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Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography (MSOT) imaging
Author(s)
Juhong, Aniwat
Li, Bo
Liu, Yifan
Yao, Cheng You
Yang, Chia Wei
Agnew, Dalen W.
Lei, Yu Leo
Luker, Gary D.
Bumpers, Harvey
Huang, Xuefei
Qiu, Zhen
Date Issued
November 1, 2023
Type
Article
Abstract
Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through-plane resolution volumetric MSOT is time-consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross-sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG-conjugated nanoworms particles (NWs-ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.
Citation
Journal of Biophotonics, 16(11), 2023