461-03-011 ISMRM Abstract

Deep Learning-Based Motion Artifact Correction in DSC MRI using PINN-Based Perfusion-Aware Loss

Primary: Acquisition & Reconstruction - Motion Correction: Neuro

Secondary: Acquisition & Reconstruction - Image Reconstruction: AI

461-03-011 · Artifact Correction Strategies in Brain and Body · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row B

Keywords: DSC & DCE Perfusion Perfusion-aware loss Restormer Motion artifact correction Deep learning-based reconstruction (DLR)

Accepted

Sojeong Kim ¹, Asaduddin Muhammad¹, Sung-Hong Park¹

¹Department of Bio and brain engineering, Korea Advanced Institute of Science & Technology, Daejeon, Korea, Republic of

Presenting Author: Sojeong Kim

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. Kiselev VG. On the theoretical basis of perfusion measurements by dynamic susceptibility contrast MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine. 2001;46(6):1113-22. https://doi.org/10.1002/mrm.1307 [doi]

2. Calamante F. Perfusion MRI using dynamic-susceptibility contrast MRI: quantification issues in patient studies. Topics in magnetic resonance imaging. 2010;21(2):75-85. DOI: 10.1097/RMR.0b013e31821e53f5 [doi]

3. Kim HJ, Lee SB, Choi JW, Jeon YS, Lee HJ, Park JJ, et al. Multiphase MR angiography collateral map: functional outcome after acute anterior circulation ischemic stroke. Radiology. 2020;295(1):192-201. https://doi.org/10.1148/radiol.2020191712 [doi]

4. Blackham KA, Passalacqua MA, Sandhu GS, Gilkeson RC, Griswold MA, Gulani V. Applications of time-resolved MR angiography. American Journal of Roentgenology. 2011;196(5):W613-W20. https://doi.org/10.2214/AJR.10.4227 [doi]

5. Ronneberger O, Fischer P, Brox T, editors. U-net: Convolutional networks for biomedical image segmentation. Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18; 2015: Springer. https://doi.org/10.1007/978-3-319-24574-4_28 [doi]

6. Zamir SW, Arora A, Khan S, Hayat M, Khan FS, Yang M-H, editors. Restormer: Efficient transformer for high-resolution image restoration. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2022. https://doi.org/10.48550/arXiv.2111.09881 [doi]

7. Asaduddin M, Kim EY, Park SH. SPINNED: Simulation‐based physics‐informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data. Magnetic Resonance in Medicine. 2024;92(3):1205-18. https://doi.org/10.1002/mrm.30095 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/461-03-011