Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA.
Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA.
J Biophotonics. 2023 Nov;16(11):e202300142. doi: 10.1002/jbio.202300142. Epub 2023 Jul 20.
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%.
多光谱光声断层扫描（MSOT）是一种用于诊断和分析生物样本的有益技术，因为它提供了精细的解剖学和生理学细节。然而，获取高穿透分辨率的体积 MSOT 是耗时的。在这里，我们提出了一种基于混合递归和卷积神经网络的深度学习模型，用于生成 MSOT 系统的序列横截面图像。该系统在单次扫描中提供三种模式（MSOT、超声和特定外源性造影剂的光声成像）。本研究使用吲哚菁绿（ICG）偶联纳米线颗粒（NWs-ICG）作为造影剂。我们可以接收两个步长为 0.6mm 的图像作为输入，而不是以 0.1mm 的步长获取七张图像。对于提出的深度学习模型，可以生成另外五张图像，步长为这两张输入图像之间的 0.1mm，这意味着我们可以将采集时间缩短约 71%。
