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1 function [minIntrVec,stat,actpctg] = genSampling(pdf,iter,tol)
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2
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3 %[mask,stat,N] = genSampling(pdf,iter,tol)
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4 %
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5 % a monte-carlo algorithm to generate a sampling pattern with
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6 % minimum peak interference. The number of samples will be
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7 % sum(pdf) +- tol
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8 %
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9 % pdf - probability density function to choose samples from
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10 % iter - number of tries
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11 % tol - the deviation from the desired number of samples in samples
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12 %
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13 % returns:
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14 % mask - sampling pattern
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15 % stat - vector of min interferences measured each try
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16 % actpctg - actual undersampling factor
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17 %
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18 % (c) Michael Lustig 2007
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19
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20 % This file is used with the kind permission of Michael Lustig
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21 % (mlustig@stanford.edu), and originally appeared in the
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22 % SparseMRI toolbox, http://www.stanford.edu/~mlustig/ .
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23 %
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24 % $Id: genSampling.m 1040 2008-06-26 20:29:02Z ewout78 $
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25
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26 % h = waitbar(0);
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27
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28 pdf(find(pdf>1)) = 1;
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29 K = sum(pdf(:));
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30
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31 minIntr = 1e99;
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32 minIntrVec = zeros(size(pdf));
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33
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34 for n=1:iter
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35 tmp = zeros(size(pdf));
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36 while abs(sum(tmp(:)) - K) > tol
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37 tmp = rand(size(pdf))<pdf;
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38 end
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39
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40 TMP = ifft2(tmp./pdf);
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41 if max(abs(TMP(2:end))) < minIntr
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42 minIntr = max(abs(TMP(2:end)));
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43 minIntrVec = tmp;
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44 end
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45 stat(n) = max(abs(TMP(2:end)));
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46 % waitbar(n/iter,h);
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47 end
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48
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49 actpctg = sum(minIntrVec(:))/prod(size(minIntrVec));
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50
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51 % close(h);
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52
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53
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