Deep learning acceleration of multiscale superresolution localization photoacoustic imaging
作者机构：Departments of Electrical EngineeringMechanical 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 LaboratoryAndrew 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 EngineeringTianjin University92 Weijin RoadNankai DistrictTianjin 300072China Opticho532CHANGeUP GROUND87 Cheongam-roNam-guPohangGyeongsangbuk 37673Republic of Korea
出 版 物：《Light(Science & Applications)》 (光（科学与应用)（英文版）)
年 卷 期：2022年第11卷第6期
页 面：1166-1177页
核心收录：
学科分类：070207[理学-光学] 07[理学] 08[工学] 0803[工学-光学工程] 0702[理学-物理学]
基 金：This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(2020R1A6A1A03047902) supported by National R&D Program through the NRF funded by the Ministry of Science and ICT(MSIT)(2020M3H2A1078045) supported by the NRF grant funded by the Korea government MSIT(No.NRF-2019R1A2C2006269 and No.2020R1C1C1013549) This work was partly supported by the Institute of Information&communications Technology Planning&Evaluation(ITP)grant funded by the Korea government MSIT(No.2019-0-01906,Artificial Intelligence Graduate School Program(POSTECH)) Korea Evaluation Institute of Industrial Technology(KEIT)grant funded by the Ministry of Trade,industry and Energy(MOTIE) This work was also supported by the Korea Medical Device Development Fund grant funded by the MOTIE(9991007019,KMDF_PR_20200901_0008) It was also supported by the BK21 Four project
摘 要：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.
维普期刊数据库