标题： 基于模块化的深度学习扩散光学断层重建方法 显示英文标题

标题： A Modular Deep Learning-based Approach for Diffuse Optical Tomography Reconstruction

摘要： 医学影像是当今诊断和治疗随访的支柱。当前的研究试图将已建立的但具有电离辐射的断层扫描技术与提供降低辐射暴露的技术相结合。扩散光学断层扫描（DOT）使用非电离的近红外（NIR）窗口光来重建生物体内的光学系数，为研究的器官/组织的组成提供功能指示。然而，由于近红外波长下的主要光散射，DOT重建是一个严重不适定的逆问题。传统的重建方法在处理稍微复杂的情况时表现出严重的弱点，并且计算上非常密集。在这项工作中，我们探索了深度学习技术用于DOT反演。具体而言，我们提出了一种基于模块化概念的完全数据驱动的方法：首先通过自动编码器分别处理数据和原始信号，然后通过一个桥接网络连接相应的低维潜在空间，该网络同时作为学习到的正则化器起作用。 显示英文摘要

摘要： Medical imaging is nowadays a pillar in diagnostics and therapeutic follow-up. Current research tries to integrate established - but ionizing - tomographic techniques with technologies offering reduced radiation exposure. Diffuse Optical Tomography (DOT) uses non-ionizing light in the Near-Infrared (NIR) window to reconstruct optical coefficients in living beings, providing functional indications about the composition of the investigated organ/tissue. Due to predominant light scattering at NIR wavelengths, DOT reconstruction is, however, a severely ill-conditioned inverse problem. Conventional reconstruction approaches show severe weaknesses when dealing also with mildly complex cases and/or are computationally very intensive. In this work we explore deep learning techniques for DOT inversion. Namely, we propose a fully data-driven approach based on a modularity concept: first data and originating signal are separately processed via autoencoders, then the corresponding low-dimensional latent spaces are connected via a bridging network which acts at the same time as a learned regularizer.