569-03-004 ISMRM Abstract

Enhancing Low-Field MRI Image Quality for Nipah Virus Infection Imaging using Deep Learning

Primary: Analysis Methods - Image Enhancement

Secondary: Physics & Engineering - Low-Field MRI

569-03-004 · Super-Resolving MRI: Methods and Applications · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row J

Accepted

Ajay Sharma ¹, Ivan Etoku Oiye¹, Russell Byrum², Michael Holbrook², Yu Cong², Claudia Calcagno², Venkatesh Mani², Sairam Geethanath¹

¹Johns Hopkins University School of Medicine, Baltimore, United States of America

²Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Fort Detrick, Frederick, United States of America

Presenting Author: Ajay Sharma

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.

References

1. Alam AM. Nipah virus, an emerging zoonotic disease causing fatal encephalitis. Clinical Medicine, Journal of the Royal College of Physicians of London. 2022;22(4). doi:10.7861/clinmed.2022-0166 2. Sharma V, Kaushik S, Kumar R, Yadav JP, Kaushik S. Emerging trends of Nipah virus: A review. Rev Med Virol. 2019;29(1). doi:10.1002/rmv.2010 3. Aggarwal K, Cong Y, Lee JH, et al. Feasibility of textural analysis and very-low-field magnetic resonance for imaging Nipah virus infection. doi:10.58530/2023/2078 4. Poojar P, Oiye IE, Aggarwal K, Jimeno MM, Vaughan JT, Geethanath S. Repeatability of image quality in very low‐field MRI. NMR Biomed. 2024;37(10). doi:10.1002/nbm.5198 5. U.S. Department of Health and Human Services. Biosafety FAQs. Science Safety Security. 6. Ssentamu T, Kimbowa A, Omoding R, et al. Denoising very low-field magnetic resonance images using native noise modeling. Frontiers in Neuroimaging. 2025;4:1501801. 7. Wang Z, Chen J, Hoi SCH. Deep Learning for Image Super-Resolution: A Survey. IEEE Trans Pattern Anal Mach Intell. 2021;43(10). doi:10.1109/TPAMI.2020.2982166 8. Yang CY, Ma C, Yang MH. Single-image super-resolution: A benchmark. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 8692 LNCS. 2014. doi:10.1007/978-3-319-10593-2_25 9. Thanh CQT, Hai NT. Trilinear Interpolation Algorithm for Reconstruction of 3D MRI Brain Image. American Journal of Signal Processing. 2017;2017(1). 10. Satapathy SC, Rajinikanth V. Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI. Journal of Optimization. 2018;2018(1). doi:10.1155/2018/3738049 11. Mafi M, Martin H, Cabrerizo M, Andrian J, Barreto A, Adjouadi M. A comprehensive survey on impulse and Gaussian denoising filters for digital images. Signal Processing. 2019;157. doi:10.1016/j.sigpro.2018.12.006 12. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015. Springer International Publishing; 2015:234-241. 13. Oktay O, Schlemper J, Folgoc L Le, et al. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999. Published online 2018. 14. Sharma A, Mishra PK. DRI-UNet: dense residual-inception UNet for nuclei identification in microscopy cell images. Neural Comput Appl. 2023;35(26):19187-19220. 15. Horé A, Ziou D. Image quality metrics: PSNR vs. SSIM. In: Proceedings - International Conference on Pattern Recognition. 2010. doi:10.1109/ICPR.2010.579 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/569-03-004