├── .gitattributes
├── .github
└── ISSUE_TEMPLATE
│ ├── bug_report.md
│ └── feature_request.md
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── docs
├── _config.yml
├── alpha.png
├── c++.png
├── c.png
├── demoicon.gif
├── down.png
├── doxysearch.php
├── fortran.png
├── hp.png
├── index.html
├── left.png
├── linux.png
├── m2html.css
├── matlabicon.gif
├── menu.html
├── mex.png
├── mrf
│ ├── Dictionary_Matching
│ │ ├── Function
│ │ │ ├── MRF_Dict_Matching.html
│ │ │ └── menu.html
│ │ ├── MRF_dict_matching_demo.html
│ │ └── menu.html
│ ├── EPG_Dict_Sim
│ │ ├── Function
│ │ │ ├── EPG_custom.html
│ │ │ ├── EPGsim_MRF.html
│ │ │ ├── Var_TE.html
│ │ │ ├── display_epg.html
│ │ │ ├── findEchoes.html
│ │ │ ├── menu.html
│ │ │ ├── relax.html
│ │ │ ├── rf_rotation.html
│ │ │ └── shift_grad.html
│ │ ├── dict_sim_MRF_demo.html
│ │ └── menu.html
│ ├── Image_Reconstruction
│ │ ├── Function
│ │ │ ├── Complex_Chan_Combining.html
│ │ │ ├── MRF_recon.html
│ │ │ ├── dat2mat_nonCart.html
│ │ │ ├── mapVBVDVE.html
│ │ │ ├── menu.html
│ │ │ ├── read_twix_hdr.html
│ │ │ ├── twix_map_obj.html
│ │ │ └── twix_map_objVE.html
│ │ ├── MRF_recon_demo.html
│ │ └── menu.html
│ ├── ROI_analysis_tool
│ │ ├── Function
│ │ │ ├── Circle_Analysis.html
│ │ │ ├── NIST_ROI_ana.html
│ │ │ ├── gen_NIST_sphere_loc.html
│ │ │ ├── gen_NIST_std_par.html
│ │ │ └── menu.html
│ │ ├── ROI_ana_demo.html
│ │ └── menu.html
│ ├── Sequence_Design
│ │ ├── Function
│ │ │ ├── design_spiral_pulseq.html
│ │ │ ├── gen_MRF_sequence_pulseq.html
│ │ │ ├── gradient_lobe.html
│ │ │ ├── gyrogamma.html
│ │ │ ├── menu.html
│ │ │ ├── vds.html
│ │ │ └── voronoi_area.html
│ │ ├── menu.html
│ │ └── sequence_design_demo.html
│ ├── demo_MRF.html
│ └── menu.html
├── pcode.png
├── right.png
├── sgi.png
├── simulinkicon.gif
├── solaris.png
├── up.png
└── windows.png
├── images
├── Brain_rec_all.jpg
├── MRF_FA_all.png
├── MRF_TR_all.png
├── Seq file.PNG
├── Sequence plot.png
├── Sliding window.png
├── Spiral plot.png
├── T2_map_sphere_1.png
├── T2_map_with_ROI.png
├── comparison.png
├── dict_evo.png
├── select raw data.png
└── select traj data.png
└── mrf
├── Dictionary_Matching
├── Data
│ └── dict_example.mat
├── Function
│ └── MRF_Dict_Matching.m
└── MRF_dict_matching_demo.m
├── EPG_Dict_Sim
├── Data
│ └── TR_TE_FA.mat
├── Function
│ ├── EPG_custom.m
│ ├── EPGsim_MRF.m
│ ├── display_epg.m
│ ├── findEchoes.m
│ ├── gen_MRF_dict.m
│ ├── relax.m
│ ├── rf_rotation.m
│ └── shift_grad.m
└── gen_MRF_dict_demo.m
├── Image_Reconstruction
├── Data
│ └── K_Traj4rec.mat
├── Function
│ ├── Complex_Chan_Combining.m
│ ├── MRF_recon.m
│ ├── dat2mat_nonCart.m
│ ├── mapVBVDVE.m
│ ├── read_twix_hdr.m
│ ├── twix_map_obj.m
│ └── twix_map_objVE.m
└── MRF_recon_demo.m
├── ROI_analysis_tool
├── Data
│ ├── T1_map.mat
│ ├── T1_map_rot45.mat
│ ├── T2_map.mat
│ └── T2_map_rot30.mat
├── Function
│ ├── Circle_Analysis.m
│ ├── NIST_ROI_ana.m
│ ├── gen_NIST_sphere_loc.m
│ └── gen_NIST_std_par.m
└── ROI_ana_demo.m
├── Sequence_Design
├── Data
│ └── method_orig.mat
├── Function
│ ├── design_spiral_pulseq.m
│ ├── gen_MRF_sequence_pulseq.m
│ ├── gradient_lobe.m
│ ├── gyrogamma.m
│ ├── make_HSI.m
│ ├── vds.m
│ └── voronoi_area.m
└── sequence_design_demo.m
└── demo_MRF.m
/.gitattributes:
--------------------------------------------------------------------------------
1 | *.dat filter=lfs diff=lfs merge=lfs -text
2 | *.gif filter=lfs diff=lfs merge=lfs -text
3 |
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/.github/ISSUE_TEMPLATE/bug_report.md:
--------------------------------------------------------------------------------
1 | ---
2 | name: Bug report
3 | about: Create a report to help us improve
4 | title: ''
5 | labels: ''
6 | assignees: ''
7 |
8 | ---
9 |
10 | **Describe the bug**
11 | A clear and concise description of what the bug is.
12 |
13 | **To Reproduce**
14 | Steps to reproduce the behavior:
15 | 1. Go to '...'
16 | 2. Click on '....'
17 | 3. Scroll down to '....'
18 | 4. See error
19 |
20 | **Expected behavior**
21 | A clear and concise description of what you expected to happen.
22 |
23 | **Screenshots**
24 | If applicable, add screenshots to help explain your problem.
25 |
26 | **Desktop (please complete the following information):**
27 | - OS: [e.g. iOS]
28 | - Browser [e.g. chrome, safari]
29 | - Version [e.g. 22]
30 |
31 | **Smartphone (please complete the following information):**
32 | - Device: [e.g. iPhone6]
33 | - OS: [e.g. iOS8.1]
34 | - Browser [e.g. stock browser, safari]
35 | - Version [e.g. 22]
36 |
37 | **Additional context**
38 | Add any other context about the problem here.
39 |
--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/feature_request.md:
--------------------------------------------------------------------------------
1 | ---
2 | name: Feature request
3 | about: Suggest an idea for this project
4 | title: ''
5 | labels: ''
6 | assignees: ''
7 |
8 | ---
9 |
10 | **Is your feature request related to a problem? Please describe.**
11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
12 |
13 | **Describe the solution you'd like**
14 | A clear and concise description of what you want to happen.
15 |
16 | **Describe alternatives you've considered**
17 | A clear and concise description of any alternative solutions or features you've considered.
18 |
19 | **Additional context**
20 | Add any other context or screenshots about the feature request here.
21 |
--------------------------------------------------------------------------------
/CODE_OF_CONDUCT.md:
--------------------------------------------------------------------------------
1 | # Contributor Covenant Code of Conduct
2 |
3 | ## Our Pledge
4 |
5 | In the interest of fostering an open and welcoming environment, we as
6 | contributors and maintainers pledge to making participation in our project and
7 | our community a harassment-free experience for everyone, regardless of age, body
8 | size, disability, ethnicity, sex characteristics, gender identity and expression,
9 | level of experience, education, socio-economic status, nationality, personal
10 | appearance, race, religion, or sexual identity and orientation.
11 |
12 | ## Our Standards
13 |
14 | Examples of behavior that contributes to creating a positive environment
15 | include:
16 |
17 | * Using welcoming and inclusive language
18 | * Being respectful of differing viewpoints and experiences
19 | * Gracefully accepting constructive criticism
20 | * Focusing on what is best for the community
21 | * Showing empathy towards other community members
22 |
23 | Examples of unacceptable behavior by participants include:
24 |
25 | * The use of sexualized language or imagery and unwelcome sexual attention or
26 | advances
27 | * Trolling, insulting/derogatory comments, and personal or political attacks
28 | * Public or private harassment
29 | * Publishing others' private information, such as a physical or electronic
30 | address, without explicit permission
31 | * Other conduct which could reasonably be considered inappropriate in a
32 | professional setting
33 |
34 | ## Our Responsibilities
35 |
36 | Project maintainers are responsible for clarifying the standards of acceptable
37 | behavior and are expected to take appropriate and fair corrective action in
38 | response to any instances of unacceptable behavior.
39 |
40 | Project maintainers have the right and responsibility to remove, edit, or
41 | reject comments, commits, code, wiki edits, issues, and other contributions
42 | that are not aligned to this Code of Conduct, or to ban temporarily or
43 | permanently any contributor for other behaviors that they deem inappropriate,
44 | threatening, offensive, or harmful.
45 |
46 | ## Scope
47 |
48 | This Code of Conduct applies both within project spaces and in public spaces
49 | when an individual is representing the project or its community. Examples of
50 | representing a project or community include using an official project e-mail
51 | address, posting via an official social media account, or acting as an appointed
52 | representative at an online or offline event. Representation of a project may be
53 | further defined and clarified by project maintainers.
54 |
55 | ## Enforcement
56 |
57 | Instances of abusive, harassing, or otherwise unacceptable behavior may be
58 | reported by contacting the project team at imr.framework2018@gmail.com . All
59 | complaints will be reviewed and investigated and will result in a response that
60 | is deemed necessary and appropriate to the circumstances. The project team is
61 | obligated to maintain confidentiality with regard to the reporter of an incident.
62 | Further details of specific enforcement policies may be posted separately.
63 |
64 | Project maintainers who do not follow or enforce the Code of Conduct in good
65 | faith may face temporary or permanent repercussions as determined by other
66 | members of the project's leadership.
67 |
68 | ## Attribution
69 |
70 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
71 | available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
72 |
73 | [homepage]: https://www.contributor-covenant.org
74 |
75 | For answers to common questions about this code of conduct, see
76 | https://www.contributor-covenant.org/faq
77 |
--------------------------------------------------------------------------------
/CONTRIBUTING.md:
--------------------------------------------------------------------------------
1 | # Contributing to Open Source MRF Package
2 | Thank you for taking the time to contribute to the package!
3 |
4 | The following is a set of guidelines for contributing to Open Source MRF Package. These are mostly guidelines, not rules. Use your best judgment, and feel free to propose changes to this document in a pull request.
5 |
6 | ## Table of contents
7 | 1. [Contributing](#contributing-to-the-software)
8 | 2. [Reporting Issues](#reporting-issues)
9 | 3. [Seeking Support](#seeking-support)
10 |
11 | ## Contributing to the Software
12 |
13 | If you would like to contribute to Open Source MRF Package, please fork and make a pull request with adequate documentation of functionality. In addition, please take a look at the Code of Conduct and documentation guide below:
14 |
15 | **Code of Conduct**
16 |
17 | This project and everyone participating in it is governed by the [Code of Conduct](https://github.com/imr-framework/mrf/blob/master/CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code. Please report unacceptable behavior to imr.framework2018@github.com.
18 |
19 | **Source code header**
20 |
21 | Insert and fill out the following header comments at the top of every script in your source code:
22 | ```
23 | Institution : (your university/research facility)
24 | Version : 1.0.0
25 | ```
26 | Note that "Version" refers to the Open Source MRF Package release you developed your code on. You can add more fields if needed.
27 |
28 | **Documenting source code**
29 |
30 | Please add a top-level description of code functionality in each script. In addition, document all inputs and outputs and add a line of description for each input and output.
31 |
32 | ## Reporting Issues
33 | Please report issues in Github Issues, following the given format for either "Bug Report" or "Feature Request".
34 |
35 | ## Seeking Support
36 | For support in installing and using Open Source MRF Package, please feel free to either use Github Issues.
37 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 |
Open Source Magnetic Resonance Fingerprinting Package (OMEGA)
5 |
6 | Open Source MRF is a Matlab based software package which enables vendor neutral, fast prototyping of magnetic resonance fingerprinting using Pulseq. It is supported by open-soure standards.
7 |
8 | Magnetic resonance fingerprinting is a framework that allows for simultaneous quantification of tissue properties and hence is a significant tool to understand multi-vendor multi-site variability. However, a vendor-neutral, open soure implementation of MRF has not been developed to the best of our knowlegde. In this work, we develop the package to enable comparisons between two sites with two vendors.
9 |
10 | Open Source MRF consists of five modules. Sequence design, image reconstruction, dictionary simulation, dictionary matching, and ROI analysis.
11 |
12 | **Please note:** You need to install Pulseq and Michigan Image Reconstruction Toolbox (MIRT) in your system for the package to run. Visit [Pulseq](http://pulseq.github.io/) and [MIRT](https://web.eecs.umich.edu/~fessler/code/) for more information.
13 |
14 | This package contains five different parts. To use the package, please pull the complete mrf repository.
15 |
16 | ## How to use the package
17 | #### Generate MRF Sequence
18 | 1. Call **Spiral_Design/Function/gen_MRF_sequence_pulseq.m**. The function returns parameters of the sequence that are used in other parts of the package
19 | 2. A MRF sequence based on Jiang's paper [1] is implemented using Pulseq framework.
20 | 3. The user should be able play the generated .seq file on a scanner with Pulseq interpreter installed. This sequence has been verified on a scanner by the author.
21 |
22 | #### Image Reconstruction
23 | 1. Call **Image_Reconstruction/Function/MRF_recon.m**
24 | 2. The script lets user select raw data file (.dat) from scanner and trajectory file (.mat) and reconstruct the image using sliding window method and complex channel combination [2].
25 |
26 | #### EPG Dictionary Simulation
27 | 1. Call **EPG_dict_sim/Function/gen_MRF_dict.m**.
28 | 2. A dictionary is simulated based on the T1/T2 ranges using Extend Phase Graph(EPG) [3] method.
29 |
30 | #### Dictionary Matching
31 | 1. Call **Dictionary_Matching/Function/MRF_Dic_Matching.m**.
32 | 2. The script uses vector dot product to find the best matching dictionary entry and retrieves all parameters for that entry.
33 |
34 | #### ROI Analysis Tool
35 | 1. Call **ROI_analysis_tool/Function/NIST_ROI_ana.m** .
36 | 2. The function takes in the parameter maps of ISMRM/NIST phantom and return locations of spheres in correct orders.
37 |
38 | ## Demo
39 | To test the package, a demo script can be found at **mrf/demo_MRF.m**. This script generates a sequence, then read a sample data, reconstruct the data. A dictionary is generated and dictionary matching is performed. Then ROI analysis is done to provide T1 and T2 values for each spheres.
40 |
41 | In addition to the overall demo script, each module can be tested by itself. The demo script can be located under each module, which will help verify the correctness of the module.
42 |
43 | ## Reference
44 | [1] Jiang Y, Ma D, Seiberlich N, Gulani V, Griswold M. MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magn Reson Med. 2015;74(6):spcone-spcone. doi:10.1002/mrm.26048
45 |
46 | [2] Roemer P, Edelstein W, Hayes C, Souza S, Mueller O. The NMR phased array. Magn Reson Med. 1990;16(2):192-225. doi:10.1002/mrm.1910160203
47 |
48 | [3] Weigel M. Extended phase graphs: Dephasing, RF pulses, and echoes - pure and simple. Journal of Magnetic Resonance Imaging. 2014;41(2):266-295. doi:10.1002/jmri.24619
49 |
50 |
51 |
52 |
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1 | $word,
70 | "match"=>$w,
71 | "index"=>$statIdx,
72 | "full"=>strlen($w)==strlen($word),
73 | "docs"=>array()
74 | );
75 | }
76 | $w = readString($file);
77 | }
78 | $totalFreq=0;
79 | for ($count=$start;$count$idx,"freq"=>$freq,"rank"=>0.0);
91 | $totalFreq+=$freq;
92 | if ($statInfo["full"]) $totalfreq+=$freq;
93 | }
94 | // read name an url info for the doc
95 | for ($i=0;$i<$numDocs;$i++)
96 | {
97 | fseek($file,$docInfo[$i]["idx"]);
98 | $docInfo[$i]["name"]=readString($file);
99 | $docInfo[$i]["url"]=readString($file);
100 | }
101 | $statInfo["docs"]=$docInfo;
102 | }
103 | for ($count=$start;$count$key,
136 | "name"=>$di["name"],
137 | "rank"=>$rank
138 | );
139 | }
140 | $docs[$key]["words"][] = array(
141 | "word"=>$wordInfo["word"],
142 | "match"=>$wordInfo["match"],
143 | "freq"=>$di["freq"]
144 | );
145 | }
146 | }
147 | return $docs;
148 | }
149 |
150 | function normalize_ranking(&$docs)
151 | {
152 | $maxRank = 0.0000001;
153 | // compute maximal rank
154 | foreach ($docs as $doc)
155 | {
156 | if ($doc["rank"]>$maxRank)
157 | {
158 | $maxRank=$doc["rank"];
159 | }
160 | }
161 | reset($docs);
162 | // normalize rankings
163 | while (list ($key, $val) = each ($docs))
164 | {
165 | $docs[$key]["rank"]*=100/$maxRank;
166 | }
167 | }
168 |
169 | function filter_results($docs,&$requiredWords,&$forbiddenWords)
170 | {
171 | $filteredDocs=array();
172 | while (list ($key, $val) = each ($docs))
173 | {
174 | $words = &$docs[$key]["words"];
175 | $copy=1; // copy entry by default
176 | if (sizeof($requiredWords)>0)
177 | {
178 | foreach ($requiredWords as $reqWord)
179 | {
180 | $found=0;
181 | foreach ($words as $wordInfo)
182 | {
183 | $found = $wordInfo["word"]==$reqWord;
184 | if ($found) break;
185 | }
186 | if (!$found)
187 | {
188 | $copy=0; // document contains none of the required words
189 | break;
190 | }
191 | }
192 | }
193 | if (sizeof($forbiddenWords)>0)
194 | {
195 | foreach ($words as $wordInfo)
196 | {
197 | if (in_array($wordInfo["word"],$forbiddenWords))
198 | {
199 | $copy=0; // document contains a forbidden word
200 | break;
201 | }
202 | }
203 | }
204 | if ($copy) $filteredDocs[$key]=$docs[$key];
205 | }
206 | return $filteredDocs;
207 | }
208 |
209 | function compare_rank($a,$b)
210 | {
211 | return ($a["rank"]>$b["rank"]) ? -1 : 1;
212 | }
213 |
214 | function sort_results($docs,&$sorted)
215 | {
216 | $sorted = $docs;
217 | usort($sorted,"compare_rank");
218 | return $sorted;
219 | }
220 |
221 | function report_results(&$docs)
222 | {
223 | echo "
% This function is a re-implementation based on P. Gomez's code
24 |
25 |
SYNOPSIS
26 |
function out = MRF_Dict_Matching(dict, data)
27 |
28 |
DESCRIPTION
29 |
% This function is a re-implementation based on P. Gomez's code
30 | This script uses vector dot product to find the most similar entry inside
31 | a dictionary and retrieve the parameters from that dictionary.
32 | INPUT
33 | dict Dictionary file
34 | data Reconstructed data file
35 |
36 | OUTPUT
37 | out An output structure that contains T1 and T2 maps
38 |
39 | 7/2019 Enlin Qian
40 | # Copyright of the Board of Trustees of Columbia University in the City of New York
41 | % Reshape data
42 |
43 |
44 |
CROSS-REFERENCE INFORMATION
45 | This function calls:
46 |
47 |
48 | This function is called by:
49 |
50 |
MRF_dict_matching_demo This script is used as a demo for MRF_Dict_Matching.m. The function takes in a
51 |
52 |
53 |
54 |
55 |
SOURCE CODE
56 |
0001 function out = MRF_Dict_Matching(dict, data)
57 | 0002 %% This function is a re-implementation based on P. Gomez's code
58 | 0003 %This script uses vector dot product to find the most similar entry inside
59 | 0004 %a dictionary and retrieve the parameters from that dictionary.
60 | 0005 % INPUT
61 | 0006 % dict Dictionary file
62 | 0007 % data Reconstructed data file
63 | 0008 %
64 | 0009 % OUTPUT
65 | 0010 % out An output structure that contains T1 and T2 maps
66 | 0011 %
67 | 0012 % 7/2019 Enlin Qian
68 | 0013 % # Copyright of the Board of Trustees of Columbia University in the City of New York
69 | 0014 %% Reshape data
70 | 0015 iz = 1;
71 | 0016 [ix,iy,T] = size(data);
72 | 0017 datadims = [ix,iy,T];
73 | 0018 N = ix*iy*iz;
74 | 0019 x = reshape(data,[N,T]);
75 | 0020 mask = ones(datadims(1:end-1)) > 0;
76 | 0021 x = single(x(mask>0,:));
77 | 0022
78 | 0023 %% Reading in dictionary and checking fields in dictionary
79 | 0024 D = dict.D;
80 | 0025 if isfield(dict,'normD'); normD = dict.normD; end
81 | 0026 if ~isfield(dict,'lut')
82 | 0027 error('dict.lut required for parameter estimation');
83 | 0028 end
84 | 0029 if ~exist('normD','var')
85 | 0030 warning('normD not found, normalizing dictionary \n');
86 | 0031 normD = zeros(1,size(D,1));
87 | 0032 for l = 1:size(D,1)
88 | 0033 normD(l)=norm(D(l,:));
89 | 0034 D(l,:)=D(l,:)/normD(l);
90 | 0035 end
91 | 0036 D(isnan(D))=0;
92 | 0037 end
93 | 0038
94 | 0039 for n1 = 1:size(D,1)
95 | 0040 temp1 = D(n1,:);
96 | 0041 temp2 = x(n1,:);
97 | 0042 temp1 = temp1./max(temp1(:));
98 | 0043 temp2 = temp2./max(temp2(:));
99 | 0044 D(n1,:) = temp1;
100 | 0045 x(n1,:) = temp2;
101 | 0046 end
102 | 0047
103 | 0048 %% Setting up parameters for dic matching
104 | 0049 S = whos('x');
105 | 0050 dataBytes = S.bytes;
106 | 0051 mem = memory;
107 | 0052 percentageOfUse = 0.6;
108 | 0053 memPercentage = mem.MemAvailableAllArrays * percentageOfUse;
109 | 0054 blockSize = memPercentage/dataBytes;
110 | 0055
111 | 0056 %% Dictionary Matching
112 | 0057 blockSize = min(max(floor(blockSize/size(D,1)),1),size(x,1));
113 | 0058 iter = ceil(size(x,1)/blockSize);
114 | 0059 mt = zeros(size(x,1),1);
115 | 0060 dm = zeros(size(x,1),1);
116 | 0061 pd = zeros(size(x,1),1);
117 | 0062 X = zeros(size(x));
118 | 0063 fprintf('Matching data \n');
119 | 0064 maxDict = max(dict.D,[],2);
120 | 0065 dtemp = abs(dict.D./maxDict);
121 | 0066 for i = 1: iter
122 | 0067 if(mod(i,1000) ==0)
123 | 0068 disp(i)
124 | 0069 end
125 | 0070 if i<iter
126 | 0071 cind = (i-1)*blockSize+1:i*blockSize;
127 | 0072 else
128 | 0073 cind = (i-1)*blockSize+1:size(x,1);
129 | 0074 end
130 | 0075 xtemp = x(cind,:)./max(abs(x(cind,:)));
131 | 0076 ip = zeros(1,size(D,1));
132 | 0077 for m = 1:size(D,1)
133 | 0078 ip(m) = sum(abs(dtemp(m, :).^2 - xtemp.^2));
134 | 0079 end
135 | 0080 [mt(cind),dm(cind)] = min(abs(ip.'),[],1);
136 | 0081 if(mod(i, 100)==0)
137 | 0082 disp(i);
138 | 0083 disp(dm(cind));
139 | 0084 end
140 | 0085 end
141 | 0086 clear match;
142 | 0087 clear dictentry;
143 | 0088
144 | 0089 %% Generate output
145 | 0090 Q=6;
146 | 0091 dm(dm==0) =1;
147 | 0092 dict.lut(1,:) =0;
148 | 0093
149 | 0094 qmap = zeros(N,Q,'single');
150 | 0095 qmap(mask>0,:) = dict.lut(dm,:);
151 | 0096 qmap(isnan(qmap)) = 0; %remove possible NaN from infeasible search window
152 | 0097 out.qmap = reshape(qmap,[datadims(1:end-1),Q]);
153 | 0098 out.mask = mask;
154 | 0099 figure; imagesc(abs(squeeze(out.qmap(:,:,1)))); axis equal tight; colormap hot; title('T1 map');
155 | 0100 figure; imagesc(abs(squeeze(out.qmap(:,:,2)))); axis equal tight; colormap hot; title('T2 map');
156 | 0101 end
This script is used as a demo for MRF_Dict_Matching.m. The function takes in a
24 |
25 |
SYNOPSIS
26 |
This is a script file.
27 |
28 |
DESCRIPTION
29 |
This script is used as a demo for MRF_Dict_Matching.m. The function takes in a
30 | .mat dictionary file and .mat recontructed image file and using vector dot
31 | product to find the most similar dictionary entry.
32 | The .mat dictionary file can be located in Dictionary_Matching/Data, while
33 | the reconstructed image data file is too large to be included in a Github
34 | repository. They can be found in Google drive. Shareable link are below.
35 | T1 Array slice: https://drive.google.com/open?id=1G4hsc00CS5ycbnQ6KQa8YDlkis_Yc-5f
36 | G2 Array slice: https://drive.google.com/open?id=199m40D-eiPWnAMtcNZj6gIM0aDZ-VZoB
37 |
38 | 8/2019 Enlin Qian
39 | # Copyright of the Board of Trustees of Columbia University in the City of New York
40 |
41 |
42 |
CROSS-REFERENCE INFORMATION
43 | This function calls:
44 |
45 |
MRF_Dict_Matching % This function is a re-implementation based on P. Gomez's code
46 | This function is called by:
47 |
48 |
49 |
50 |
51 |
52 |
53 |
SOURCE CODE
54 |
0001 % This script is used as a demo for MRF_Dict_Matching.m. The function takes in a
55 | 0002 % .mat dictionary file and .mat recontructed image file and using vector dot
56 | 0003 % product to find the most similar dictionary entry.
57 | 0004 % The .mat dictionary file can be located in Dictionary_Matching/Data, while
58 | 0005 % the reconstructed image data file is too large to be included in a Github
59 | 0006 % repository. They can be found in Google drive. Shareable link are below.
60 | 0007 % T1 Array slice: https://drive.google.com/open?id=1G4hsc00CS5ycbnQ6KQa8YDlkis_Yc-5f
61 | 0008 % G2 Array slice: https://drive.google.com/open?id=199m40D-eiPWnAMtcNZj6gIM0aDZ-VZoB
62 | 0009 %
63 | 0010 % 8/2019 Enlin Qian
64 | 0011 % # Copyright of the Board of Trustees of Columbia University in the City of New York
65 | 0012
66 | 0013 %% Image Reconstruction
67 | 0014 addpath(genpath('.'));
68 | 0015 data = image_data_final_Complex;
69 | 0016 clear image_data_final_Complex;
70 | 0017 out = MRF_Dict_Matching(dict, data);
[om_store,echoes] = EPG_custom(seq)
30 | Performs EPG simulation of a general pulse sequence
31 | INPUTS
32 | seq: sequence struct with the required fields
33 | seq.rf - 2 x P matrix with each column = [phi,alpha]'
34 | seq.grad - vector of k-shifts (must be integers)
35 | seq.events - cell of strings: 'rf','grad', or 'relax'
36 | seq.time - vector of timing for each event in seq.events
37 | seq.T1, seq.T2 : T1 and T2 values for relaxation
38 | (set both to 0 for no relaxation)
39 | OUTPUTS
40 |
41 | om_store - 1 x Q cell array of matrices with size (3 x K).
42 | Each matrix is a record of configuration states (F+(k),F-(k),Z(k)
43 | at all k's generated up to that point. Each matrix corresponds to
44 | the elements in seq.events and seq.timing at the same index.
45 |
46 | echoes - output of echo information using findEchoes
echoes = FINDECHOES(seq,om_store)
30 | Finds echo timings and intensities given sequence seq and
31 | om_store (the later being generated by EPG_custom.m)
32 |
33 | We only find the proper and unique echoes
34 | Criteria:
35 | (a) The F(0) component must be non-zero (>5*eps)
36 | (b) If there are multiple echoes satisfying (a) at the same timing,
37 | use the last value. So for grad->relax->RF, we only get F(0) after
38 | the RF, and for grad->relax, we only get F(0) after the relaxation.
39 |
40 | output: echoes is N x 2 matrix where N is the number of echoes found
41 | 1st col - timing; 2nd col - intensity (modulus of F(0))
0001 function echoes = findEchoes(seq,om_store)
57 | 0002 %echoes = FINDECHOES(seq,om_store)
58 | 0003 % Finds echo timings and intensities given sequence seq and
59 | 0004 % om_store (the later being generated by EPG_custom.m)
60 | 0005 %
61 | 0006 % We only find the proper and unique echoes
62 | 0007 % Criteria:
63 | 0008 % (a) The F(0) component must be non-zero (>5*eps)
64 | 0009 % (b) If there are multiple echoes satisfying (a) at the same timing,
65 | 0010 % use the last value. So for grad->relax->RF, we only get F(0) after
66 | 0011 % the RF, and for grad->relax, we only get F(0) after the relaxation.
67 | 0012 %
68 | 0013 % output: echoes is N x 2 matrix where N is the number of echoes found
69 | 0014 % 1st col - timing; 2nd col - intensity (modulus of F(0))
70 | 0015
71 | 0016 echoes = [];
72 | 0017 timing = seq.time;
73 | 0018 for v = 1:length(om_store)
74 | 0019 if abs(om_store{v}(1,1)) > 5*eps %&& sum(rftimes == timing(v))==0
75 | 0020 newecho = [timing(v),abs(om_store{v}(1,1))];
76 | 0021 if ~isempty(echoes) && echoes(end,1) == timing(v)
77 | 0022 % same timing - always take the 2nd one
78 | 0023 echoes = [echoes(1:end-1,:);newecho];
79 | 0024 else
80 | 0025 echoes = [echoes;newecho];
81 | 0026 end
82 | 0027
83 | 0028 end
84 | 0029 end
85 | 0030 echoes = unique(echoes,'rows');
86 | 0031 end
87 | 0032
relax(tau,T1,T2,omega)
30 | relax.m: updates omega with relaxation effects
31 | INPUTS
32 | tau: duration of relaxation in ms
33 | T1,T2: time constants in ms
34 | omega: the input (3 x n) matrix of k-states
RF_ROTATION(phi,alpha)
30 | 3 x 3 rotation matrix in complex representation for RF pulse
31 | with phase = phi (from the x axis) and filp angle = alpha
32 | See Weigel 2015, Eq. 15
SHIFT_GRAD(delk,omega)
30 | Modified by: Gehua Tong
31 | Date: 15 Oct 2018
32 | Modified by: Sachin A B Anchan
33 | Date: 30 June 2014
34 | Shift applies to only F+ and F-* as it does not dephase in z
35 | check size of previous omega to determine the effect - test multiple
36 | times
This script reads in .dat raw data and convert it to kspace data.
24 |
25 |
SYNOPSIS
26 |
function [kspace_data]= dat2mat_nonCart()
27 |
28 |
DESCRIPTION
29 |
This script reads in .dat raw data and convert it to kspace data.
30 | IUPUT
31 | None the user is asked to select a .dat raw data file
32 |
33 | OUTPUT
34 | ksapce_data kspace data
35 |
36 | Created 8/2013 Sairam Geethanath
37 | Modified 7/2019 Enlin Qian
38 | # Copyright of the Board of Trustees of Columbia University in the City of New York
39 |
40 |
41 |
CROSS-REFERENCE INFORMATION
42 | This function calls:
43 |
44 |
mapVBVDVE This script reads Siemens raw .dat file from VB/VD MRI raw data.
45 | This function is called by:
46 |
47 |
MRF_recon This script uses sliding window method and complex coil combination to
48 |
49 |
50 |
51 |
52 |
SOURCE CODE
53 |
0001 function [kspace_data]= dat2mat_nonCart()
54 | 0002 % This script reads in .dat raw data and convert it to kspace data.
55 | 0003 % IUPUT
56 | 0004 % None the user is asked to select a .dat raw data file
57 | 0005 %
58 | 0006 % OUTPUT
59 | 0007 % ksapce_data kspace data
60 | 0008 %
61 | 0009 % Created 8/2013 Sairam Geethanath
62 | 0010 % Modified 7/2019 Enlin Qian
63 | 0011 % # Copyright of the Board of Trustees of Columbia University in the City of New York
64 | 0012
65 | 0013 %% Obtain the file
66 | 0014 [Filename,Pathname] = uigetfile('*.dat','Pick the raw data file');
67 | 0015
68 | 0016 %% Read data using mapVBVD
69 | 0017 twix_obj = mapVBVDVE(fullfile(Pathname,Filename));
70 | 0018
71 | 0019 % image_obj = twix_obj.image; %Body coil
72 | 0020 if(~iscell(twix_obj))
73 | 0021 % if ( size(twix_obj.image) ==1)
74 | 0022 image_obj = twix_obj.image;
75 | 0023 else
76 | 0024 image_obj = twix_obj{2}.image;
77 | 0025 end
78 | 0026
79 | 0027 sizeData = image_obj.sqzSize; %kx, nch, ky, slices, partitions
80 | 0028 dimsData = image_obj.sqzDims;
81 | 0029 dimsallData = image_obj.dataDims;
82 | 0030
83 | 0031 %% Concatenate slices and partitions together and has a N x N acquisition set
84 | 0032 kspace_data = zeros(sizeData);
85 | 0033
86 | 0034 sl = 1;
87 | 0035
88 | 0036 partitions=1;
89 | 0037
90 | 0038 %% Determine k-space shift to the center
91 | 0039 % temp = squeeze(image_obj(:,1,:,round(sizeData(4)/2),round(sizeData(5)/2)));
92 | 0040 % [~,idx] = max(abs(temp(:)));
93 | 0041 % [idx,idy] = ind2sub(size(temp),idx);
94 | 0042
95 | 0043 %%
96 | 0044
97 | 0045
98 | 0046 tic;
99 | 0047 % for nch=1:size(kspace_data,2) %number of channels
100 | 0048
101 | 0049 % for narms=1:size(kspace_data,1) %number of arms
102 | 0050 kspace_data = squeeze(image_obj());
103 | 0051
104 | 0052 % temp = squeeze(image_obj(:,nch,:,sl,partitions));
105 | 0053 % temp = circshift(temp, [round(size(temp,1)/2) - idx,round(size(temp,2)/2) - idy ]);
106 | 0054 % trunc_part =round(0.5 .*(size(temp,1) - size(temp,2)));
107 | 0055 % temp = squeeze(temp(trunc_part+1:end-trunc_part,:));
108 | 0056 %
109 | 0057 % kspace_data(:,:,nslices,nch) = temp;
110 | 0058
111 | 0059
112 | 0060
113 | 0061
114 | 0062 % end
115 | 0063 toc;
116 | 0064
117 | 0065 kspace_data = permute(kspace_data,[1 3 2 4]); %kx, ky, slices, partitions, nch
118 | 0066 kspace_data = squeeze(kspace_data);
119 | 0067
120 | 0068
121 | 0069
122 | 0070
123 | 0071
124 | 0072
125 | 0073
126 | 0074
127 | 0075
128 | 0076
129 | 0077
MRF_recon This script uses sliding window method and complex coil combination to
36 | This function is called by:
37 |
38 |
39 |
40 |
41 |
42 |
43 |
SOURCE CODE
44 |
0001 clc
45 | 0002 clear
46 | 0003 %This script is used as a demo for MRF_recon.m. The function takes in a
47 | 0004 %.dat raw data file and .mat trajectory file and using sliding windown
48 | 0005 %reconstruction and complex coil combination to reconstruct the image.
49 | 0006 %These two files can both be found under Image_Reconstruction/Data
50 | 0007 % 8/2019 Enlin Qian
51 | 0008 % # Copyright of the Board of Trustees of Columbia University in the City of New York
52 | 0009
53 | 0010 %% Image Reconstruction
54 | 0011 addpath(genpath('.'));
55 | 0012 image_data_final_Complex = MRF_recon();
Input center, radius, and map size and return index of pixels inside the
24 |
25 |
SYNOPSIS
26 |
function [CircleMap, PointsIndex] = Circle_Analysis(Center, Radius, ImXSize, ImYSize)
27 |
28 |
DESCRIPTION
29 |
Input center, radius, and map size and return index of pixels inside the
30 | circle.
31 | INPUT
32 | Center Center of circle [x, y]
33 | Radius Radius of circle (in pixels)
34 | ImXSize Image size along x direction
35 | ImYSize Image size along y direction
36 |
37 | OUTPUT
38 | CircleMap Binary map for each circle
39 | PointsIndex Pixel index for all pixels inside input circle
40 |
41 | 7/2019 Enlin Qian
42 | # Copyright of the Board of Trustees of Columbia University in the City of New York
43 | (x,y) = (colum, row)
44 |
45 |
46 |
CROSS-REFERENCE INFORMATION
47 | This function calls:
48 |
49 |
50 | This function is called by:
51 |
52 |
NIST_ROI_ana Input map mat file and return ROI analysis of a ISMRM/NIST phantom, all
53 |
54 |
55 |
56 |
57 |
SOURCE CODE
58 |
0001 function [CircleMap, PointsIndex] = Circle_Analysis(Center, Radius, ImXSize, ImYSize)
59 | 0002 % Input center, radius, and map size and return index of pixels inside the
60 | 0003 % circle.
61 | 0004 % INPUT
62 | 0005 % Center Center of circle [x, y]
63 | 0006 % Radius Radius of circle (in pixels)
64 | 0007 % ImXSize Image size along x direction
65 | 0008 % ImYSize Image size along y direction
66 | 0009 %
67 | 0010 % OUTPUT
68 | 0011 % CircleMap Binary map for each circle
69 | 0012 % PointsIndex Pixel index for all pixels inside input circle
70 | 0013 %
71 | 0014 % 7/2019 Enlin Qian
72 | 0015 % # Copyright of the Board of Trustees of Columbia University in the City of New York
73 | 0016 % (x,y) = (colum, row)
74 | 0017 [X,Y] = meshgrid(1:ImXSize,1:ImYSize);
75 | 0018 DistanceMap = sqrt((X-Center(1)).^2+(Y-Center(2)).^2);
76 | 0019 CircleMap = DistanceMap<=Radius;
77 | 0020 PointsIndex = find(CircleMap);
78 | 0021 end
Input map parameters and Sphere 1 loc to generate a map that finds all
24 |
25 |
SYNOPSIS
26 |
function NIST_sphere_loc = gen_NIST_sphere_loc(map_type, map_size, fov, Sphere1_center)
27 |
28 |
DESCRIPTION
29 |
Input map parameters and Sphere 1 loc to generate a map that finds all
30 | locations of other spheres.
31 | INPUT
32 | map_type type of map (T1 or T2)
33 | map_size size of map, for example, 128 means the map size is 128x128
34 | fov field of view [mm]
35 | Sphere1_loc centers of sphere 1 (x, y)
36 |
37 | OUTPUT
38 | NIST_sphere_loc sphere locations for all spheres
39 |
40 | 7/2019 Enlin Qian
41 | # Copyright of the Board of Trustees of Columbia University in the City of New York
42 |
43 |
44 |
CROSS-REFERENCE INFORMATION
45 | This function calls:
46 |
47 |
48 | This function is called by:
49 |
50 |
NIST_ROI_ana Input map mat file and return ROI analysis of a ISMRM/NIST phantom, all
51 |
52 |
53 |
54 |
55 |
SOURCE CODE
56 |
0001 function NIST_sphere_loc = gen_NIST_sphere_loc(map_type, map_size, fov, Sphere1_center)
57 | 0002 % Input map parameters and Sphere 1 loc to generate a map that finds all
58 | 0003 % locations of other spheres.
59 | 0004 % INPUT
60 | 0005 % map_type type of map (T1 or T2)
61 | 0006 % map_size size of map, for example, 128 means the map size is 128x128
62 | 0007 % fov field of view [mm]
63 | 0008 % Sphere1_loc centers of sphere 1 (x, y)
64 | 0009 %
65 | 0010 % OUTPUT
66 | 0011 % NIST_sphere_loc sphere locations for all spheres
67 | 0012 %
68 | 0013 % 7/2019 Enlin Qian
69 | 0014 % # Copyright of the Board of Trustees of Columbia University in the City of New York
70 | 0015 NIST_sphere_loc.num_spheres = 14;
71 | 0016 NIST_sphere_loc.voxel_size = fov/map_size; % unit in mm/voxel
72 | 0017 NIST_sphere_loc.temp_rot_center = [map_size/2, map_size/2]; % unit in pixel
73 | 0018 switch(map_type)
74 | 0019 case'T1'
75 | 0020 NIST_sphere_loc.dist_map = [0, 31.324, 58.893, 81.810, 95.898, 100.317,...
76 | 0021 95.991, 81.077, 58.860, 31.451, 35.380, 35.931, 73.124, 73.157]; % unit in mm
77 | 0022 NIST_sphere_loc.ang_map = [0, 70.0850, 53.3538, 35.3270, 16.9426, -0.6324,...
78 | 0023 -18.7705, -36.9326, -56.4287, -72.7011, 33.6653, -34.0284, 14.9729, -16.4091]; % unit in degree
79 | 0024
80 | 0025 case'T2'
81 | 0026 NIST_sphere_loc.dist_map = [0, 31.547, 58.642, 80.697, 95.713, 99.812,...
82 | 0027 96.050, 80.936, 58.824, 30.036, 36.204, 35.882, 73.002, 72.552]; % unit in mm
83 | 0028 NIST_sphere_loc.ang_map = [0, 71.4899, 54.0302, 35.7043, 17.5603, -0.1549,...
84 | 0029 -18.3830, -35.8010, -54.2831, -70.3638, 33.1854, -33.0284, 15.2130, -16.2861]; % unit in degree
85 | 0030 end
86 | 0031
87 | 0032 %% convert mm to voxel based on map_size and fov
88 | 0033 NIST_sphere_loc.dist_map = NIST_sphere_loc.dist_map/NIST_sphere_loc.voxel_size; % convert to unit in voxel
89 | 0034
90 | 0035 %% Use Sphere 1 loc to calculate template Sphere1 loc
91 | 0036 dist_Sphere12center = sqrt((Sphere1_center(1)- NIST_sphere_loc.temp_rot_center(1))^2+...
92 | 0037 (Sphere1_center(2)- NIST_sphere_loc.temp_rot_center(2))^2);
93 | 0038 NIST_sphere_loc.template_Sphere1_loc = [map_size/2, map_size/2-dist_Sphere12center]; % pixel
94 | 0039
95 | 0040 %% get coordinates for all spheres
96 | 0041 NIST_sphere_loc.template_loc = zeros(NIST_sphere_loc.num_spheres, 2);
97 | 0042 NIST_sphere_loc.template_loc(:, 1) = NIST_sphere_loc.template_Sphere1_loc(1, 1) +...
98 | 0043 NIST_sphere_loc.dist_map.*sind(NIST_sphere_loc.ang_map); % x axis
99 | 0044 NIST_sphere_loc.template_loc(:, 2) = NIST_sphere_loc.template_Sphere1_loc(1, 2) +...
100 | 0045 NIST_sphere_loc.dist_map.*cosd(NIST_sphere_loc.ang_map); % y axis
101 | 0046
Input map parameters and return phantom parameters based on map type, map
24 |
25 |
SYNOPSIS
26 |
function NIST_std_par = gen_NIST_std_par(map_type, map_size, fov)
27 |
28 |
DESCRIPTION
29 |
Input map parameters and return phantom parameters based on map type, map
30 | size and fov.
31 | INPUT
32 | map_type type of map (T1 or T2)
33 | map_size size of map, for example, 128 means the map size is 128x128
34 | fov field of view [mm]
35 |
36 | OUTPUT
37 | phan_std_par standard phantom parameters.
38 |
39 | 7/2019 Enlin Qian
40 | # Copyright of the Board of Trustees of Columbia University in the City of New York
41 |
42 |
43 |
CROSS-REFERENCE INFORMATION
44 | This function calls:
45 |
46 |
47 | This function is called by:
48 |
49 |
NIST_ROI_ana Input map mat file and return ROI analysis of a ISMRM/NIST phantom, all
50 |
51 |
52 |
53 |
54 |
SOURCE CODE
55 |
0001 function NIST_std_par = gen_NIST_std_par(map_type, map_size, fov)
56 | 0002 % Input map parameters and return phantom parameters based on map type, map
57 | 0003 % size and fov.
58 | 0004 % INPUT
59 | 0005 % map_type type of map (T1 or T2)
60 | 0006 % map_size size of map, for example, 128 means the map size is 128x128
61 | 0007 % fov field of view [mm]
62 | 0008 %
63 | 0009 % OUTPUT
64 | 0010 % phan_std_par standard phantom parameters.
65 | 0011 %
66 | 0012 % 7/2019 Enlin Qian
67 | 0013 % # Copyright of the Board of Trustees of Columbia University in the City of New York
68 | 0014 NIST_std_par.num_spheres = 14;
69 | 0015 NIST_std_par.voxel_size = fov/map_size; % unit in mm/voxel
70 | 0016 NIST_std_par.sphere_radius = 8; % unit in mm
71 | 0017 NIST_std_par.temp_rot_center = [map_size/2, map_size/2]; % unit in pixel
72 | 0018
73 | 0019 switch(map_type)
74 | 0020 case'T1'
75 | 0021 NIST_std_par.true_value = [1.989, 1.454, 0.9841, 0.706, 0.4967,...
76 | 0022 0.3515, 0.24713, 0.1753, 0.1259, 0.089, 0.0627, 0.04453, 0.03084, 0.021719];
77 | 0023 NIST_std_par.plane_radius = 170; % mm
78 | 0024
79 | 0025 case'T2'
80 | 0026 NIST_std_par.true_value = [0.5813, 0.4035, 0.2781, 0.19094, 0.13327,...
81 | 0027 0.09689, 0.06407, 0.04642, 0.03197, 0.02256, 0.015813, 0.011237, 0.007911, 0.005592];
82 | 0028 NIST_std_par.plane_radius = 195; % mm
83 | 0029 end
84 | 0030 NIST_std_par.intensity_range = [NIST_std_par.true_value(1)-0.2, NIST_std_par.true_value(1)+0.2];
85 | 0031
86 | 0032 %% convert mm to voxel based on map_size and fov
87 | 0033 NIST_std_par.sphere_radius = NIST_std_par.sphere_radius/NIST_std_par.voxel_size; % convert to unit in voxel
88 | 0034 NIST_std_par.plane_radius = NIST_std_par.plane_radius/NIST_std_par.voxel_size; % convert to unit in voxel
89 | 0035
90 | 0036 end
NIST_ROI_ana Input map mat file and return ROI analysis of a ISMRM/NIST phantom, all
36 | This function is called by:
37 |
38 |
39 |
40 |
41 |
42 |
43 |
SOURCE CODE
44 |
0001 clc
45 | 0002 clear
46 | 0003 %This script is used as a demo for NIST_ROI_ana.m. For now, four sets of
47 | 0004 %data are used for testing.
48 | 0005 % T1_map.mat T1_map data
49 | 0006 % T1_map_rot45.mat T1_map data rotated counterclockwise 45 degrees
50 | 0007 % T2_map.mat T2_map data
51 | 0008 % T2_map_rot30.mat T2_map data rotated counterclockwise 30 degree
52 | 0009 %
53 | 0010 % 7/2019 Enlin Qian
54 | 0011 % # Copyright of the Board of Trustees of Columbia University in the City of New York
55 | 0012
56 | 0013 %% Load data
57 | 0014 addpath(genpath('.'))
58 | 0015 load('T2_map_rot30.mat')
59 | 0016 sphere_par = NIST_ROI_ana(T2_map_rot, 'T2', 128, 210);
Calculate the area (density) of a k-space trajectory via Voronoi
24 |
25 |
SYNOPSIS
26 |
function dcf_out = voronoi_area(k)
27 |
28 |
DESCRIPTION
29 |
Calculate the area (density) of a k-space trajectory via Voronoi
30 | triangulation
31 | INPUT
32 | k k-space trajectory ([-0.5 0.5]*res)
33 |
34 | OUTPUT
35 | dcf_out area assosiated with each k-space point
36 |
37 | Created Florian Wiesinger
38 | Modified 7/2006 Rolf Schulte
gen_MRF_sequence_pulseq This script uses pulseq to generate a sequence proposed in Jiang's paper[1]
36 | This function is called by:
37 |
38 |
39 |
40 |
41 |
42 |
43 |
SOURCE CODE
44 |
0001 clc
45 | 0002 clear
46 | 0003 %This script is used as a demo for gen_MRF_sequence_pulse.m. The function
47 | 0004 %uses TR, TE and FA from Spiral_Design/Data/method_orig.mat to construct a
48 | 0005 %sequence.
49 | 0006 % 8/2019 Enlin Qian
50 | 0007 % # Copyright of the Board of Trustees of Columbia University in the City of New York
51 | 0008
52 | 0009 %% Sequence Design
53 | 0010 addpath(genpath('.'));
54 | 0011 [kshot, dcf, ind, TR_all, FA_all, TE_all] = gen_MRF_sequence_pulseq();
3 | 
