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.

翻译：医学影像如今已成为诊断和治疗监测的重要支柱。当前研究致力于将成熟的电离断层成像技术与辐射暴露更低的替代技术相结合。漫射光学断层成像（DOT）利用近红外（NIR）窗口内的非电离光重建活体组织的光学系数，提供所研究器官/组织成分的功能性信息。然而，由于近红外波段光散射效应显著，DOT重建本质上是一个严重病态逆问题。传统重建方法在处理中等复杂病例时存在明显缺陷，且/或计算开销极大。本研究探索用于DOT逆问题的深度学习技术，具体提出一种基于模块化概念的纯数据驱动方法：首先通过自编码器分别处理原始数据与待重建信号，继而利用兼具学习正则化功能的桥接网络，将对应的低维潜在空间进行连接。