Deep Learning-Based MR Image Re-parameterization

• URL: http://arxiv.org/abs/2206.05516v2
• Date: Fri, 12 Apr 2024 06:51:06 GMT
• Title: Deep Learning-Based MR Image Re-parameterization
• Authors: Abhijeet Narang, Abhigyan Raj, Mihaela Pop, Mehran Ebrahimi,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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