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Deep learning acceleration of multiscale superresolution localization photoacoustic imaging

Light(Science & Applications) 2022年第6期11卷 1166-1177页

作者： Jongbeom kim Gyuwon kim Lei Li Pengfei Zhang Jin Young kim yeonggeun kim Hyung Ham Kkim Lihong V.Wang Seungchul Lee Chulhong kim Departments of Electrical Engineering Mechanical EngineeringConvergence IT Engineeringand Interdisciplinary Bioscience and BioengineeringGraduate School of Artificial IntelligenceMedical Device Innovation CenterPohang University of Science and Technology(POSTECH)77 Cheongam-roNam-guPohangGyeongbuk 37673Republic of Korea Caltech Optical Imaging Laboratory Andrew and Peggy Cherng Department of Medical EngineeringDepartment of Electrical EngineeringCalifornia Institute of Technology1200 E.California Blvd.MC 138-78PasadenaCA 91125USA School of Precision Instruments and Optoelectronics Engineering Tianjin University92 Weijin RoadNankai DistrictTianjin 300072China Opticho 532CHANGeUP GROUND87 Cheongam-roNam-guPohangGyeongsangbuk 37673Republic of Korea

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 ***,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 ***,we demonstrate a computational strategy based on deep neural networks(DNNs)to reconstruct high-density superresolution images from far fewer raw image *** 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 *** the latter,the required number of raw 2D frames is reduced by 12 ***,our proposed method has simultaneously improved temporal(via the DNN)and spatial(via the localization method)resolutions in both label-free microscopy and labeled ***-learning powered localization PA imaging can potentially provide a practical tool in preclinical and clinical studies requiring fast temporal and fine spatial resolutions.

关键词： Deep frames networks

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