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arXiv · 2206.05516

Deep Learning-Based MR Image Re-parameterization

Abhijeet Narang·Abhigyan Raj·Mihaela Pop·Mehran Ebrahimi
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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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Abhijeet Narang, Abhigyan Raj, Mihaela Pop, Mehran Ebrahimi. 2022-06-11. Deep Learning-Based MR Image Re-parameterization. https://doi.org/10.1109/csce60160.2023.00094

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