Speaker
Description
Magnetic Resonance Imaging (MRI) is a key non-invasive imaging modality offering large versatility in creating high-resolution images. Reconstructing MR images from measurements requires knowledge of the MR physics involved in the measurement process, which are highly complex (e.g., involving quantum-mechanical effects) and are commonly modeled via the Bloch equations. Recent investigations have shown that the consideration of multi-pool models using Bloch-McConnell equations are beneficial, however, the related physics are still not fully understood. We address the challenge of uncovering hidden physics in MRI through a structured model learning approach. Specifically, we build upon the fundamental Bloch model and propose its extension with a potentially non-linear source term that captures the unknown dynamics associated with the multi-pool structure of the Bloch-McConnell model. The source term is formulated as a recurrent neural network with a highly interpretable architecture, strongly inspired by principles derived from the Bloch-McConnell model. We demonstrate the versatility and generalizability of the proposed framework and evaluate the learning process using artificially generated data in a multi-pool setting.