A superresolution imaging approach that localizes very small targets,such as red blood cells or droplets of injected photoacoustic dye,has significantly improved spatial resolution in various biological and medical imaging modalities.However,this superior spatial resolution is achieved by sacrificing temporal resolution because many raw image frames,each containing the localization target,must be superimposed to form a sufficiently sampled high-density superresolution image.Here,we demonstrate a computational strategy based on deep neural networks(DNNs)to reconstruct high-density superresolution images from far fewer raw image frames.The localization strategy can be applied for both 3D label-free localization optical-resolution photoacoustic microscopy(OR-PAM)and 2D labeled localization photoacoustic computed tomography(PACT).For the former,the required number of raw volumetric frames is reduced from tens to fewer than ten.For the latter,the required number of raw 2D frames is reduced by 12 fold.Therefore,our proposed method has simultaneously improved temporal(via the DNN)and spatial(via the localization method)resolutions in both label-free microscopy and labeled tomography.Deep-learning powered localization PA imaging can potentially provide a practical tool in preclinical and clinical studies requiring fast temporal and fine spatial resolutions.

Jongbeom Kim;Gyuwon Kim;Lei Li;Pengfei Zhang;Jin Young Kim;Yeonggeun Kim;Hyung Ham Kim;Lihong V.Wang;Seungchul Lee;Chulhong Kim

Departments of Electrical Engineering,Mechanical Engineering,Convergence IT Engineering,and Interdisciplinary Bioscience and Bioengineering,Graduate School of Artificial Intelligence,Medical Device Innovation Center,Pohang University of Science and Technology(POSTECH),77 Cheongam-ro,Nam-gu,Pohang,Gyeongbuk 37673,Republic of Korea;Caltech Optical Imaging Laboratory,Andrew and Peggy Cherng Department of Medical Engineering,Department of Electrical Engineering,California Institute of Technology,1200 E.California Blvd.,MC 138-78,Pasadena,CA 91125,USA;School of Precision Instruments and Optoelectronics Engineering,Tianjin University,92 Weijin Road,Nankai District,Tianjin 300072,China;Opticho,532,CHANGeUP GROUND,87 Cheongam-ro,Nam-gu,Pohang,Gyeongsangbuk 37673,Republic of Korea

光：科学与应用（英文版）

[1]Jongbeom Kim;Gyuwon Kim;Lei Li;Pengfei Zhang;Jin Young Kim;Yeonggeun Kim;Hyung Ham Kim;Lihong V.Wang;Seungchul Lee;Chulhong Kim-.Deep learning acceleration of multiscale superresolution localization photoacoustic imaging)[J].光：科学与应用（英文版）,2022(06):1166-1177

superresolution

Deep,learning,acceleration,multiscale,localization,photoacoustic,imaging,approach,that,localizes,very,small,targets,such,blood,cells,droplets,injected,dye,has,significantly,improved,spatial,various,biological,medical,modalities,However,this,superior,achieved,by,sacrificing,temporal,because,many,raw,frames,each,containing,must,superimposed,sufficiently,sampled,high,density,Here,demonstrate,computational,strategy,deep,neural,networks,DNNs,reconstruct,images,from,far,fewer,applied,both,free,optical,microscopy,PAM,2D,labeled,computed,tomography,PACT,For,former,required,number,volumetric,reduced,tens,than,latter,fold,Therefore,our,proposed,method,simultaneously,via,resolutions,powered,potentially,provide,practical,tool,preclinical,studies,requiring,fast,fine

0.52106