# Dynamic MRI with Locally Low-Rank Subspace Constraint: Towards 1-Second Temporal Resolution Aided by Deep Learning

## TL;DR

This paper introduces a new MRI framework that improves image quality and temporal resolution for dynamic imaging, especially useful for breast cancer screening.

## Contribution

The novel framework combines locally low-rank subspace modeling with deep learning to enhance dynamic MRI performance.

## Key findings

- The framework improves contrast-to-noise ratio and reduces noise in dynamic MRI.
- It enables flexible temporal resolution down to 1 second with minimal undersampling penalties.
- The method shows potential for applications beyond breast imaging, such as head and neck and brain MRI.

## Abstract

MRI is the most effective method for screening high-risk breast cancer patients. While current exams primarily rely on the qualitative evaluation of morphological features before and after contrast administration and less on contrast kinetic information, the latest developments in acquisition protocols aim to combine both. However, balancing between spatial and temporal resolution poses a significant challenge in dynamic MRI. Here, we propose a radial MRI reconstruction framework for Dynamic Contrast Enhanced (DCE) imaging, which offers a joint solution to existing spatial and temporal MRI limitations. It leverages a locally low-rank (LLR) subspace model to represent spatially localized dynamics based on tissue information. Our framework demonstrated substantial improvement in CNR, noise reduction and enables a flexible temporal resolution, ranging from a few seconds to 1-second, aided by a neural network, resulting in images with reduced undersampling penalties. Finally, our reconstruction framework also shows potential benefits for head and neck, and brain MRI applications, making it a viable alternative for a range of DCE-MRI exams.