@inproceedings{e45a9e2c6bda4b8883753b5884cddd1d,
title = "Learning-based enhancement of limited-view optoacoustic tomography based on image- and time-domain data",
abstract = "Optoacoustic images are often afflicted with distortions and artifacts corresponding to system limitations, including limited-view tomographic data. We developed a convolutional neural network (CNN) approach for optoacoustic image quality enhancement combining training on both time-resolved signals and tomographic reconstructions. Reference human finger data for training the CNN were recorded using a full-ring array system with optimal tomographic coverage. The reconstructions were further refined with a dedicated algorithm that minimizes acoustic reflection artifacts induced by acoustically mismatch structures, such as bones. The combined methodology is shown to outperform other CNN-based methods solely operating on image-domain data.",
keywords = "Deep learning, Image enhancement, Limited-view artifacts, Optoacoustics, Photoacoustics",
author = "Neda Davoudi and Berkan Lafci and Ali {\"O}zbek and De{\'a}n-Ben, {Xos{\'e} Lu{\'i}s} and Daniel Razansky",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; Photons Plus Ultrasound: Imaging and Sensing 2022 ; Conference date: 20-02-2022 Through 24-02-2022",
year = "2022",
doi = "10.1117/12.2608717",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Oraevsky, {Alexander A.} and Wang, {Lihong V.}",
booktitle = "Photons Plus Ultrasound",
}