forked from markbangert/matRad
-
Notifications
You must be signed in to change notification settings - Fork 0
/
matRad_DijSampling.m
59 lines (51 loc) · 2.34 KB
/
matRad_DijSampling.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
function [ix,bixelDose] = matRad_DijSampling(ix,bixelDose,relDoseLimits,SamplingRate)
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% matRad dij sampling function. This function samples
%
% call
% [ix,bixelDose] = matRad_DijImportanceSampling(ix,bixelDose,relDoseLimits)
%
% input
% ix: indices of voxels where we want to compute dose influence data
% bixelDose: dose at specified locations as linear vector
% relDoseLimits: relative dose levels used to define sampling range
% e.g.[0.01 0.001] means that dij elements will be
% sampled in the relative dose range 1%-0.1%
%
% output
% ix: reduced indices of voxels where we want to compute dose influence data
% bixelDose reduced dose at specified locations as linear vector
%
% References
% [1] http://dx.doi.org/10.1118/1.1469633
%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Copyright 2016 the matRad development team.
%
% This file is part of the matRad project. It is subject to the license
% terms in the LICENSE file found in the top-level directory of this
% distribution and at https://github.com/e0404/matRad/LICENSES.txt. No part
% of the matRad project, including this file, may be copied, modified,
% propagated, or distributed except according to the terms contained in the
% LICENSE file.
%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~exist('SamplingRate','var')
SamplingRate = 0.1;
end
maxDose = max(bixelDose);
linIxSample = find(bixelDose < maxDose * relDoseLimits(1) & bixelDose > maxDose * relDoseLimits(2));
NumSamples = fix(numel(linIxSample)*SamplingRate);
bixelSampDose = bixelDose(linIxSample);
Prob = bixelSampDose/max(bixelSampDose);
ProbNorm = sort(Prob,'descend')./(sum(Prob));
CDF = cumsum(ProbNorm);
randomValues = (CDF(end)-CDF(1)).* rand(NumSamples,1) + CDF(1);
ixSamp = interp1(CDF,linIxSample,randomValues,'nearest');
ixNew = (bixelDose > maxDose * relDoseLimits(1)) ;
ixNew(ixSamp) = 1;
bixelDose(ixSamp) = maxDose * relDoseLimits(1);
bixelDose = bixelDose(ixNew);
ix = ix(ixNew);