A unified framework for Multi-contrast ANalysis of TIssue microStructure

Magnetic Resonance Imaging (MRI) provides a uniquely versatile, non-invasive window into tissue microstructure, thanks to its wide array of contrast mechanisms. Yet, each contrast relies on its own biophysical model to extract quantitative information. This siloed approach often leads to fragmented, inconsistent, or even contradictory representations of the same tissue features, introducing confounding factors and limiting interpretability. This project aims to develop a unified multi-contrast MRI framework that integrates four key contrasts—T1 and T2 relaxation, magnetization transfer, and diffusion—within a single, coherent biophysical model of tissue microstructure. By capturing both shared and complementary information across contrasts, the framework will overcome critical limitations of current single-contrast approaches and improve the specificity and robustness of microstructural imaging. The methodology combines innovative acquisition strategies with advanced signal modeling and optimization, designed to enhance sensitivity across contrasts. Development will be carried out using a cutting-edge experimental pipeline that includes brain organoids, numerical simulations, and tissue-mimicking phantoms. The framework will be validated against histological biomarkers for tissue microstructure in a rodent model of Multiple Sclerosis (MS)—a disease marked by multiple overlapping pathological mechanisms—where it holds strong potential for improved disease characterization and biomarker discovery. By bridging MRI physics, computational modeling, and experimental neurobiology, this project will lay the groundwork for a new generation of quantitative MRI tools with high translational value for neuroscience and clinical research.