rad_diff.png) {:class=\"img-responsive\"}\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline \n", + "The final two metrics we will discuss are axial diffusivity (AD) and radial diffusivity (RD). Two tensors with different shapes may yield the same FA values, and additional measures such as AD and RD are required to further characterize the tensor. AD describes the diffusion rate along the primary axis of diffusion, along ![Axial diffusivity eigenvalue](../../../fig/diffusion_tensor_imaging/primary_diffusion.png), or parallel to the axon (and hence, some works refer to it as the *parallel diffusivity*). On the other hand, RD reflects the average diffusivity along the other two minor axes (being named as *perpendicular diffusivity* in some works) (![Radial diffusivity eigenvalues](../../../fig/diffusion_tensor_imaging/minor_axes.png)). Both are measured in mm^2/s.\n", "\n", - "rd_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.rd)\n", - "plot.plot_anat(rd_img)" + "![Axial and radial diffusivities](../../../fig/diffusion_tensor_imaging/ax_rad_diff.png)" ] }, { @@ -223,21 +261,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "%matplotlib inline \n", - "\n", + "from scipy import ndimage # To rotate image for visualization purposes\n", "from dipy.reconst.dti import color_fa\n", + "%matplotlib inline\n", + "\n", "RGB_map = color_fa(dti_fit.fa, dti_fit.evecs)\n", "\n", - "from scipy import ndimage # To rotate image for visualization purposes\n", "\n", - "fig, ax = plt.subplots(1,3, figsize=(10, 10))\n", - "ax[0].imshow(ndimage.rotate(RGB_map[:, RGB_map.shape[1]//2, :, :], 90, reshape=False))\n", - "ax[1].imshow(ndimage.rotate(RGB_map[RGB_map.shape[0]//2, :, :, :], 90, reshape=False))\n", - "ax[2].imshow(ndimage.rotate(RGB_map[:, :, RGB_map.shape[2]//2, :], 90, reshape=False))" + "fig, ax = plt.subplots(1, 3, figsize=(10, 10))\n", + "ax[0].imshow(ndimage.rotate(\n", + " RGB_map[:, RGB_map.shape[1]//2, :, :], 90, reshape=False))\n", + "ax[1].imshow(ndimage.rotate(\n", + " RGB_map[RGB_map.shape[0]//2, :, :, :], 90, reshape=False))\n", + "ax[2].imshow(ndimage.rotate(\n", + " RGB_map[:, :, RGB_map.shape[2]//2, :], 90, reshape=False))" ] }, { @@ -255,21 +328,102 @@ "source": [ "### Some notes on DTI\n", "\n", - "DTI is only one of many models and is one of the simplest models available for modelling diffusion. While it is used for many studies, there are also some drawbacks (eg. ability to distinguish multiple fibre orientations in one imaging voxel). Some examples can be seen below! \n", + "DTI is only one of many models and is one of the simplest models available for modelling diffusion. While it is used for many studies, there are also some drawbacks (e.g. ability to distinguish multiple fibre orientations in an imaging voxel). Examples of this can be seen below!\n", + "\n", + "![DTI drawbacks](../../../fig/diffusion_tensor_imaging/FiberConfigurations.png)\n", "\n", - "![fiber_configurations](../../../fig/diffusion_tensor_imaging/FiberConfigurations.png)\n", + "_Sourced from Sotiropoulos and Zalesky (2017). Building connectomes using diffusion MRI: why, how, and but. NMR in Biomedicine. 4(32). e3752. doi:10.1002/nbm.3752._\n", + "\n", + "Though other models are outside the scope of this lesson, we recommend looking into some of the pros and cons of each model (listed previously) to choose one best suited for your data!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1\n", "\n", - "Sourced from: Sotiropolous and Zalewsky. (2017). Building connectomes using diffusion MRI: why, how, and but. NMR in Biomedicine. 4(32). e3752. 10.1002/nbm.3752. \n", + "Plot the axial and radial diffusivity maps of the example given. Start from fitting the preprocessed diffusion image.\n", "\n", - "Though other models are outside the scope of this lesson, we recommend looking into some of the pros and cons of each model (listed previously) to choose one best suited for your data! " + "## Solution" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bids.layout import BIDSLayout\n", + "from dipy.io.gradients import read_bvals_bvecs\n", + "from dipy.core.gradients import gradient_table\n", + "import dipy.reconst.dti as dti\n", + "from dipy.segment.mask import median_otsu\n", + "from nilearn import image as img\n", + "import nibabel as nib\n", + "\n", + "deriv_layout = BIDSLayout(\"../../../data/ds000221/derivatives\", validate=False)\n", + "subj = \"010006\"\n", + "\n", + "t1 = deriv_layout.get(subject=subj, space=\"dwi\",\n", + " extension='nii.gz', return_type='file')[0]\n", + "dwi = \"../../../data/ds000221/derivatives/uncorrected_topup_eddy/sub-%s/ses-01/dwi/dwi.nii.gz\" % subj\n", + "bval = \"../../../data/ds000221/sub-%s/ses-01/dwi/sub-%s_ses-01_dwi.bval\" % (\n", + " subj, subj)\n", + "bvec = \"../../../data/ds000221/derivatives/uncorrected_topup_eddy/sub-%s/ses-01/dwi/dwi.eddy_rotated_bvecs\" % subj\n", + "\n", + "t1_data = img.load_img(t1)\n", + "dwi_data = img.load_img(dwi)\n", + "\n", + "gt_bvals, gt_bvecs = read_bvals_bvecs(bval, bvec)\n", + "gtab = gradient_table(gt_bvals, gt_bvecs)\n", + "\n", + "dwi_data = dwi_data.get_fdata()\n", + "dwi_data, dwi_mask = median_otsu(dwi_data, vol_idx=[0], numpass=1)\n", + "\n", + "# Fit dti model\n", + "dti_model = dti.TensorModel(gtab)\n", + "dti_fit = dti_model.fit(dwi_data, mask=dwi_mask) # This step may take a while\n", + "\n", + "# Plot axial diffusivity map\n", + "ad_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.ad)\n", + "plot.plot_anat(ad_img, cut_coords=(0, -29, 20), vmin=0, vmax=0.01)\n", + "\n", + "# Plot radial diffusivity map\n", + "rd_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.rd)\n", + "plot.plot_anat(rd_img, cut_coords=(0, -29, 20), vmin=0, vmax=0.01)" + ] } ], "metadata": { @@ -288,9 +442,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10-final" + "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From 726f13bab49f6d4b9040510528246a0f4124eb5e Mon Sep 17 00:00:00 2001 From: Jason Kai Date: Mon, 5 Apr 2021 17:45:12 -0400 Subject: [PATCH 5/7] fix fig path --- .../solutions/diffusion_tensor_imaging_solutions.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb b/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb index c5e6573c..a4516b63 100644 --- a/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb +++ b/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb @@ -12,7 +12,7 @@ "\n", "![Diffusion signal equation](../../../fig/diffusion_tensor_imaging/diffusion_eqn.png)\n", "\n", - "Where ![Diffusion unit vector](../../../fig/diffusion_tensor_imaging/inline_unitvector.png) is a unit vector in 3D space indicating the direction of measurement and b are the parameters of the measurement, such as the strength and duration of diffusion-weighting gradient. ![Diffusion signal](../../../fig/diffusion_tensor_imaging/inline_diffusionsignal.png) is the diffusion-weighted signal measured and ![Non-weighted diffusion signal](../../..//fig/diffusion_tensor_imaging/inline_nondiffsignal.png) is the signal conducted in a measurement with no diffusion weighting. ![Diffusivity](../../../fig/diffusion_tensor_imaging/inline_diffusionmatrix.png) is a positive-definite quadratic form, which contains six free parameters to be fit. These six parameters are:\n", + "Where ![Diffusion unit vector](../../../fig/diffusion_tensor_imaging/inline_unitvector.png) is a unit vector in 3D space indicating the direction of measurement and b are the parameters of the measurement, such as the strength and duration of diffusion-weighting gradient. ![Diffusion signal](../../../fig/diffusion_tensor_imaging/inline_diffusionsignal.png) is the diffusion-weighted signal measured and ![Non-weighted diffusion signal](../../../fig/diffusion_tensor_imaging/inline_nondiffsignal.png) is the signal conducted in a measurement with no diffusion weighting. ![Diffusivity](../../../fig/diffusion_tensor_imaging/inline_diffusionmatrix.png) is a positive-definite quadratic form, which contains six free parameters to be fit. These six parameters are:\n", "\n", "![Diffusivity matrix](../../../fig/diffusion_tensor_imaging/diffusion_matrix.png)\n", "\n", From fbc19c3611827a633946049263ae143fef9694bb Mon Sep 17 00:00:00 2001 From: Jason Kai Date: Mon, 5 Apr 2021 17:45:28 -0400 Subject: [PATCH 6/7] update non-solutions notebook and fix paths --- .../diffusion_tensor_imaging.ipynb | 416 +++++------------- 1 file changed, 116 insertions(+), 300 deletions(-) diff --git a/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb b/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb index 1e11debb..a3d91bf3 100644 --- a/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb +++ b/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb @@ -6,63 +6,60 @@ "source": [ "## Diffusion Tensor Imaging (DTI)\n", "\n", - "Diffusion tensor imaging or \"DTI\" refers to images describing diffusion as a tensor model and is derived from preprocessed DWI data. First proposed by Basser and colleagues ([Basser, 1994](https://www.ncbi.nlm.nih.gov/pubmed/8130344)), the diffusion tensor model describes the diffusion within a voxel. This model has been very influential in demonstrating the utility of diffusion MRI in characterizing the microstructure of white matter and the biophysical properties (inferred from local diffusion properties). DTI is still a commonly used.\n", + "Diffusion tensor imaging or \"DTI\" refers to images describing diffusion with a tensor model. DTI is derived from preprocessed diffusion weighted imaging (DWI) data. First proposed by Basser and colleagues ([Basser, 1994](https://www.ncbi.nlm.nih.gov/pubmed/8130344)), the diffusion tensor model describes diffusion characteristics within an imaging voxel. This model has been very influential in demonstrating the utility of the diffusion MRI in characterizing the microstructure of white matter and the biophysical properties (inferred from local diffusion properties). The DTI model is still a commonly used model to investigate white matter.\n", "\n", - "The diffusion tensor models the diffusion signal as:\n", + "The tensor models the diffusion signal mathematically as:\n", "\n", - "$\\frac{S(\\mathbf{g}, b)}{S_0} = e^{-b\\mathbf{g}^T \\mathbf{D} \\mathbf{g}}$\n", + "![Diffusion signal equation](../../fig/diffusion_tensor_imaging/diffusion_eqn.png)\n", "\n", - "Where $\\mathbf{g}$ is a unit vector in 3D space indicating the direction of measurement and b are the parameters of measurement, such as the strength and duration of diffusion-weighting gradient. $S(\\mathbf{g}, b)$ is the diffusion-weighted signal measured and $S_0$ is the signal conducted in a measurement with no diffusion weighting. $\\mathbf{D}$ is a positive-definite quadratic form, which contains six free parameters to be fit. These six parameters are:\n", + "Where ![Diffusion unit vector](../../fig/diffusion_tensor_imaging/inline_unitvector.png) is a unit vector in 3D space indicating the direction of measurement and b are the parameters of the measurement, such as the strength and duration of diffusion-weighting gradient. ![Diffusion signal](../../fig/diffusion_tensor_imaging/inline_diffusionsignal.png) is the diffusion-weighted signal measured and ![Non-weighted diffusion signal](../../fig/diffusion_tensor_imaging/inline_nondiffsignal.png) is the signal conducted in a measurement with no diffusion weighting. ![Diffusivity](../../fig/diffusion_tensor_imaging/inline_diffusionmatrix.png) is a positive-definite quadratic form, which contains six free parameters to be fit. These six parameters are:\n", "\n", - "$\\mathbf{D} = \\begin{pmatrix} D_{xx} & D_{xy} & D_{xz} \\\\\n", - " D_{yx} & D_{yy} & D_{yz} \\\\\n", - " D_{zx} & D_{zy} & D_{zz} \\\\ \n", - " \\end{pmatrix}$\n", + "![Diffusivity matrix](../../fig/diffusion_tensor_imaging/diffusion_matrix.png)\n", "\n", - "This matrix is a variance/covariance matrix of the diffusivity along the three spatial dimensions. Note that we can assume that diffusivity has antipodal symmetry, so elements across the diagonal are equal. For example: $D_{xy} = D_{yx}$. This is why there are only 6 free parameters to estimate here. \n", + "The diffusion matrix is a variance-covariance matrix of the diffusivity along the three spatial dimensions. Note that we can assume that the diffusivity has antipodal symmetry, so elements across the diagonal of the matrix are equal. For example: ![Symmetry in the diffusivity matrix](../../fig/diffusion_tensor_imaging/inline_diagelements.png). This is why there are only 6 free parameters to estimate here.\n", "\n", - "Tensors are represented by ellipsoids characterized by calculated eigenvalues ($\\lambda_1, \\lambda_2, \\lambda_3$) and eigenvectors ($\\epsilon_1, \\epsilon_2, \\epsilon_3$) from the previously described matrix. Eigenvalues and eigenvectors are normally sorted in descending magnitude.\n", + "Tensors are represented by ellipsoids characterized by calculated eigenvalues (![Diffusivity matrix eigenvalues](../../fig/diffusion_tensor_imaging/inline_eigval.png)) and eigenvectors (![Diffusivity matrix eigenvectors](../../fig/diffusion_tensor_imaging/inline_eigvec.png)) from the previously described matrix. The computed eigenvalues and eigenvectors are normally sorted in descending magnitude (i.e. ![Diffusivity matrix eigenvalues magnitudes](../../fig/diffusion_tensor_imaging/inline_sortedeigvec.png)). Eigenvalues are always strictly positive in the context of dMRI and are measured in mm^2/s. In the DTI model, the largest eigenvalue gives the principal direction of the diffusion tensor, and the other two eigenvectors span the orthogonal plane to the former direction.\n", "\n", - "![Diffusion Tensor](../../fig/diffusion_tensor_imaging/DiffusionTensor.png)
\n", - "Adapated from Jellison _et al._, 2004\n", + "![Diffusion tensor](../../fig/diffusion_tensor_imaging/DiffusionTensor.png)\n", + "_Adapted from Jelison et al., 2004_\n", "\n", - "In the following example, we show how to model your diffusion datasets. It should be noted that there are a number of diffusion models and many of these are implemented in `Dipy`. However, for the purposes of this tutorial, we will be focus on the tensor model.\n", + "In the following example, we will walk through how to model a diffusion dataset. While there are a number of diffusion models, many of which are implemented in `DIPY`. However, for the purposes of this lesson, we will focus on the tensor model described above.\n", "\n", "### Reconstruction with the `dipy.reconst` module\n", "\n", - "The `reconst` module contains implementations of the following models: \n", - "\n", - "- Tensor (Basser et al., 1994)\n", - "- Constrained Spherical Deconvolution (Tournier et al. 2007)\n", - "- Diffusion Kurtosis (Jensen et al. 2005)\n", - "- DSI (Wedeen et al. 2008)\n", - "- DSI with deconvolution (Canales-Rodriguez et al. 2010)\n", - "- Generalized Q Imaging (Yeh et al. 2010)\n", - "- MAPMRI (Ozarsalan et al. 2013)\n", - "- SHORE (Ozarsalan et al. 2008)\n", - "- CSA (Aganj et al. 2009)\n", - "- Q ball (Descoteaux et al. 2007)\n", - "- OPDT (Tristan-Vega et al. 2010)\n", - "- Sparse Fascicle Model (Rokem et al. 2015)\n", - "\n", - "The different algorithms implemented in the `reconst` module all share a similar conceptual structure: \n", - "\n", - "- `ReconstModel` objects (e.g, `TensorModel`) carry the parameters that are required in order to fit a model. For example, the directions and intensities of the gradients that were applied in the experiment. The all have a `fit` method, which takes in data, and emits a `ReconstFit` object. This is where a lot of the heavy lifting will take place.\n", - "- `ReconstFit` objects carry the model that was used to generate them. They also carry around the parameters that were estimated during fitting of the data. They have methods to calculate derived statistics, such as FA and MD (for the tensor), which can differ from module to module. The also all have an `odf` , and most of them (but not all) have `predict` methods, which allow you to predict another data-set based on the a gradient table." + "The `reconst` module contains implementations of the following models:\n", + "\n", + "* Tensor (Basser et al., 1994)\n", + "* Constrained Spherical Deconvolution (Tournier et al. 2007)\n", + "* Diffusion Kurtosis (Jensen et al. 2005)\n", + "* DSI (Wedeen et al. 2008)\n", + "* DSI with deconvolution (Canales-Rodriguez et al. 2010)\n", + "* Generalized Q Imaging (Yeh et al. 2010)\n", + "* MAPMRI (Özarslan et al. 2013)\n", + "* SHORE (Özarslan et al. 2008)\n", + "* CSA (Aganj et al. 2009)\n", + "* Q ball (Descoteaux et al. 2007)\n", + "* OPDT (Tristan-Vega et al. 2010)\n", + "* Sparse Fascicle Model (Rokem et al. 2015)\n", + "\n", + "The different algorithms implemented in the module all share a similar conceptual structure:\n", + "\n", + "* `ReconstModel` objects (e.g. `TensorModel`) carry the parameters that are required in order to fit a model. For example, the directions and magnitudes of the gradients that were applied in the experiment. The objects all have a `fit` method, which takes in data, and emites a `ReconstFit` object. This is where a lot of the heavy lifting of the processing will take place.\n", + "* `ReconstFit` objects carry the model that was used to generate the object. They also include the parameters that were estimated during fitting of the data. They have methods to calculate derived statistics, which can differ from model to model. All objects also have an orientation distribution function (`odf`), and most (but not all) contain a `predict` method, which enables the prediction of another dataset based on the current gradient table.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Reconstruction with the Tensor (dti) model\n", + "### Reconstruction with the DTI model\n", "\n", - "Let's get started! First, we will need to grab **pre-processed** dwi files and load them! We will also load in the anatomical image to use as a reference later on! " + "Let's get started! First, we will need to grab **preprocessed** DWI files and load them! We will also load in the anatomical image to use as a reference later on! " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -72,12 +69,18 @@ "from nilearn import image as img\n", "import nibabel as nib\n", "\n", - "layout = BIDSLayout(\"../../data/ds000030/derivatives\", validate=False)\n", + "deriv_layout = BIDSLayout(\"../../data/ds000221/derivatives\", validate=False)\n", + "subj = \"010006\"\n", "\n", - "t1 = layout.get(subject='10788', suffix='T1w', extension='nii.gz', return_type='file')[0]\n", - "dwi = layout.get(subject='10788', suffix='preproc', extension='nii.gz', return_type='file')[0]\n", - "bval = layout.get(subject='10788', suffix='preproc', extension='bval', return_type='file')[0]\n", - "bvec = layout.get(subject='10788', suffix='preproc', extension='bvec', return_type='file')[0]\n", + "# Grab the transformed t1 file for reference\n", + "t1 = deriv_layout.get(subject=subj, space=\"dwi\",\n", + " extension='nii.gz', return_type='file')[0]\n", + "\n", + "# Recall the preprocessed data is no longer in BIDS - we will directly grab these files\n", + "dwi = \"../../data/ds000221/derivatives/uncorrected_topup_eddy/sub-%s/ses-01/dwi/dwi.nii.gz\" % subj\n", + "bval = \"../../data/ds000221/sub-%s/ses-01/dwi/sub-%s_ses-01_dwi.bval\" % (\n", + " subj, subj)\n", + "bvec = \"../../data/ds000221/derivatives/uncorrected_topup_eddy/sub-%s/ses-01/dwi/dwi.eddy_rotated_bvecs\" % subj\n", "\n", "t1_data = img.load_img(t1)\n", "dwi_data = img.load_img(dwi)\n", @@ -90,12 +93,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need to create the \"tensor model using our gradient table and then fit the model using our data! We will start by creating a mask from our data and apply it to avoid calculating tensors on the background! This can be done using dipy's mask module. Then, we will our data!" + "Next, we need to create the tensor model using our gradient table and then fit the model using our data! We will start by creating a mask from our data and apply it to avoid calculating tensors on the background! This can be done using `DIPY`'s mask module. Then, we will our data!" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -109,253 +112,85 @@ "dti_fit = dti_model.fit(dwi_data, mask=dwi_mask) # This step may take a while" ] }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(176, 256, 256)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dwi_mask.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "x_slice = dwi_mask[88, :, :]\n", - "y_slice = dwi_mask[:, 128, :]\n", - "z_slice = dwi_mask[:, :, 128]\n", - "\n", - "slices = [x_slice, y_slice, z_slice]\n", - "\n", - "fig, axes = plt.subplots(1, len(slices))\n", - "for i, slice in enumerate(slices):\n", - " axes[i].imshow(slice.T, cmap=\"gray\", origin=\"lower\")" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The fit method creates a TensorFit object which contains the fitting parameters and other attributes of the model. A number of quantitative scalar metrics can be computed using these eigenvalues. In this tutorial we will cover fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity! Each of these scalar metrics were calculated in the previous fitting step!\n", + "The fit method creates a TensorFit object which contains the fitting parameters and other attributes of the model. A number of quantitative scalar metrics can be derived from the eigenvalues! In this tutorial, we will cover fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. Each of these scalar, rotationally invariant metrics were calculated in the previous fitting step!\n", "\n", "### Fractional anisotropy (FA)\n", - "**Fractional anisotropy (FA)** characterizes the degree to which the distribution of diffusion in a voxel is directional. That is, whether there is relatively unrestricted diffusion in one particular direction.\n", "\n", - "Mathematically, FA is defined as the normalized variance of the eigenvalues of the tensor: \n", + "Fractional anisotropy (FA) characterizes the degree to which the distribution of diffusion in an imaging voxel is directional. That is, whether there is relatively unrestricted diffusion in a particular direction.\n", + "\n", + "Mathematically, FA is defined as the normalized variance of the eigenvalues of the tensor:\n", "\n", - "$FA = \\sqrt{\\frac{1}{2}\\frac{(\\lambda_1-\\lambda_2)^2+(\\lambda_1-\n", - " \\lambda_3)^2+(\\lambda_2-\\lambda_3)^2}{\\lambda_1^2+\n", - " \\lambda_2^2+\\lambda_3^2}}$\n", - " \n", - "Values of FA vary between 0 and 1. In the cases of perfect, isotropic diffusion, $\\lambda_1 = \\lambda_2 = \\lambda_3$, the diffusion ellipsoid is a sphere and FA = 0. As diffusion progressively becomes more aniostropic, eigenvalues become more unequal causing the ellipsoid to be elongated, with FA → 1. Note that FA should be interpreted carefully. It may be an indication of the density of packing of fibers in a voxel, and the amount of myelin wrapping these axons, but it is not always a measure of \"tissue integrity\".\n", + "![FA equation](../../fig/diffusion_tensor_imaging/fa_eqn.png)\n", "\n", - "Lets take a look at what the FA map looks like! A FA map is a gray-scale, where brighter areas reflect more anisotropic regions.\n", + "Values of FA vary between 0 and 1 (unitless). In the cases of perfect, isotropic diffusion, ![Isotropic diffusion eigenvalues](../../fig/diffusion_tensor_imaging/fa_iso.png), the diffusion tensor is a sphere and FA = 0. If the first two eigenvalues are equal the tensor will be oblate or planar, whereas if the first eigenvalue is larger than the other two, it will have the mentioned ellipsoid shape: as diffusion progressively becomes more anisotropic, eigenvalues become more unequal, causing the tensor to be elongated, with FA approaching 1. Note that FA should be interpreted carefully. It may be an indication of the density of packing fibers in a voxel and the amount of myelin wrapped around those axons, but it is not always a measure of \"tissue integrity\".\n", "\n", - "_Note: We will have to first create the image from the array (hint: use the reference anatomical)_" + "Let's take a look at what the FA map looks like! An FA map is a gray-scale image, where higher intensities reflect more anisotropic diffuse regions.\n", + "\n", + "_Note: we will have to first create the image from the array, making use of the reference anatomical_" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from nilearn import plotting as plot" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "from nilearn import plotting as plot\n", + "import matplotlib.pyplot as plt # To enable plotting\n", + "%matplotlib inline\n", + "\n", "fa_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.fa)\n", - "plot.plot_anat(fa_img)" + "plot.plot_anat(fa_img, cut_coords=(0, -29, 20))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Mean diffusivity (MD)\n", - "An often used complimentary measure to FA is **mean diffusivity (MD)**. MD is a measure of the degree of diffusion, independent of direction. This is sometimes known as the **apparent diffusion coefficient (ADC)**. Mathematically, MD is computed as the mean eigenvalues of the tensor.\n", + "Derived from partial volume effects in imaging voxels due to the presence of different tissues, noise in the measurements and numerical errors, the DTI model estimation may yield negative eigenvalues. Such *degenerate* case is not physically meaningful. These values are usually revealed as black or 0-valued pixels in FA maps.\n", "\n", - "$MD = \\frac{\\lambda_1 + \\lambda_2 + \\lambda_3}{3}$ \n", - " \n", - "Lets take a look at what the MD map looks like! Brighter areas reflect higher diffusivity!." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "md_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.md)\n", - "plot.plot_anat(md_img)" + "FA is a central value in dMRI: large FA values imply that the underlying fiber populations have a very coherent orientation, whereas lower FA values point to voxels containing multiple fiber crossings. Lowest FA values are indicative of non-white matter tissue in healthy brains (see, for example, Alexander et al.'s \"Diffusion Tensor Imaging of the Brain\". Neurotherapeutics 4, 316-329 (2007), and Jeurissen et al.'s \"Investigating the Prevalence of Complex Fiber Configurations in White Matter Tissue with Diffusion Magnetic Resonance Imaging\". Hum. Brain Mapp. 2012, 34(11) pp. 2747-2766)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Axial Diffusivity (AD)\n", - "**Axial diffusivity (AD)** describes the diffusion rate along the primary axis of diffusion - along $\\lambda_1$, or parallel to the axon.\n", + "### Mean diffusivity (MD)\n", + "\n", + "An often used complimentary measure to FA is mean diffusivity (MD). MD is a measure of the degree of diffusion, independent of direction. This is sometimes known as the apparent diffusion coefficient (ADC). Mathematically, MD is computed as the mean eigenvalues of the tensor and is measured in mm^2/s.\n", "\n", - "$AD = \\lambda_1$\n", + "![MD equation](../../fig/diffusion_tensor_imaging/md_eqn.png)\n", "\n", - "Try plotting the image!" + "Similar to the previous FA image, let's take a look at what the MD map looks like. Again, higher intensities reflect higher mean diffusivity!\n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "%matplotlib inline \n", + "%matplotlib inline\n", "\n", - "ad_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.ad)\n", - "plot.plot_anat(ad_img)" + "md_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.md)\n", + "# Arbitrarily set min and max of color bar\n", + "plot.plot_anat(md_img, cut_coords=(0, -29, 20), vmin=0, vmax=0.01)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Radial Diffusivity (RD)\n", + "### Axial and radial diffusivity (AD & RD)\n", "\n", - "The final metric we will discuss is **radial diffusivity (RD)**. This metric reflects the average diffusivity along the two minor axes ($\\lambda_2, \\lambda_3$).\n", + "The final two metrics we will discuss are axial diffusivity (AD) and radial diffusivity (RD). Two tensors with different shapes may yield the same FA values, and additional measures such as AD and RD are required to further characterize the tensor. AD describes the diffusion rate along the primary axis of diffusion, along ![Axial diffusivity eigenvalue](../../fig/diffusion_tensor_imaging/primary_diffusion.png), or parallel to the axon (and hence, some works refer to it as the *parallel diffusivity*). On the other hand, RD reflects the average diffusivity along the other two minor axes (being named as *perpendicular diffusivity* in some works) (![Radial diffusivity eigenvalues](../../fig/diffusion_tensor_imaging/minor_axes.png)). Both are measured in mm^2/s.\n", "\n", - "$RD = \\frac{\\lambda_2 + \\lambda_3}{2}$\n", - "\n", - "Again, try plotting the RD map!" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline \n", - "\n", - "rd_img = img.new_img_like(ref_niimg=t1_data, data=dti_fit.rd)\n", - "plot.plot_anat(rd_img)" + "![Axial and radial diffusivities](../../fig/diffusion_tensor_imaging/ax_rad_diff.png)" ] }, { @@ -363,71 +198,36 @@ "metadata": {}, "source": [ "### Tensor visualizations\n", - "There are several ways of visualizing tensors. One way is using an RGB map based on the primary diffusion direction ($\\lambda_1$) overlayed on the FA map, where the colour encodes the direction of diffusion! To do this with dipy, we can use the `color_fa` function. The colors map to the following directions:\n", "\n", - "* Red = Left/Right\n", - "* Green = Anterior/Posterior\n", - "* Blue = Superior/Inferior\n", + "There are several ways of visualizing tensors. One way is using an RGB map, which overlays the primary diffusion orientation on an FA map. The colours of this map encodes the diffusion orientation. Note that this map provides no directional information (e.g. whether the diffusion flows from right-to-left or vice-versa). To do this with DIPY, we can use the color_fa function. The colours map to the following orientations:\n", "\n", - "_Note: `nilearn`'s plotting functions are unable to visualize these RGB maps. However, we can use Python's `matplotlib` to view these images._" + "* Red = Left / Right\n", + "* Green = Anterior / Posterior\n", + "* Blue = Superior / Inferior\n", + "\n", + "_Note: The plotting functions in nilearn are unable to visualize these RGB maps. However, we can use the matplotlib library to view these images._" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "from scipy import ndimage # To rotate image for visualization purposes\n", "from dipy.reconst.dti import color_fa\n", - "RGB_map = color_fa(dti_fit.fa, dti_fit.evecs)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline \n", + "%matplotlib inline\n", + "\n", + "RGB_map = color_fa(dti_fit.fa, dti_fit.evecs)\n", "\n", - "from scipy import ndimage # To rotate image for visualization purposes\n", "\n", - "fig, ax = plt.subplots(1,3, figsize=(10,10))\n", - "ax[0].imshow(ndimage.rotate(RGB_map[:, RGB_map.shape[1]//2, :, :], 90, reshape=False))\n", - "ax[1].imshow(ndimage.rotate(RGB_map[RGB_map.shape[0]//2, :, :, :], 90, reshape=False))\n", - "ax[2].imshow(ndimage.rotate(RGB_map[:, :, RGB_map.shape[2]//2, :], 90, reshape=False))" + "fig, ax = plt.subplots(1, 3, figsize=(10, 10))\n", + "ax[0].imshow(ndimage.rotate(\n", + " RGB_map[:, RGB_map.shape[1]//2, :, :], 90, reshape=False))\n", + "ax[1].imshow(ndimage.rotate(\n", + " RGB_map[RGB_map.shape[0]//2, :, :, :], 90, reshape=False))\n", + "ax[2].imshow(ndimage.rotate(\n", + " RGB_map[:, :, RGB_map.shape[2]//2, :], 90, reshape=False))" ] }, { @@ -445,14 +245,30 @@ "source": [ "### Some notes on DTI\n", "\n", - "DTI is only one of many models and is one of the simplest models available for modelling diffusion. While it is used for many studies, there are also some drawbacks (eg. ability to distinguish multiple fibre orientations in one imaging voxel). Some examples can be seen below! \n", + "DTI is only one of many models and is one of the simplest models available for modelling diffusion. While it is used for many studies, there are also some drawbacks (e.g. ability to distinguish multiple fibre orientations in an imaging voxel). Examples of this can be seen below!\n", "\n", - "![fiber_configurations](../../fig/diffusion_tensor_imaging/FiberConfigurations.png)\n", + "![DTI drawbacks](../../fig/diffusion_tensor_imaging/FiberConfigurations.png)\n", "\n", - "Sourced from: Sotiropolous and Zalewsky. (2017). Building connectomes using diffusion MRI: why, how, and but. NMR in Biomedicine. 4(32). e3752. 10.1002/nbm.3752. \n", + "_Sourced from Sotiropoulos and Zalesky (2017). Building connectomes using diffusion MRI: why, how, and but. NMR in Biomedicine. 4(32). e3752. doi:10.1002/nbm.3752._\n", "\n", - "Though other models are outside the scope of this lesson, we recommend looking into some of the pros and cons of each model (listed previously) to choose one best suited for your data! " + "Though other models are outside the scope of this lesson, we recommend looking into some of the pros and cons of each model (listed previously) to choose one best suited for your data!" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1\n", + "\n", + "Plot the axial and radial diffusivity maps of the example given. Start from fitting the preprocessed diffusion image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -471,9 +287,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From 1e5254f687f0364f0ee014d432df03cba374de61 Mon Sep 17 00:00:00 2001 From: Jason Kai Date: Mon, 5 Apr 2021 18:38:53 -0400 Subject: [PATCH 7/7] Fix sentence about ReconstModel --- _episodes/diffusion_tensor_imaging.md | 2 +- code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb | 4 ++-- .../solutions/diffusion_tensor_imaging_solutions.ipynb | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/_episodes/diffusion_tensor_imaging.md b/_episodes/diffusion_tensor_imaging.md index e3984569..f3dadecb 100644 --- a/_episodes/diffusion_tensor_imaging.md +++ b/_episodes/diffusion_tensor_imaging.md @@ -87,7 +87,7 @@ The different algorithms implemented in the module all share a similar conceptua * ReconstModel objects (e.g. TensorModel) carry the parameters that are required in order to fit a model. For example, the directions and magnitudes of the gradients that were applied in the experiment. The objects all have a fit method, which takes -in data, and emites a ReconstFit object. This is where a lot of the heavy lifting +in data, and returns a ReconstFit object. This is where a lot of the heavy lifting of the processing will take place. * ReconstFit objects carry the model that was used to generate the object. They also include the parameters that were estimated during fitting of the data. They have diff --git a/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb b/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb index a3d91bf3..b48966c0 100644 --- a/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb +++ b/code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb @@ -44,7 +44,7 @@ "\n", "The different algorithms implemented in the module all share a similar conceptual structure:\n", "\n", - "* `ReconstModel` objects (e.g. `TensorModel`) carry the parameters that are required in order to fit a model. For example, the directions and magnitudes of the gradients that were applied in the experiment. The objects all have a `fit` method, which takes in data, and emites a `ReconstFit` object. This is where a lot of the heavy lifting of the processing will take place.\n", + "* `ReconstModel` objects (e.g. `TensorModel`) carry the parameters that are required in order to fit a model. For example, the directions and magnitudes of the gradients that were applied in the experiment. The objects all have a `fit` method, which takes in data, and returns a `ReconstFit` object. This is where a lot of the heavy lifting of the processing will take place.\n", "* `ReconstFit` objects carry the model that was used to generate the object. They also include the parameters that were estimated during fitting of the data. They have methods to calculate derived statistics, which can differ from model to model. All objects also have an orientation distribution function (`odf`), and most (but not all) contain a `predict` method, which enables the prediction of another dataset based on the current gradient table.\n" ] }, @@ -292,4 +292,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb b/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb index a4516b63..ad4d17a8 100644 --- a/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb +++ b/code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb @@ -44,7 +44,7 @@ "\n", "The different algorithms implemented in the module all share a similar conceptual structure:\n", "\n", - "* `ReconstModel` objects (e.g. `TensorModel`) carry the parameters that are required in order to fit a model. For example, the directions and magnitudes of the gradients that were applied in the experiment. The objects all have a `fit` method, which takes in data, and emites a `ReconstFit` object. This is where a lot of the heavy lifting of the processing will take place.\n", + "* `ReconstModel` objects (e.g. `TensorModel`) carry the parameters that are required in order to fit a model. For example, the directions and magnitudes of the gradients that were applied in the experiment. The objects all have a `fit` method, which takes in data, and returns a `ReconstFit` object. This is where a lot of the heavy lifting of the processing will take place.\n", "* `ReconstFit` objects carry the model that was used to generate the object. They also include the parameters that were estimated during fitting of the data. They have methods to calculate derived statistics, which can differ from model to model. All objects also have an orientation distribution function (`odf`), and most (but not all) contain a `predict` method, which enables the prediction of another dataset based on the current gradient table.\n" ] }, @@ -447,4 +447,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file