{"title":"Full-Wave Simulations of Tomographic Optical Imaging Through Augmented Partial Factorization","authors":"Zeyu Wang;Yiwen Zhang;Chia Wei Hsu","doi":"10.1109/TCI.2024.3499747","DOIUrl":null,"url":null,"abstract":"Label-free optical imaging inside scattering media is important for many disciplines. One challenge is that the ground-truth structure is often unknown, so one cannot rigorously assess and compare different imaging schemes. Full-wave simulations can address this issue, but the heavy computing cost has restricted them to small, typically weakly scattering, systems. Here we use a recently introduced “augmented partial factorization” method to enable full-wave simulations of tomographic optical imaging deep inside multiple-scattering media. We also provide a unifying framework that models different scattering-based imaging methods including reflectance confocal microscopy, optical coherence tomography and microscopy, interferometric synthetic aperture microscopy, and the recently proposed scattering matrix tomography in the same virtual setup, so they can be directly compared to the ground truth and against each other. The ground truth enables the identification of artifacts that would typically be mistaken as being correct while setting a rigorous and uniform standard across different methods. By leveraging the latest advances in computational electromagnetics, this work brings the power, versatility, and convenience of full-wave modeling to deep imaging in the multiple-scattering regime.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1775-1785"},"PeriodicalIF":4.2000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10764744","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Imaging","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10764744/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Label-free optical imaging inside scattering media is important for many disciplines. One challenge is that the ground-truth structure is often unknown, so one cannot rigorously assess and compare different imaging schemes. Full-wave simulations can address this issue, but the heavy computing cost has restricted them to small, typically weakly scattering, systems. Here we use a recently introduced “augmented partial factorization” method to enable full-wave simulations of tomographic optical imaging deep inside multiple-scattering media. We also provide a unifying framework that models different scattering-based imaging methods including reflectance confocal microscopy, optical coherence tomography and microscopy, interferometric synthetic aperture microscopy, and the recently proposed scattering matrix tomography in the same virtual setup, so they can be directly compared to the ground truth and against each other. The ground truth enables the identification of artifacts that would typically be mistaken as being correct while setting a rigorous and uniform standard across different methods. By leveraging the latest advances in computational electromagnetics, this work brings the power, versatility, and convenience of full-wave modeling to deep imaging in the multiple-scattering regime.
期刊介绍:
The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.