A Modular Deep Learning-based Approach for Diffuse Optical Tomography Reconstruction
<|reference_start|>A Modular Deep Learning-based Approach for Diffuse Optical Tomography Reconstruction: 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.<|reference_end|>
@article{benfenati2024a,
    title={A Modular Deep Learning-based Approach for Diffuse Optical Tomography
  Reconstruction},
    author={Alessandro Benfenati, Paola Causin, Martina Quinteri},
    journal={arXiv preprint arXiv:2402.09277},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09277},
    primaryClass={math.NA cs.NA eess.IV}
}