# Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging

## TL;DR

This paper introduces a physics-based modeling approach for MRI that combines explicit physical constraints with compressed sensing to improve multi-contrast and quantitative imaging, enabling better image quality and parameter recovery.

## Contribution

It presents a novel framework integrating physics-based models with compressed sensing for MRI reconstruction and quantitative parameter mapping.

## Key findings

- Enhanced image reconstruction quality from highly accelerated scans
- Successful multi-contrast imaging using physics-based constraints
- Accurate recovery of bio-physical parameters from MRI data

## Abstract

Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transform, finite differences, and low rank extensions. Though powerful, these image priors are phenomenological in nature and do not account for the mechanism behind the image formation. On the other hand, MRI signal dynamics are governed by physical laws, which can be explicitly modeled and used as priors for reconstruction. {1}These explicit and implicit signal priors can be synergistically combined in an inverse problem framework to recover sharp, multi-contrast images from highly accelerated scans. Furthermore, the physics-based constraints provide a recipe for recovering quantitative, bio-physical parameters from the data. This article introduces physics-based modeling constraints in MRI and shows how they can be used in conjunction with compressed sensing for image reconstruction and quantitative imaging. We describe model-based quantitative MRI, as well as its linear subspace approximation. We also discuss approaches to selecting user-controllable scan parameters given knowledge of the physical model. We present several MRI applications that take advantage of this framework for the purpose of multi-contrast imaging and quantitative mapping.