Deep Learning-Based MR Image Re-parameterization

TL;DR

This paper introduces a deep learning model for MRI re-parameterization, enabling the simulation of MR images with different parameters to improve diagnosis without repeated scans.

Contribution

It presents a novel convolutional deep learning approach to predict MRI contrast changes, reducing the need for multiple scans in medical diagnosis.

Findings

DL models can learn non-linear re-parameterization functions

Potential to generate diverse MRI contrasts from limited data

Improves efficiency and patient comfort in MRI diagnostics

Abstract

Magnetic resonance (MR) image re-parameterization refers to the process of generating via simulations of an MR image with a new set of MRI scanning parameters. Different parameter values generate distinct contrast between different tissues, helping identify pathologic tissue. Typically, more than one scan is required for diagnosis; however, acquiring repeated scans can be costly, time-consuming, and difficult for patients. Thus, using MR image re-parameterization to predict and estimate the contrast in these imaging scans can be an effective alternative. In this work, we propose a novel deep learning (DL) based convolutional model for MRI re-parameterization. Based on our preliminary results, DL-based techniques hold the potential to learn the non-linearities that govern the re-parameterization.