Abstract

In the past decades, the use of Computerized Tomography (CT) has increased dramatically owing to its excellent diagnostic performance, easy accessibility, short scanning time, and cost-effectiveness. Enabling CT technologies with a reduced/null radiation dose while preserving/enhancing the diagnostic quality is a key challenge in modern medical imaging. Increased noise levels are, however, an expected downfall of all these new technologies. In this talk we will refer about our research focused on Diffuse Optical Tomography (DOT), a CT technology based on NIR light as investigating signal [1]. Strong light scattering in biological tissues makes DOT reconstruction a severely ill-conditioned problem, so that denoising is a crucial step. After a brief description of the DOT modality, first we will present our results in exploring variational approaches based on partial differential equation models endowed with different regularizers to compute a stable DOT -CT reconstruction [2,3]. Then, we will discuss our more recent research on the use of DL-based generative models to produce more effective soft priors which, used in combination with standard forward problem solvers or DL-based forward problem solvers allow to improve spatial resolution in high contrast zones and reduce noise in low-contrast zones, typical of medical imaging.   [1] Arridge, S. R. (1999). Optical tomography in medical imaging. Inverse problems, 15(2), R41 . [2] Causin, P., Lupieri, M. G., Naldi, G., & Weishaeupl, R. M. (2020). Mathematical and numerical challenges in optical screening of female breast. International Journal for Numerical Methods in Biomedical Engineering, 36(2), e3286. [3] Benfenati, A., Causin, P., Lupieri, M. G., & Naldi, G. (2020, March). Regularization techniques for inverse problem in DOT applications. In Journal of Physics: Conference Series (Vol. 1476, No. 1, p. 012007). IOP Publishing.