Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography (MSOT) imaging

• Published in: Journal of biophotonics Vol. 16; no. 11; p. e202300142
• Main Authors: 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, Piyawattanametha, Wibool, Qiu, Zhen
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.11.2023; Wiley Blackwell (John Wiley & Sons)
• Subjects: Artificial neural networks; Biological properties; Biological samples; Contrast agents; Contrast media; Deep learning; Image acquisition; Image contrast; Nanostructured materials; Neural networks; Tomography; multispectral optoacoustic tomography; convolutional neural networks; recurrent neural networks; volumetric imaging
• ISSN: 1864-063X; 1864-0648
• DOI: 10.1002/jbio.202300142

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%.