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

2024/02/14 by Benfenati, Alessandro, Causin, Paola, Quinteri, Martina
#65Z05 #68T07 #FOS: Electrical engineering #FOS: Mathematics #Image and Video Processing (eess.IV) #Numerical Analysis (math.NA) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2402.09277

Abstract

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.

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