Space-Time Double-Constrained AI Method for Biomedical Monitoring

In this work, a novel inverse scattering (IS) method for imaging and monitoring the evolution of biological pathologies is presented. More specifically, a physics-informed artificial intelligence (AI) approach formulated within the System-by-Design (SbD) framework is employed to efficiently retrieve accurate qualitative and quantitative information on time-evolving tumoral tissues. Towards this end, the simultaneous exploitation of spatial and temporal priors is introduced with the aim of regularizing the IS problem at hand through a double-constrained (DC) formulation and solution approach. A numerical example, concerned with breast tumor imaging, is shown to preliminarily assess the effectiveness of the proposed DC-SbD technique.