OSCR

Real-time volumetric imaging of cells and molecules in deep tissues with Takoyaki ultrasound.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches
  1. [1] § Methods › Takoyaki AM/B-mode sequences ↔ Code/SetupMatrix1024_15MHz_Takoyaki_LiveImaging.m, lines 1–38 · score 0.87 · sparse random apertures, full amplitude, half amplitude, transmission aperture, receive apertures, 3.3 mm
  2. [2] § Methods › Takoyaki BURST sequence ↔ Code/SetupMatrix1024_15MHz_Takoyaki_BURST.m, lines 1–47 · score 0.85 · BURST scheme, BURST script, half cycles, transmit aperture, receive aperture, accumulated
  3. [3] § Methods › Image similarity analysis ↔ Data/Fig2/a-h/similarity_analysis.m, lines 22–73 · score 0.81 · multi scale SSIM, MS SSIM, bounding box, transformation matrices, mask, phantoms
  4. [4] § Results › The Takoyaki sequence allows efficient acquisition of 3D volumes ↔ Code/SetupMatrix1024_15MHz_Takoyaki_LiveImaging.m, lines 1–38 · score 0.80 · sparse random apertures, full amplitude, half amplitude, transmit aperture, receive apertures, complementary
  5. [5] § Results › The Takoyaki sequence allows efficient acquisition of 3D volumes ↔ Code/SetupMatrix1024_15MHz_Takoyaki_BURST.m, lines 1–47 · score 0.80 · full amplitude, half amplitude, Takoyaki BURST, transmit aperture, receive apertures, sparse
  6. [6] § Methods › Image similarity analysis ↔ Data/Fig2/a-h/similarity_analysis.m, lines 1–19 · score 0.70 · Medical Registration, transformation matrices, registered mask, phantom, axis
  7. [7] § Methods › Takoyaki AM/B-mode sequences ↔ Code/SetupMatrix1024_15MHz_Takoyaki_LiveImaging.m, lines 112–171 · score 0.61 · longer jump caused, Frustum, compensate, gap, Takoyaki, depth
  8. [8] § Methods › Contrast-to-background ratio (CBR) calculation for in vitro experiments ↔ Data/Fig2/a-h/CBR_anisotropy.m, lines 54–153 · score 0.56 · eroded masks, rectangular, interspace, box, slice, CBR
  9. [9] § Results › The Takoyaki sequence allows efficient acquisition of 3D volumes ↔ Code/SetupMatrix1024_15MHz_Takoyaki_LiveImaging.m, lines 234–302 · score 0.52 · delay laws, focused beams, translated, matrix, Takoyaki, apertures

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 1,007 lines · 34 KB · no license · 4 matches

  1. %% Takoyaki live imaging script
  2. % This script scans multiple focal points simultaneously using the 1024 element 15 MHz
  3. % 2D matrix array probe from Vermon with the UTA 1024-MUX adapter on a
  4. % Vantage 256 system. All 4 banks are used for transmission. For receive apertures,
  5. % one complementary set of 4 sparse random apertures are used. The 4
  6. % receive apertures are synthesized for each transmit aperture.
  7. %
  8. % P.nCode = 3, Takoyaki AM / P.nCode = 1, Takoyaki Bmode
  9. % At the beginning, the sequence shows live imaging without saving images.
  10. % After clicking "save the images", it starts to store the images.
  11. %
  12. % To run this script, there are two required files:
  13. % computeTrans_MatrixProbeIncluded.m
  14. % saved100CompRndApod.mat
  15. %
  16. % written by Sunho Lee
  18. % Parameter settings
  19. clear;
  21. P.pathName = 'D:\Sunho\TakoyakiAM' ;
  22. P.numberImagesToSave = 2;
  24. P.startDepth_mm = 3.3;
  25. P.endDepth_mm = 10;
  26. P.Voltage = 1.6;
  28. P.numFrames = 2 ;
  30. P.numSyntheticRcvs = 4; % 4 receives for Fully-Sampled Array
  32. P.txFocus_mm = 7; % focal depth
  33. P.aperpatch = 8;
  34. P.numRays = 32 - P.aperpatch + 1; % no. of Rays (for each dimension)
  35. P.nCode = 3; % 3 = 1 full amplitude + 2 half amplitude
  37. Display_output = 20; %Display mode only: 20 gives a power of 1.0 for a linear output, 40 gives a power of 0.5 for a square root compression
  38. RcvProfile.AntiAliasCutoff = 15; % 10, 15, 20, 30
  40. %% Specify system parameters.
  41. Resource.Parameters.numTransmit = 256; % number of transmit channels.
  42. Resource.Parameters.numRcvChannels = 256; % number of receive channels.
  43. Resource.Parameters.verbose = 2; % 2: warnings and status 1: warnings 0: errors only
  44. Resource.Parameters.speedOfSound = 1540; % set speed of sound in m/sec before calling computeTrans
  45. Resource.Parameters.speedCorrectionFactor = 1.0;
  46. Resource.Parameters.fakeScanhead = 0; % allow system HW operation with nothing connected
  47. Resource.Parameters.simulateMode = 0;
  48. % Resource.Parameters.simulateMode = 1 forces simulate mode, even if hardware is present.
  49. % Resource.Parameters.simulateMode = 2 stops sequence and processes RcvData continuously.
  50. Resource.Parameters.Connector = 1;
  52. %% Specify Trans structure array.
  53. Trans.name = 'Matrix1024-15';
  54. Trans.units = 'mm';
  55. Trans = computeTrans_MatrixProbeIncluded(Trans);
  56. Trans.HVMux = computeUTAMux1024; % add the HV Mux programming for the UTA 1024-MUX
  58. % this files contains 100 sets of 4 Rnd Apertures that are complementary.
  59. % CompRndApod is matrix of 100x4x1024. The sum of every 4 matrices (of the
  60. % second dimention) account for the full aperture.
  61. load('saved100CompRndApod'); %This file has 100x4x1024
  63. CompRndApod=CompRndApod(1,:,:); % for rcv aperture - will use only one set
  65. % patch-like aperture
  66. Papod = zeros(P.numRays^2, 5, 1024);
  68. for i = 1:P.numRays
  69. for j = 1:P.numRays
  71. center1 = floor(P.aperpatch/2)+i-1;
  72. center2 = floor(P.aperpatch/2)+j-1;
  74. M = zeros(32); % initialize 32 x 32 matrix
  75. M(center1-floor(P.aperpatch/2)+1:center1+ceil(P.aperpatch/2),...
  76. center2-floor(P.aperpatch/2)+1:center2+ceil(P.aperpatch/2)) = 1;
  78. Papod((i-1)*P.numRays + j, 1, :) = reshape(M, [1, 1024]);
  79. Papod((i-1)*P.numRays + j,2:5,:) = CompRndApod;
  81. end
  82. end
  84. Papod2 = Papod;
  85. Papod(:,1,:) = ones(size(Papod,1),1,1024);
  87. % create aperture index for each Papod
  88. for i = 1:size(Papod, 1)
  89. for j = 1:size(Papod, 2)
  90. Paper(i,j) = computeMuxAperture(squeeze(Papod(i,j,:))', Trans);
  91. end
  92. end
  94. % Intermediate Variables
  95. waveLength = (Resource.Parameters.speedOfSound/1000)/Trans.frequency;
  96. if strcmp(Trans.units,'mm')
  97. Trans.ElementPosMm = Trans.ElementPos;
  98. Trans.ElementPosWL = Trans.ElementPos./waveLength;
  99. else
  100. Trans.ElementPosMm = Trans.ElementPos.*waveLength;
  101. Trans.ElementPosWL = Trans.ElementPos;
  102. end
  104. ElementPosx = unique(Trans.ElementPosWL(:,1));
  105. ElementPosy = unique(Trans.ElementPosWL(:,2)); % y direction has a gap
  107. % Convert mm to wavelength
  108. demodFreq = Trans.frequency; % demodulation frequency
  109. P.startDepth = P.startDepth_mm/waveLength; % Acquisition depth in wavelengths
  110. P.endDepth = P.endDepth_mm/waveLength; % This should preferrably be a multiple of 128 samples
  111. P.txFocus = P.txFocus_mm/waveLength;
  112. %% PData
  113. PData(1).PDelta = [1 1 1]/2;
  115. PData(1).Size(1) = ceil(((P.numRays+3)*Trans.spacing)/PData(1).PDelta(1)); % +3 compensates for gaps
  116. PData(1).Size(2) = ceil(((P.numRays)*Trans.spacing)/PData(1).PDelta(2));
  117. PData(1).Size(3) = ceil((P.endDepth-P.startDepth)/PData(1).PDelta(3));
  118. PData(1).Origin = [-((PData(1).Size(2)-1)/2)*PData(1).PDelta(2), ((PData(1).Size(1)-1)/2)*PData(1).PDelta(1), P.startDepth];
  119. P.lateral_voxel_size = PData(1).PDelta(1)*100e-6 ;
  120. P.axial_voxel_size = PData(1).PDelta(3)*100e-6 ;
  122. % specify Region structures.
  123. PData(1).Region = repmat(struct('Shape',struct( ...
  124. 'Name','Frustum',...
  125. 'Position',[0,0,P.startDepth],...
  126. 'l1',Trans.spacing,...
  127. 'w1',Trans.spacing,...
  128. 'height',P.endDepth-P.startDepth,...
  129. 'l2',Trans.spacing,...
  130. 'w2',Trans.spacing)),1,P.numRays^2);
  132. for i = 1:P.numRays
  133. for j = 1:P.numRays
  134. PData.Region((i-1)*P.numRays + j).Shape.Position(1) = (ElementPosx(i)+ElementPosx(i+P.aperpatch-1))/2;
  135. PData.Region((i-1)*P.numRays + j).Shape.Position(2) = -(ElementPosy(j)+ElementPosy(j+P.aperpatch-1))/2;
  137. if ismember(j, [2:8:P.numRays])
  138. PData.Region((i-1)*P.numRays + j).Shape.Position(2) = -(ElementPosy(j)+ElementPosy(j+8-1)-Trans.spacing/2)/2;
  139. PData.Region((i-1)*P.numRays + j).Shape.w1 = 1.5*Trans.spacing; % to compensate longer jump caused by gaps
  140. PData.Region((i-1)*P.numRays + j).Shape.w2 = 1.5*Trans.spacing;
  141. end
  143. if ismember(j, [8:8:P.numRays])
  144. PData.Region((i-1)*P.numRays + j).Shape.Position(2) = -(ElementPosy(j)+ElementPosy(j+8-1)+Trans.spacing/2)/2;
  145. PData.Region((i-1)*P.numRays + j).Shape.w1 = 1.5*Trans.spacing;
  146. PData.Region((i-1)*P.numRays + j).Shape.w2 = 1.5*Trans.spacing;
  147. end
  149. end
  150. end
  152. PData(1).Region = computeRegions(PData(1));
  154. for i = 1:8
  155. for j = 1:8
  157. PData.Region(P.numRays^2 + (i-1)*8 + j).Shape.Name = 'Custom';
  159. center_idx = (j:8:P.numRays)' + P.numRays*((i:8:P.numRays)-1); center_idx = center_idx(:);
  160. pixels_set = PData.Region(center_idx(1)).PixelsLA;
  161. for set_n = 2:length(center_idx)
  162. pixels_set = union(pixels_set, PData.Region(center_idx(set_n)).PixelsLA);
  163. end
  165. PData.Region(P.numRays^2 + (i-1)*8 + j).PixelsLA = pixels_set;
  166. PData.Region(P.numRays^2 + (i-1)*8 + j).numPixels = length(PData.Region(P.numRays^2 + (i-1)*8 + j).PixelsLA);
  168. end
  169. end
  171. PData.Region = PData.Region(end-8^2+1:end);
  173. %% Specify Media. Use point targets in middle of PData.
  174. Media.MP(1,:) = [0,0,60,1.0]; % single point.
  175. Media.numPoints = size(Media.MP,1);
  177. %% Resources
  178. % Resource.VDAS.el2ChMapDisable = 1;
  180. Resource.RcvBuffer(1).datatype = 'int16';
  181. Resource.RcvBuffer(1).rowsPerFrame = 4096*8^2*P.nCode*P.numSyntheticRcvs ;
  182. Resource.RcvBuffer(1).colsPerFrame = 256;
  183. Resource.RcvBuffer(1).numFrames = P.numFrames;
  184. Resource.ImageBuffer(1).numFrames = P.numFrames;
  185. % Resource.ImageBuffer(2).numFrames = P.numFrames;
  187. Resource.InterBuffer(1).datatype = 'complex';
  188. Resource.InterBuffer(1).numFrames = 1;
  190. Resource.DisplayWindow(1).Type = 'Verasonics';
  191. Resource.DisplayWindow(1).Title = '3D Flash Image - XZ plane';
  192. Resource.DisplayWindow(1).pdelta = 0.25;
  193. Resource.DisplayWindow(1).Position = [0,580, ...
  194. ceil(PData(1).Size(2)*PData(1).PDelta(2)/Resource.DisplayWindow(1).pdelta), ... % width
  195. ceil(PData(1).Size(3)*PData(1).PDelta(3)/Resource.DisplayWindow(1).pdelta)]; % height
  196. Resource.DisplayWindow(1).Orientation = 'xz';
  197. Resource.DisplayWindow(1).ReferencePt = [PData(1).Origin(1),-15,PData(1).Origin(3)];
  198. Resource.DisplayWindow(1).Colormap = gray(256);
  199. Resource.DisplayWindow(1).AxesUnits = 'mm';
  201. % Resource.DisplayWindow(2).mode = '2d';
  202. Resource.DisplayWindow(2).Type = 'Verasonics';
  203. Resource.DisplayWindow(2).Title = '3D Flash Image - YZ plane';
  204. Resource.DisplayWindow(2).pdelta = Resource.DisplayWindow(1).pdelta;
  205. Resource.DisplayWindow(2).Position = [430,580, ...
  206. ceil(PData(1).Size(1)*PData(1).PDelta(1)/Resource.DisplayWindow(2).pdelta), ... % width
  207. ceil(PData(1).Size(3)*PData(1).PDelta(3)/Resource.DisplayWindow(2).pdelta)]; % height
  208. Resource.DisplayWindow(2).Orientation = 'yz';
  209. Resource.DisplayWindow(2).ReferencePt = [-15,-PData(1).Origin(2),PData(1).Origin(3)];
  210. Resource.DisplayWindow(2).Colormap = gray(256);
  211. Resource.DisplayWindow(2).AxesUnits = 'mm';
  213. % Resource.DisplayWindow(3).mode = '2d';
  214. Resource.DisplayWindow(3).Type = 'Verasonics';
  215. Resource.DisplayWindow(3).Title = '3D Flash Image - XY plane';
  216. Resource.DisplayWindow(3).pdelta = Resource.DisplayWindow(1).pdelta;
  217. Resource.DisplayWindow(3).Position = [0,40, ...
  218. ceil(PData(1).Size(2)*PData(1).PDelta(2)/Resource.DisplayWindow(3).pdelta), ... % width
  219. ceil(PData(1).Size(1)*PData(1).PDelta(1)/Resource.DisplayWindow(3).pdelta)]; % height
  220. Resource.DisplayWindow(3).Orientation = 'xy';
  221. Resource.DisplayWindow(3).ReferencePt = [PData(1).Origin(1),-PData(1).Origin(2),60];%PData.Region(end).Shape.oPAIntersect];
  222. Resource.DisplayWindow(3).Colormap = gray(256);
  223. Resource.DisplayWindow(3).AxesUnits = 'mm';
  224. % Resource.DisplayWindow(3).mode = '2d';
  226. %% Specify Transmit waveform structure.
  227. TW.type = 'parametric';
  228. TW.Parameters = [Trans.frequency,.67,2,1]; % A, B, C, D
  229. P.TW = TW;
  231. % Specify TPC structure.
  232. TPC(1).hv = P.Voltage;
  234. %% Specify TX structure array.
  235. TX = repmat(struct('waveform', 1, ...
  236. 'Origin', [0.0,0.0,0.0], ...
  237. 'focus', P.txFocus, ...
  238. 'Steer', [0.0,0.0], ...
  239. 'Apod', ones(1,1024), ...
  240. 'Delay', zeros(1,256),...
  241. 'peakCutOff', 2,...
  242. 'peakBLMax', 20,...
  243. 'Angle', 0,...
  244. 'aperture',1,...
  245. 'TXPD',[]), 1,P.numRays^2);
  247. % generate parabolic delay law for each patch
  248. for i=1:P.numRays
  249. for j = 1:P.numRays
  251. TX((i-1)*P.numRays+j).Apod = squeeze(Papod2((i-1)*P.numRays+j,1,:))';
  252. TX((i-1)*P.numRays+j).aperture = Paper((i-1)*P.numRays+j,1);
  253. TX((i-1)*P.numRays+j).Origin = [(ElementPosx(i)+ElementPosx(i+P.aperpatch-1))/2, -(ElementPosy(j)+ElementPosy(j+P.aperpatch-1))/2, 0];
  254. TX((i-1)*P.numRays+j).Delay = computeTXDelays(TX((i-1)*P.numRays+j));
  256. % TX((i-1)*P.numRays+j).TXPD = computeTXPD(TX((i-1)*(P.numRays-1)+j),PData(1));
  258. end
  259. end
  261. k = P.numRays^2;
  262. Paper_real = zeros(1,64);
  264. % multiple focused points
  265. for i = 1:8
  266. for j = 1:8
  268. delay_matrix = reshape(TX(j+25*(i-1)).Delay, [32 32]);
  269. delay_matrix = repmat(delay_matrix(i:i+7,j:j+7),4,4);
  270. translated_D = reshape(circshift(delay_matrix, [i-1, j-1]), 1, 1024);
  272. Ap = ones(32);
  273. % remove truncated unit arrays that do not fully form focused beams
  274. if i > 1; Ap([1:i-1, 32-8+i:32], :) = 0; end
  275. if j > 1; Ap(:, [1:j-1, 32-8+j:32]) = 0; end
  277. Ap = reshape(Ap, 1, 1024);
  279. TX(k+j+8*(i-1)) = TX(1);
  280. TX(k+j+8*(i-1)).Delay = translated_D;
  281. TX(k+j+8*(i-1)).focus = 0.0;
  282. TX(k+j+8*(i-1)).Origin = [0.0, 0.0, 0.0];
  283. TX(k+j+8*(i-1)).Apod = Ap;
  285. Paper_real(j+(i-1)*8) = computeMuxAperture(Ap, Trans);
  286. TX(k+j+8*(i-1)).aperture = Paper_real(j+(i-1)*8);
  288. if P.nCode > 1
  289. TX(k+(i-1)*8+j+8^2) = TX(k+j+8*(i-1));
  290. TX(k+(i-1)*8+j+2*8^2) = TX(k+j+8*(i-1));
  292. % checkerboard masking for AM
  293. mask = repmat([repmat([1 0], 1, 16) repmat([0 1], 1, 16)], 1, 16);
  294. TX(k+(i-1)*8+j+8^2).Apod = TX(k+j+8*(i-1)).Apod.*mask;
  295. TX(k+(i-1)*8+j+2*8^2).Apod = TX(k+j+8*(i-1)).Apod.*(~mask);
  296. end
  297. end
  298. end
  300. TX = TX(end-8^2*P.nCode+1:end);
  302. % showTXPD
  303. %% allows to visualize the TX in 3D (similar to the EventAnalysisTool)
  304. % f = figure;
  305. %
  306. % for i=1:64
  307. % plot3(Trans.ElementPos(find(TX(i).Apod==1),1),Trans.ElementPos(find(TX(i).Apod==1),2),TX(i).Delay(find(TX(i).Apod==1)),'k*');
  308. % zlim([0 5]);
  309. % xlim([min(Trans.ElementPos(:,1)) max(Trans.ElementPos(:,1))]);
  310. % ylim([min(Trans.ElementPos(:,2)) max(Trans.ElementPos(:,2))]);
  311. % view(gca,-40,49);grid on;
  312. % zlabel('Delay (wavelength)'); xlabel('X (mm)'); ylabel('Y (mm)')
  313. %
  314. % pause(.5);
  315. %
  316. % end
  318. %% Specify TGC Waveform structure.
  319. TGC(1).CntrlPts = [100 1010 1023 1023 1023 1023 1023 1023];
  320. TGC(1).rangeMax = 128;
  321. TGC(1).Waveform = computeTGCWaveform(TGC);
  322. maxAcqLength = sqrt(P.endDepth^2 + (32*Trans.spacing)^2 + (35*Trans.spacing)^2 );
  324. %% Receive
  325. Receive = repmat(struct(...
  326. 'Apod', zeros(1,Trans.numelements), ...
  327. 'startDepth', P.startDepth, ...
  328. 'endDepth', 128/(4*2)*ceil(maxAcqLength/(128/(4*2))), ...
  329. 'TGC', 1, ...
  330. 'mode', 0, ...
  331. 'bufnum', 1, ...
  332. 'framenum', 1, ...
  333. 'acqNum', 1, ...
  334. 'aperture',0, ...
  335. 'sampleMode', 'NS200BW', ...
  336. 'callMediaFunc', 0), 1, P.numFrames*P.numSyntheticRcvs*P.nCode*8^2);
  338. for j = 1:P.numFrames
  339. k=P.numSyntheticRcvs*8^2*P.nCode*(j-1);
  340. for n = 1:P.nCode
  341. kk = P.numSyntheticRcvs*8^2*(n-1);
  342. for i = 1:8^2
  343. kkk=P.numSyntheticRcvs*(i-1);
  345. for w=1:P.numSyntheticRcvs
  346. Receive(w+kkk+kk+k).acqNum = w+kkk;
  347. Receive(w+kkk+kk+k).framenum = j;
  348. Receive(w+kkk+kk+k).aperture = Paper(i,w+1);
  349. if n==1
  350. Receive(w+kkk+kk+k).Apod = squeeze(Papod(i,w+1,:))';
  351. else % for AM
  352. Receive(w+kkk+kk+k).Apod = -squeeze(Papod(i,w+1,:))'; % subtract
  353. Receive(w+kkk+kk+k).mode = 1; % accumulate
  354. end
  355. end
  356. end
  357. end
  358. end
  360. %% Recon
  361. senscutoff = 0.6;
  363. Recon(1) = struct('senscutoff', senscutoff, ...
  364. 'pdatanum', 1, ...
  365. 'rcvBufFrame', -1, ...
  366. 'IntBufDest', [1,1],...
  367. 'ImgBufDest', [1,-1], ...
  368. 'RINums', 1:8^2*P.numSyntheticRcvs) ;
  370. % Define ReconInfo structures.
  371. ReconInfo = repmat(struct('mode', 'accumIQ', ...
  372. 'txnum', 1, ...
  373. 'rcvnum', 1, ...
  374. 'scaleFactor', 1, ...
  375. 'regionnum', 1), 1, 8^2*P.numSyntheticRcvs);
  378. ReconInfo(1).mode = 3;
  379. for w=1:8^2
  380. k= P.numSyntheticRcvs*(w-1);
  381. for i = 1:P.numSyntheticRcvs
  382. ReconInfo(i+k).txnum = w;
  383. ReconInfo(i+k).rcvnum = i+k;
  384. ReconInfo(i+k).regionnum = w;
  385. if i == 1
  386. ReconInfo(i+k).mode = 3;
  387. end
  388. if i == P.numSyntheticRcvs
  389. ReconInfo(i+k).mode = 5;
  390. end
  391. end
  392. end
  394. ReconInfo(end).mode = 5;
  396. %% Specify Process structure arrays.
  397. pgain=20;
  399. Process(1).classname = 'Image';
  400. Process(1).method = 'imageDisplay';
  401. Process(1).Parameters = {'imgbufnum',1,...
  402. 'framenum',-1,...
  403. 'pdatanum',1,...
  404. 'srcData','intensity3D',...
  405. 'pgain', pgain,...
  406. 'persistMethod','none',...
  407. 'persistLevel',30,...
  408. 'interpMethod','4pt',...
  409. 'compressMethod','power',...
  410. 'compressFactor',75,...
  411. 'mappingMethod','full',...
  412. 'display',1,...
  413. 'displayWindow',1};
  414. Process(2).classname = 'Image';
  415. Process(2).method = 'imageDisplay';
  416. Process(2).Parameters = {'imgbufnum',1,...
  417. 'framenum',-1,...
  418. 'pdatanum',1,...
  419. 'srcData','intensity3D',...
  420. 'pgain', pgain,...
  421. 'persistMethod','none',...
  422. 'persistLevel',30,...
  423. 'interpMethod','4pt',...
  424. 'compressMethod','power',...
  425. 'compressFactor',75,...
  426. 'mappingMethod','full',...
  427. 'display',1,...
  428. 'displayWindow',2};
  429. Process(3).classname = 'Image';
  430. Process(3).method = 'imageDisplay';
  431. Process(3).Parameters = {'imgbufnum',1,...
  432. 'framenum',-1,...
  433. 'pdatanum',1,...
  434. 'srcData','intensity3D',...
  435. 'pgain', pgain,...
  436. 'persistMethod','none',...
  437. 'persistLevel',30,...
  438. 'interpMethod','4pt',...
  439. 'compressMethod','power',...
  440. 'compressFactor',75,...
  441. 'mappingMethod','full',...
  442. 'display',1,...
  443. 'displayWindow',3};
  444. Process(4).classname = 'External';
  445. Process(4).method = 'volumetricPlot';
  446. Process(4).Parameters = {'srcbuffer','image',...
  447. 'srcbufnum',1,...
  448. 'srcframenum',-1,...
  449. 'dstbuffer','none'};
  450. % Save Image Data
  451. Process(5).classname = 'External';
  452. Process(5).method = 'saveImageData';
  453. Process(5).Parameters = {'srcbuffer','image',... % name of buffer to process.
  454. 'srcbufnum',1,...
  455. 'srcframenum',-1,... % process the most recent frame.
  456. 'dstbuffer','none'};
  458. Process(6).classname = 'External';
  459. Process(6).method = 'vstic';
  460. Process(6).Parameters = {'srcbuffer','none'};
  462. Process(7).classname = 'External';
  463. Process(7).method = 'vstoc';
  464. Process(7).Parameters = {'srcbuffer','none'};
  466. % Save RF Data
  467. Process(8).classname = 'External';
  468. Process(8).method = 'saveRFData';
  469. Process(8).Parameters = {'srcbuffer','receive',... % name of buffer to process.
  470. 'srcbufnum',1,...
  471. 'srcframenum',-1,... % process the most recent frame.
  472. 'dstbuffer','none'};
  474. %external function
  475. EF(1).Function = vsv.seq.function.ExFunctionDef('volumetricPlot', @volumetricPlot);
  476. EF(2).Function = vsv.seq.function.ExFunctionDef('saveImageData', @saveImageData);
  477. EF(3).Function = vsv.seq.function.ExFunctionDef('vstic', @vstic);
  478. EF(4).Function = vsv.seq.function.ExFunctionDef('vstoc', @vstoc);
  479. EF(5).Function = vsv.seq.function.ExFunctionDef('saveRFData', @saveRFData);
  481. % Specify SeqControl structure arrays.
  482. SeqControl(1).command = 'jump'; % jump back to start
  483. SeqControl(1).argument = 1;
  484. SeqControl(2).command = 'timeToNextAcq'; % time between synthetic aperture acquisitions
  485. SeqControl(2).argument = 250;
  486. SeqControl(3).command = 'timeToNextAcq'; % time between frames
  487. % SeqControl(3).argument = 20000; % 20 msec
  488. SeqControl(3).argument = 1000000; % 2s
  489. SeqControl(3).condition = 'ignore'; %Recon processing time
  490. SeqControl(4).command = 'returnToMatlab';
  491. SeqControl(5).command = 'triggerOut';
  492. SeqControl(6).command = 'loopCnt'; % - Set loop count. for looping on blocs
  493. SeqControl(6).argument = P.numberImagesToSave/P.numFrames-1; %
  494. SeqControl(6).condition = 'counter1';
  495. SeqControl(7).command = 'loopTst'; % loop test
  496. SeqControl(7).argument = []; % set apres
  497. SeqControl(7).condition = 'counter1';
  499. nsc = 8; % nsc is count of SeqControl objects
  500. %% Specify Event Structure arrays
  501. n = 1; % start index for Events
  503. for j = 1:Resource.RcvBuffer(1).numFrames
  505. v=P.numSyntheticRcvs*8^2*P.nCode*(j-1);
  506. for w=1:8^2
  507. k= P.numSyntheticRcvs*(w-1);
  508. for i = 1:P.numSyntheticRcvs
  509. for m = 1:P.nCode
  510. Event(n).info = 'Transmit/Receive.';
  511. Event(n).tx = w + (m-1)*8^2;
  512. Event(n).rcv = i+k+v+(m-1)*8^2*P.numSyntheticRcvs;
  513. Event(n).recon = 0; % no reconstruction.
  514. Event(n).process = 0; % no processing
  515. Event(n).seqControl = [2,5]; %
  516. n = n+1;
  517. end
  518. end
  519. end
  520. Event(n-1).seqControl = [3,5];
  522. Event(n).info = 'Transfer To Host';
  523. Event(n).tx = 0;
  524. Event(n).rcv = 0;
  525. Event(n).recon = 0; % no reconstruction.
  526. Event(n).process = 0; % no processing
  527. Event(n).seqControl = [nsc,nsc+1]; % set wait time and transfer data
  528. SeqControl(nsc).command = 'transferToHost';
  529. nsc = nsc + 1;
  530. SeqControl(nsc).command = 'waitForTransferComplete';
  531. SeqControl(nsc).argument = nsc-1;
  532. nsc = nsc + 1;
  533. n = n+1;
  535. Event(n).info = ['3D Reconstruct Frame ' num2str(i)];
  536. Event(n).tx = 0;
  537. Event(n).rcv = 0;
  538. Event(n).recon = 1;
  539. Event(n).process = 0;
  540. Event(n).seqControl = 0;
  541. n = n+1;
  543. Event(n).info = ['Process XZ - Frame ' num2str(i)];
  544. Event(n).tx = 0;
  545. Event(n).rcv = 0;
  546. Event(n).recon = 0;
  547. Event(n).process = 1;
  548. Event(n).seqControl = 0;
  549. n = n+1;
  551. Event(n).info = ['Process YZ - Frame ' num2str(i)];
  552. Event(n).tx = 0;
  553. Event(n).rcv = 0;
  554. Event(n).recon = 0;
  555. Event(n).process = 2;
  556. Event(n).seqControl = 0;
  557. n = n+1;
  559. Event(n).info = ['Process XY - Frame ' num2str(i)];
  560. Event(n).tx = 0;
  561. Event(n).rcv = 0;
  562. Event(n).recon = 0;
  563. Event(n).process = 3;
  564. Event(n).seqControl = 0;
  565. n = n+1;
  567. Event(n).info = ['3D Process - Frame' num2str(i)];
  568. Event(n).tx = 0;
  569. Event(n).rcv = 0;
  570. Event(n).recon = 0;
  571. Event(n).process = 4;
  572. Event(n).seqControl = 0;
  573. n = n+1;
  575. Event(n).info = 'Return to Matlab';
  576. Event(n).tx = 0;
  577. Event(n).rcv = 0;
  578. Event(n).recon = 0;
  579. Event(n).process = 0;
  580. Event(n).seqControl = 4;
  581. n = n+1;
  583. Event(n).info = 'HW/SW Sync';
  584. Event(n).tx = 0;
  585. Event(n).rcv = 0;
  586. Event(n).recon = 0;
  587. Event(n).process = 0;
  588. Event(n).seqControl = nsc;
  589. SeqControl(nsc).command = 'sync';
  590. nsc = nsc + 1;
  591. n = n+1;
  593. end
  595. Event(n).info = 'Jump';
  596. Event(n).tx = 0; % no TX structure.
  597. Event(n).rcv = 0; % no Rcv structure.
  598. Event(n).recon = 0; % no reconstruction.
  599. Event(n).process = 0; % call processing function
  600. Event(n).seqControl = 1; %
  601. n = n+1;
  603. % Acquire Image Data
  604. nStartAcquire = n;
  606. Event(n).info = 'tic ';
  607. Event(n).tx = 0;
  608. Event(n).rcv = 0;
  609. Event(n).recon = 0;
  610. Event(n).process = 6;
  611. Event(n).seqControl = 0;
  612. n = n+1;
  614. % set loop count
  615. Event(n).info = 'start counter';
  616. Event(n).tx = 0;
  617. Event(n).rcv = 0;
  618. Event(n).recon = 0; % no reconstruction.
  619. Event(n).process = 0; % no processing
  620. Event(n).seqControl = 6;
  621. n = n+1;
  622. SeqControl(7).argument = n;
  624. for j = 1:Resource.RcvBuffer(1).numFrames
  626. v=P.numSyntheticRcvs*8^2*P.nCode*(j-1);
  627. for w=1:8^2
  628. k= P.numSyntheticRcvs*(w-1);
  629. for i = 1:P.numSyntheticRcvs
  630. for m = 1:P.nCode
  631. Event(n).info = 'Transmit/Receive.';
  632. Event(n).tx = w + (m-1)*8^2;
  633. Event(n).rcv = i+k+v+(m-1)*8^2*P.numSyntheticRcvs;
  634. Event(n).recon = 0; % no reconstruction.
  635. Event(n).process = 0; % no processing
  636. Event(n).seqControl = [2,5]; %
  637. n = n+1;
  638. end
  639. end
  640. end
  641. Event(n-1).seqControl = [3,5];
  643. Event(n).info = 'Transfer To Host';
  644. Event(n).tx = 0;
  645. Event(n).rcv = 0;
  646. Event(n).recon = 0; % no reconstruction.
  647. Event(n).process = 0; % no processing
  648. Event(n).seqControl = [nsc, nsc+1]; % set wait time and transfer data [nsc,nsc+1]
  649. SeqControl(nsc).command = 'transferToHost';
  650. nsc = nsc + 1;
  651. SeqControl(nsc).command = 'waitForTransferComplete';
  652. SeqControl(nsc).argument = nsc-1;
  653. nsc = nsc + 1;
  654. n = n+1;
  657. Event(n).info = ['3D Reconstruct Frame ' num2str(i)];
  658. Event(n).tx = 0;
  659. Event(n).rcv = 0;
  660. Event(n).recon = 1;
  661. Event(n).process = 0;
  662. Event(n).seqControl = 0;
  663. n = n+1;
  665. Event(n).info = ['Process XZ - Frame ' num2str(i)];
  666. Event(n).tx = 0;
  667. Event(n).rcv = 0;
  668. Event(n).recon = 0;
  669. Event(n).process = 1;
  670. Event(n).seqControl = 0;
  671. n = n+1;
  673. Event(n).info = ['Process YZ - Frame ' num2str(i)];
  674. Event(n).tx = 0;
  675. Event(n).rcv = 0;
  676. Event(n).recon = 0;
  677. Event(n).process = 2;
  678. Event(n).seqControl = 0;
  679. n = n+1;
  681. Event(n).info = ['Process XY - Frame ' num2str(i)];
  682. Event(n).tx = 0;
  683. Event(n).rcv = 0;
  684. Event(n).recon = 0;
  685. Event(n).process = 3;
  686. Event(n).seqControl = 0;
  687. n = n+1;
  689. Event(n).info = ['3D Process - Frame' num2str(i)];
  690. Event(n).tx = 0;
  691. Event(n).rcv = 0;
  692. Event(n).recon = 0;
  693. Event(n).process = 4;
  694. Event(n).seqControl = 0;
  695. n = n+1;
  696. %
  697. Event(n).info = 'Return to Matlab';
  698. Event(n).tx = 0;
  699. Event(n).rcv = 0;
  700. Event(n).recon = 0;
  701. Event(n).process = 0;
  702. Event(n).seqControl = 4;
  703. n = n+1;
  705. Event(n).info = 'HW/SW Sync';
  706. Event(n).tx = 0;
  707. Event(n).rcv = 0;
  708. Event(n).recon = 0;
  709. Event(n).process = 0;
  710. Event(n).seqControl = nsc;
  711. SeqControl(nsc).command = 'sync';
  712. nsc = nsc + 1;
  713. n = n+1;
  715. Event(n).info = 'save Image Data';
  716. Event(n).tx = 0; % no transmit
  717. Event(n).rcv = 0; % no rcv
  718. Event(n).recon = 0; % no reconstruction
  719. Event(n).process = 5; % process
  720. Event(n).seqControl = 0;
  721. n = n+1;
  723. % Event(n).info = 'save RF Data';
  724. % Event(n).tx = 0; % no transmit
  725. % Event(n).rcv = 0; % no rcv
  726. % Event(n).recon = 0; % no reconstruction
  727. % Event(n).process = 8; % process
  728. % Event(n).seqControl = 0;
  729. % n = n+1;
  731. Event(n).info = 'toc ';
  732. Event(n).tx = 0;
  733. Event(n).rcv = 0;
  734. Event(n).recon = 0;
  735. Event(n).process = 7;
  736. Event(n).seqControl = 0;
  737. n = n+1;
  738. end
  741. % Event(n).info = 'save Image Data';
  742. % Event(n).tx = 0; % no transmit
  743. % Event(n).rcv = 0; % no rcv
  744. % Event(n).recon = 0; % no reconstruction
  745. % Event(n).process = 5; % process
  746. % Event(n).seqControl = 0;
  747. % n = n+1;
  749. Event(n).info = 'Loop back until all images are acquired';
  750. Event(n).tx = 0; % no transmit
  751. Event(n).rcv = 0; % no rcv
  752. Event(n).recon = 0; % no reconstruction
  753. Event(n).process = 0; % process
  754. Event(n).seqControl = 7;
  755. n = n+1;
  757. %% User specified UI Control Elements
  758. import vsv.seq.uicontrol.VsSliderControl;
  760. % - Sensitivity Cutoff
  761. UI(1).Control = VsSliderControl('LocationCode','UserB7','Label','Sens. Cutoff',...
  762. 'SliderMinMaxVal',[0,1.0,Recon(1).senscutoff],...
  763. 'SliderStep',[0.025,0.1],'ValueFormat','%1.3f', ...
  764. 'Callback', @SensCutoffCallback );
  766. % - Section number change
  767. UI(2).Control = VsSliderControl('LocationCode','UserB1','Label','Z-Section',...
  768. 'SliderMinMaxVal',[1,P.endDepth,Resource.DisplayWindow(3).ReferencePt(3)],...
  769. 'SliderStep',[1/(P.endDepth-1) 10/(P.endDepth-1)],'ValueFormat','%3.0f', ...
  770. 'Callback', @ZSectionChange );
  772. % - Section number change
  773. UI(3).Control = VsSliderControl('LocationCode','UserB2','Label','Y-Section',...
  774. 'SliderMinMaxVal',[PData(1).Origin(2)-(PData(1).Size(1)-1)*PData(1).PDelta(1), PData(1).Origin(2), Resource.DisplayWindow(1).ReferencePt(2)],...
  775. 'SliderStep',[1/((PData(1).Size(2)-1)*PData(1).PDelta(2)-1) 10/((PData(1).Size(2)-1)*PData(1).PDelta(2)-1)],'ValueFormat','%3.0f', ...
  776. 'Callback', @YSectionChange);
  778. % - Section number change
  779. UI(4).Control = VsSliderControl('LocationCode','UserB3','Label','X-Section',...
  780. 'SliderMinMaxVal',[PData(1).Origin(1), PData(1).Origin(1)+(PData(1).Size(2)-1)*PData(1).PDelta(2), Resource.DisplayWindow(2).ReferencePt(1)],...
  781. 'SliderStep',[1/((PData(1).Size(1)-1)*PData(1).PDelta(1)-1) 10/((PData(1).Size(1)-1)*PData(1).PDelta(1)-1)],'ValueFormat','%3.0f', ...
  782. 'Callback', @XSectionChange);
  784. % - compression
  785. CompressionFactor=1.3;
  786. UI(5).Control = VsSliderControl('LocationCode','UserA2','Label','CompressionP',...
  787. 'SliderMinMaxVal',[1,11,CompressionFactor],...
  788. 'SliderStep',[1/40,4/40],'ValueFormat','%1.1f', ...
  789. 'Callback', @CompressionP );
  791. UI(6).Control = {'UserC1','Style','VsPushButton','Label','Save Image'};
  792. UI(6).Callback = text2cell('%StartSaving');
  794. % Specify factor for converting sequenceRate to frameRate.
  795. frameRateFactor = 3;
  797. % Save all the structures to a .mat file.
  798. filename = 'SetupMatrix1024_15MHz_Takoyaki_LiveImaging';
  799. save(['MatFiles/',filename]);
  800. VSX
  801. return
  803. %StartSaving
  804. display('Start Saving')
  805. Control = repmat(struct('Command','set&Run','Parameters',[]),1,1);
  806. nStartAcquire = evalin('base','nStartAcquire');
  807. Control(1).Parameters = {'Parameters',1,'startEvent',nStartAcquire};
  808. evalin('base','Resource.Parameters.startEvent = nStartAcquire;');
  809. assignin('base','Control', Control);
  810. %StartSaving
  812. %% **** Callback routines used by UIControls (UI) ****
  814. function SensCutoffCallback(~,~,UIValue)
  815. %SensCutoff - Sensitivity cutoff change
  816. ReconL = evalin('base', 'Recon');
  817. for i = 1:size(ReconL,2)
  818. ReconL(i).senscutoff = UIValue;
  819. end
  820. assignin('base','Recon',ReconL);
  821. Control = evalin('base','Control');
  822. Control.Command = 'update&Run';
  823. Control.Parameters = {'Recon'};
  824. assignin('base','Control', Control);
  825. end
  827. function YSectionChange(~,~,UIValue)
  828. %YSectionChange
  829. RS = evalin('base','Resource');
  830. RefPt = RS.DisplayWindow(1).ReferencePt;
  831. RefPt(2) = UIValue;
  832. RS.DisplayWindow(1).ReferencePt = RefPt;
  833. assignin('base','Resource',RS);
  835. PDataT = evalin('base','PData');
  836. P = evalin('base','P');
  838. PDataT(1).Region(1).Shape.oPAIntersect = RefPt(2);
  840. PDataT(1).Region = computeRegions(PDataT(1));
  841. assignin('base','PData',PDataT);
  843. Control = repmat(struct('Command',[],'Parameters',[]),1,2);
  844. Control(1).Command = 'set&Run';
  845. Control(1).Parameters = {'DisplayWindow',1,'ReferencePt',RefPt};
  846. Control(2).Command = 'update&Run';
  847. Control(2).Parameters = {'PData'};
  848. assignin('base','Control', Control);
  849. end
  851. function XSectionChange(~,~,UIValue)
  852. %XSectionChange
  853. RS = evalin('base','Resource');
  854. RefPt = RS.DisplayWindow(2).ReferencePt;
  855. RefPt(1) = UIValue;
  856. RS.DisplayWindow(2).ReferencePt = RefPt;
  857. assignin('base','Resource',RS);
  859. PDataT = evalin('base','PData');
  860. P = evalin('base','P');
  862. PDataT(1).Region(1).Shape.oPAIntersect = RefPt(1);
  864. PDataT(1).Region = computeRegions(PDataT(1));
  865. assignin('base','PData',PDataT);
  867. Control = repmat(struct('Command',[],'Parameters',[]),1,2);
  868. Control(1).Command = 'set&Run';
  869. Control(1).Parameters = {'DisplayWindow',2,'ReferencePt',RefPt};
  870. Control(2).Command = 'update&Run';
  871. Control(2).Parameters = {'PData'};
  872. assignin('base','Control', Control);
  873. end
  875. function ZSectionChange(~,~,UIValue)
  876. %ZSectionChange
  877. RS = evalin('base','Resource');
  878. RefPt = RS.DisplayWindow(3).ReferencePt;
  879. RefPt(3) = UIValue;
  880. RS.DisplayWindow(3).ReferencePt = RefPt;
  881. assignin('base','Resource',RS);
  883. PDataT = evalin('base','PData');
  884. P = evalin('base','P');
  886. PDataT(1).Region(1).Shape.oPAIntersect = RefPt(3);
  888. PDataT(1).Region = computeRegions(PDataT(1));
  889. assignin('base','PData',PDataT);
  891. Control = repmat(struct('Command',[],'Parameters',[]),1,2);
  892. Control(1).Command = 'set&Run';
  893. Control(1).Parameters = {'DisplayWindow',3,'ReferencePt',RefPt};
  894. Control(2).Command = 'update&Run';
  895. Control(2).Parameters = {'PData'};
  896. assignin('base','Control', Control);
  897. end
  899. function CompressionP(~,~,UIValue)
  900. %CompressionP
  901. CompressionFactor = UIValue;
  902. assignin('base','CompressionFactor',CompressionFactor);
  903. end
  905. %% **** Callback routines used by External function definition (EF) ****
  907. function volumetricPlot(ImgData)
  909. persistent handle3D
  911. PData = evalin('base','PData');
  912. Trans = evalin('base','Trans');
  913. P = evalin('base','P');
  914. v_depth = 1;
  915. CFactor = evalin('base','CompressionFactor');
  916. ImgData=ImgData.^(1/CFactor);
  917. ImgData(:,:,1:v_depth)=0;
  918. ImgData = flipdim(ImgData,3);
  920. if isempty(handle3D) || ~ishandle(handle3D)
  921. figure('Position', [430, 50, 450, 450]);
  922. handle3D = axes;
  923. end
  925. set(handle3D,'NextPlot','replacechildren');
  926. vol3d('cdata', ImgData,'texture', '2D','Parent', handle3D);
  927. grid(handle3D, 'on'); colormap(handle3D,'gray');
  928. set(handle3D,'NextPlot','add');
  930. xl=PData(1).Size(2);
  931. yl=PData(1).Size(1);
  932. zl=PData(1).Size(3);
  933. dx=PData(1).PDelta(2);
  934. dy=PData(1).PDelta(1);
  935. dz=PData(1).PDelta(3);
  936. plot3(handle3D,Trans.ElementPosWL(:,1)./dx+(xl-1)/2,Trans.ElementPosWL(:,2)./dy+(yl-1)/2,(Trans.ElementPosWL(:,3)+P.endDepth)./dz,'k.');
  937. view(handle3D,-45,30);
  938. end
  940. function saveImageData(ImgData)
  942. persistent bloc_count;
  943. if isempty(bloc_count)
  944. bloc_count = 1;
  945. end
  946. P = evalin('base','P');
  947. Path = P.pathName;
  949. if bloc_count == 1
  950. mkdir(Path)
  951. save([Path '\P.mat'],'P');
  952. end
  953. eval(['pAM3D_',num2str(bloc_count),' = squeeze(ImgData);']);
  954. save([Path '\pAM3D_' num2str(bloc_count) '.mat'],['pAM3D_',num2str(bloc_count)]);
  955. display(['Saved image #',num2str(bloc_count) '!'])
  956. bloc_count = bloc_count+1;
  957. if bloc_count == P.numberImagesToSave + 1
  958. display('End of the acquisition!')
  959. end
  960. end
  962. function vstic(varargin)
  963. tic
  964. end
  966. function vstoc(varargin)
  968. persistent Tend;
  970. if isempty(Tend)
  971. Tend = toc;
  972. disp(['Elapsed time is ', num2str(Tend), ' seconds.'])
  973. else
  974. Tend(end+1) = toc;
  975. disp(['Elapsed time is ', num2str(Tend(end)), ' seconds.'])
  976. end
  978. P = evalin('base','P');
  979. if length(Tend) == P.numberImagesToSave
  980. Path = P.pathName;
  981. save([Path '\timelog.mat'],'Tend');
  982. end
  984. end
  986. function saveRFData(RFData)
  988. persistent RF_count;
  989. if isempty(RF_count)
  990. RF_count = 1;
  991. end
  992. P = evalin('base','P');
  993. Path = P.pathName;
  995. if RF_count == 1
  996. mkdir(Path)
  997. save([Path '\P.mat'],'P');
  998. end
  1000. eval(['RFData_',num2str(RF_count),' = squeeze(RFData);']);
  1001. save([Path '\RFData_' num2str(RF_count) '.mat'],['RFData_',num2str(RF_count)]);
  1002. display(['Saved RFData #',num2str(RF_count) '!'])
  1003. RF_count = RF_count+1;
  1004. if RF_count == P.numberImagesToSave + 1
  1005. display('End of the acquisition!')
  1006. end
  1007. end

SetupMatrix1024_15MHz_Takoyaki_LiveImaging.m at commit e3bab2d, no license · at the source

Overview

Authors: Sunho Lee1, Di Wu2, Dina Malounda2, Claire Rabut2, Mikhail G Shapiro1,2,3
ORCID iDs: Sunho Lee, Di Wu, Dina Malounda, Mikhail G Shapiro
  1. Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA
  2. Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA USA
  3. Howard Hughes Medical Institute, Pasadena, CA USA
Institutions: California Institute of Technology (United States); Howard Hughes Medical Institute (United States)
Journal: Nature communications, volume 17, issue 1, article 7281
Dates: received 16 June 2025; accepted 28 April 2026; published online 21 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-72961-0 · PMID 42168209 · PMCID PMC13402839 · OpenAlex W4404406274
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.11.14.623368
Status: code verified
Categories: other (modality), human (organism), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Connectivity, Preprocessing, fMRI & imaging
Keywords: Ultrasound, Molecular imaging, Synthetic biology, Nanoparticles, Microbiology
MeSH: Glioblastoma*, Imaging, Three-Dimensional*, Animals, Brain, Brain Neoplasms, Cell Line, Tumor, Contrast Media, Genes, Reporter, Humans, Mice, Microbubbles, Ultrasonography (* major topic)
Topic: Photoacoustic and Ultrasonic Imaging (Biomedical Engineering, Engineering), according to OpenAlex
Funding: NIBIB NIH HHS (R01 EB018975); U.S. Department of Health & Human Services | National Institutes of Health (NIH) (R01NS120828, R01EB018975); Chan Zuckerberg Initiative; NINDS NIH HHS (R01 NS120828); U.S. Department of Health & Human Services | National Institutes of Health (R01EB018975, R01NS120828); Howard Hughes Medical Institute
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Acoustic contrast agents and reporter genes play a critical role in allowing ultrasound to visualize blood flow, map molecules and track cellular function in optically opaque living organisms. However, many advanced agents requiring high acoustic pressures have been imaged primarily in 2D, while biological phenomena of interest unfurl in three dimensions. Here, we introduce a method for efficient, dynamic imaging of contrast agents and reporter genes in 3D using multiplexed matrix array transducers. Our “Takoyaki” pulse sequence uses the simultaneous scanning of multiple focal points to excite contrast agents with sufficient acoustic pressure for nonlinear imaging while efficiently covering 3D space. We first characterize and benchmark Takoyaki imaging performance with gas vesicle contrast agents in vitro. Then we establish utility in cellular imaging by visualizing acoustic reporter gene expression in a mouse model of glioblastoma. Finally, we demonstrate real-time volumetric imaging by tracking the dynamics of fluid motion in mouse brain ventricles during and after intraventricular contrast injection. Takoyaki imaging enables a more comprehensive understanding of biological processes by providing spatiotemporal information in 3D within the constraints of accessible multiplexed matrix-array systems.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

shapiro-lab/Takoyaki-imaging

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e3bab2d2b11eee2aaeb479eaf530bf6efea03ef2, 19 March 2026
Languages: MATLAB (7), Python (4)
Size: 23 files, 11 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: napari (4 files), NumPy (4 files), scikit-image (4 files), SciPy (4 files), Image Processing Toolbox (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
12 files
At the source: github.com/shapiro-lab/Takoyaki-imaging

Zenodo 19124225

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: napari (4 files), NumPy (4 files), scikit-image (4 files), SciPy (4 files), Image Processing Toolbox (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
12 files
At the source:

Code availability

The ultrasound sequence scripts and data analysis codes used to generate key figures and results are available on GitHub (https://github.com/shapiro-lab/Takoyaki-imaging)65.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 22 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Primary image data have been deposited in the CaltechDATA repository at 10.22002/knnyt-x4762, available at https://data.caltech.edu/records/knnyt-x4762. Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 12 MeSH terms, 6 funders, 58 references.

Cite

This paper

Lee, S., Wu, D., Malounda, D., Rabut, C., & Shapiro, M. G. (2026). Real-time volumetric imaging of cells and molecules in deep tissues with Takoyaki ultrasound. Nature communications, 17(1), 7281. https://doi.org/10.1038/s41467-026-72961-0

BibTeX

@article{lee2026real,
author = {Lee, Sunho and Wu, Di and Malounda, Dina and Rabut, Claire and Shapiro, Mikhail G},
title = {{Real-time volumetric imaging of cells and molecules in deep tissues with Takoyaki ultrasound}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {7281},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72961-0},
url = {https://doi.org/10.1038/s41467-026-72961-0},
pmid = {42168209},
pmcid = {PMC13402839}
}

RIS

TY - JOUR
AU - Lee, Sunho
AU - Wu, Di
AU - Malounda, Dina
AU - Rabut, Claire
AU - Shapiro, Mikhail G
TI - Real-time volumetric imaging of cells and molecules in deep tissues with Takoyaki ultrasound
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/21
VL - 17
IS - 1
SP - 7281
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72961-0
UR - https://doi.org/10.1038/s41467-026-72961-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-72961-0",
"type": "article-journal",
"title": "Real-time volumetric imaging of cells and molecules in deep tissues with Takoyaki ultrasound",
"container-title": "Nature communications",
"author": [
{
"family": "Lee",
"given": "Sunho"
},
{
"family": "Wu",
"given": "Di"
},
{
"family": "Malounda",
"given": "Dina"
},
{
"family": "Rabut",
"given": "Claire"
},
{
"family": "Shapiro",
"given": "Mikhail G"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7281",
"DOI": "10.1038/s41467-026-72961-0",
"PMID": "42168209",
"PMCID": "PMC13402839",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72961-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
21
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1111/ejn.70582 [code]
Multifiber Array-Based Photometry System for Multiregional Functional Mapping in the Mouse Brain.
Journal: The European journal of neuroscience
In common: scikit-image, SciPy, NumPy, mouse, 3 references
[2] doi: [code]
Real-time closed-loop feedback system for mouse mesoscale cortical signal and movement control
Journal: eLife
In common: napari, scikit-image, SciPy, 1 other tool, mouse, 1 reference
[3] doi:10.1016/j.celrep.2026.117420 [code]
Neural population dynamics of direct electrical stimulation of neocortex.
Journal: Cell reports
In common: napari, scikit-image, SciPy, 1 other tool, mouse, 1 reference
[4] doi:10.1038/s42003-026-10063-9 [code]
Conserved Kir channel mechanisms governing intrinsic excitability in human and rodent parvalbumin neurons.
Journal: Communications biology
In common: napari, scikit-image, SciPy, 1 other tool, mouse, cellular / molecular
[5] doi:10.1038/s41586-026-10323-y [code]
Genetically encoded assembly recorder temporally resolves cellular history.
Journal: Nature
In common: napari, scikit-image, SciPy, 1 other tool, mouse, cellular / molecular
[6] doi:10.1126/sciadv.aed5548 [code]
In situ proteomics unveils specialized domains for extrasynaptic signaling on neuronal cilia.
Journal: Science advances
In common: napari, scikit-image, SciPy, 1 other tool, mouse, cellular / molecular
[7] doi:10.1093/neuonc/noag128 [code]
Spatially-resolved single-cell imaging of melanoma brain metastases identifies localized immune patterns predictive of immune checkpoint blockade response.
Journal: Neuro-oncology
In common: napari, scikit-image, SciPy, 1 other tool, other condition
[8] doi:10.1038/s41467-026-73770-1 [code]
Non-coding structural variants disrupt FOXG1 transcriptional regulation in early neurodevelopment.
Journal: Nature communications
In common: napari, scikit-image, SciPy, 1 other tool, other condition
[9] doi:10.1038/s41593-026-02262-8 [code]
Cheese3D enables sensitive detection and analysis of whole-face movement in mice.
Journal: Nature neuroscience
In common: napari, scikit-image, SciPy, 1 other tool, mouse
[10] doi:10.1002/advs.77822 [code]
Temporal Trajectories of the Tau Aggregate Interactome Reveal Stage-Specific Vulnerabilities in Alzheimer's Disease.
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
In common: napari, scikit-image, SciPy, 1 other tool, cellular / molecular

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.