Deep Learning-Based MR Image Re-parameterization
| Autor: | Narang, Abhijeet, Raj, Abhigyan, Pop, Mihaela, Ebrahimi, Mehran |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/CSCE60160.2023.00094 |
| Popis: | 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. Comment: A. Narang, A. Raj, M. Pop and M. Ebrahimi, "Deep Learning-Based MR Image Re-parameterization," 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE), Las Vegas, NV, USA, 2023, pp. 536-541, doi: 10.1109/CSCE60160.2023.00094 |
| Databáze: | arXiv |
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