Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026
· Cape Town, South Africa
569-03-008
ISMRM Abstract
Revolutionizing MRI Reformatting: ESRGAN Transfer Learning for Through-Plane Resolution Enhancement
Primary:
Acquisition & Reconstruction - Image Reconstruction: AI
Secondary:
Physics & Engineering - Hybrid & Novel Systems Technology
569-03-008 · Super-Resolving MRI: Methods and Applications
· Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row J
Keywords:MR Image ReformattingSpatial Resolution EnhancementThrough-Plane Resolution Enhancement (T-PRE)MRI-ESRGANDeep Learning Model
Accepted
YASHWANT K KURMI 1,2, Malvika Viswanathan1,3, Zhongliang Zu1,2,3
1Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, United States of America
2Vanderbilt University Medical Center, Nashville, United States of America
3Vanderbilt University, Nashville, United States of America
Presenting Author: YASHWANT K KURMI
Synopsis
Motivation:
Goals:
Approach:
Results:
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
1. Crooks LEaODAaKLaHJaAMaWJaCCRaB-Z. Clinical efficiency of nuclear magnetic resonance imaging. Radiology 1983; 146 (1):123-128. PMID: 6849032 DOI: 10.1148/radiology.146.1.6849032. [doi][pmid]
2. Frank Fischbach and Michael Müller and Harald B. High-resolution depiction of the cranial nerves in the posterior fossa (N III–N XII) with 2D fast spin echo and 3D gradient echo sequences at 3.0 T. Clinical Imaging 2009; 33 (3):169-174. PMID: 19411020 DOI: 10.1016/j.clinimag.2008.09.012. [doi][pmid]
4. Srinivasan SaWHHaSKaMDJAaEDB. Fast 3D T2-weighted imaging using variable flip angle transition into driven equilibrium (3D T2-TIDE) balanced SSFP for prostate imaging at 3T. Magnetic Resonance in Medicine 2015; 74 (2):442-451. PMID: 25195659 DOI: 10.1002/mrm.25430 [doi][pmid]
5. Gold GE, Busse Rf Fau - Beehler C, Beehler C Fau - Han E, Han E Fau - Brau ACS, Brau Ac Fau - Beatty PJ, Beatty Pj Fau - Beaulieu CF, et al. Isotropic MRI of the knee with 3D fast spin-echo extended echo-train acquisition (XETA): initial experience. (1546-3141 (Electronic)). PMID: 17449772 DOI: 10.2214/AJR.06.1208 [doi][pmid]
6. Rosenkrantz AB, Neil J, Kong X, Melamed J, Babb JS, Taneja SS, et al. Prostate Cancer: Comparison of 3D T2-Weighted With Conventional 2D T2-Weighted Imaging for Image Quality and Tumor Detection. American Journal of Roentgenology 2010; 194 (2):446-452. PMID: 20093608 DOI: 10.2214/AJR.09.3217. [doi][pmid]
7. Aiken AH, Mukherjee P Fau - Green AJ, Green Aj Fau - Glastonbury CM, Glastonbury CM. MR imaging of optic neuropathy with extended echo-train acquisition fluid-attenuated inversion recovery. (1936-959X (Electronic)). Epub 2010 Dec 23. PMID: 21183615; PMCID: PMC7965711. doi: 10.3174/ajnr.A2391. [doi][pmid]
8. Peled HGaGOaNKaS. MRI inter-slice reconstruction using super-resolution. Magnetic Resonance Imaging 2002; 20 (5):437-446. PMID: 12206870 DOI: 10.1016/s0730-725x(02)00511-8. [doi][pmid]
9. Okanovic M, Hillig B, Breuer F, Jakob P, Blaimer M. Time-of-flight MR-angiography with a helical trajectory and slice-super-resolution reconstruction. (1522-2594 (Electronic)). PMID: 29527736 DOI: 10.1002/mrm.27167. [doi][pmid]
10. Kargar SaBEAaFATaGRCaKAaKBFaSEGaRSJ. Use of k-space for high through-plane resolution in multislice MRI: Application to prostate. Magnetic Resonance in Medicine 2019; 81 (6):3691-3704. PMID: 30844092 DOI: 10.1002/mrm.27691. [doi][pmid]
11. Application of Tikhonov Regularization to Super-Resolution Reconstruction of Brain MRI Images. In: Gao XaMHaLMJaCRaLS editor. Medical Imaging and Informatics: Springer Berlin Heidelberg; 2008. pp. 51--56. DOI: http://dx.doi.org/10.1007/978-3-540-79490-5_8. [doi]
12. Tibrewala R, Dutt T, Tong A, Ginocchio L, Lattanzi R, Keerthivasan MB, et al. FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging. Scientific Data 2024; 11 (1):404. PMID: 38643291 PMCID: PMC11032332 DOI: 10.1038/s41597-024-03252-w. [doi][pmid]
13. Knoll FA-O, Zbontar J, Sriram AA-O, Muckley MA-O, Bruno MA-O, Defazio A, et al. fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. (2638-6100 (Electronic)). PMID: 32076662 PMCID: PMC6996599 DOI: 10.1148/ryai.2020190007. [doi][pmid]
14. Masoudi SA-O, Harmon SA-O, Mehralivand SA-O, Walker SA-O, Raviprakash HA-O, Bagci UA-O, et al. Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research. (2329-4302 (Print)). PMID: 33426151 PMCID: PMC7790158 DOI: 10.1117/1.JMI.8.1.010901. [doi][pmid]
15. Litjens GDOBJKN, Huisman H. PROSTATEx Challenge Data. The Cancer Imaging Archive (2017) 2017:1327-1344 , doi= https //doi.org/1310.7937/K1329TCIA.
16. Elad MaFA. Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images. IEEE Transactions on Image Processing 1997; 6 (12):1646-1658. PMID: 18285235 DOI: 10.1109/83.650118. [doi][pmid]
17. Liu Y-QaDXaSH-LaCS-J. Estimating Generalized Gaussian Blur Kernels for Out-of-Focus Image Deblurring. IEEE Transactions on Circuits and Systems for Video Technology 2021; 31 (3):829-843. doi: 10.1109/TCSVT.2020.2990623. [doi]
18. Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. (0740-3194 (Print)). PMID: 8598820 PMCID: PMC2254141 DOI: 10.1002/mrm.1910340618. [doi][pmid]
19. Schönfeld EaSBaKA. A U-Net Based Discriminator for Generative Adversarial Networks. 2020. pp. 8204-8213. doi: 10.1109/CVPR42600.2020.00823. [doi]
20. Takeru Miyato and Toshiki Kataoka and Masanori Koyama and Yuichi Y. Spectral Normalization for Generative Adversarial Networks. International Conference on Learning Representations2018. DOI: 10.48550/arXiv.1802.05957. [doi]
21. Wang XaXLaDCaSY. Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. 2021. pp. 1905-1914. doi: 10.1109/ICCVW54120.2021.00217. [doi]
22. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Computational and Biological Learning Society; 2015. pp. 1-14. https://doi.org/10.48550/arXiv.1409.1556. [doi]
23. Vint DaDCGaSJJaLRAaHD. Evaluation of Performance of VDSR Super Resolution on Real and Synthetic Images. 2019. pp. 1-5. doi: 10.1109/SSPD.2019.8751651. [doi]
24. Farsiu SaRMDaEMaMP. Fast and robust multiframe super resolution. IEEE Transactions on Image Processing 2004; 13 (10):1327-1344. PMID: 15462143 DOI: 10.1109/tip.2004.834669. [doi][pmid]