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Deep Learning-Based MR Image Re-parameterization

2022-06-11 12:39:37
Abhijeet Narang, Abhigyan Raj, Mihaela Pop, Mehran Ebrahimi
arXiv_CV
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2206.05516

PDF

https://arxiv.org/pdf/2206.05516.pdf

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