303-01-002 / 271-01-011 ISMRM Abstract

Initial Clinical Evaluation of DeepAcq: Ultra-Fast Multi-Contrast MRI and T₁/T₂ Mapping in Multiple Sclerosis

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Acquisition & Reconstruction - AI methods

303-01-002 · AI Methods Beyond Reconstruction · Monday, 11 May, 8:20 AM–10:10 AM · Auditorium 1

271-01-011 · ISMRM AMPC Selected Posters · Sunday, 10 May, 7:00 AM–2:00 PM · Traditional Posters

Keywords: Machine Learning/Artificial Intelligence Multiple Sclerosis Quantitative MRI Image synthesis Multi-contrast MRI

Accepted

Beril Alyuz ^1,2, Shihan Qiu¹, Hsu-Lei Lee¹, Chang Gao³, Sreekanth Madhusoodhanan ⁴, Dan Ruan^2,5,6, Nancy Sicotte⁴, Pascal Sati^1,4, Yibin Xie¹, Debiao Li^1,2

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, United States of America

²Department of Bioengineering, University of California Los Angeles, Los Angeles, United States of America

³MR R&D Collaborations, Siemens Medical Solutions, Los Angeles, United States of America

⁴Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, United States of America

⁵Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, United States of America

⁶Radiation Oncology, UCLA Health System, United States of America

Presenting Author: Beril Alyuz

Synopsis

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References

1. Alyuz B, Qiu S, Lee H-L, et al. Ultra-fast high-resolution multi-contrast qualitative and quantitative MRI of the entire brain in 3 minutes. ISMRM Annual Meeting. doi:10.58530/2025/0752 [doi]

2. ANTsX. ANTsX/antspy: A fast medical imaging analysis library in python with algorithms for registration, segmentation, and more. GitHub. Accessed September 18, 2025. https://github.com/ANTsX/ANTsPy.

3. Fischl B. Freesurfer. NeuroImage. 2012;62(2):774-781. doi:10.1016/j.neuroimage.2012.01.021 [doi]

4. Xin B, Ye M, Axel L, Metaxas DN. Fill the K-space and refine the image: Prompting for dynamic and multi-contrast MRI reconstruction. Lecture Notes in Computer Science. Published online 2024:261-273. doi:10.1007/978-3-031-52448-6_25 [doi]

5. Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and Super-Resolution. Lecture Notes in Computer Science. Published online 2016:694-711. doi:10.1007/978-3-319-46475-6_43 [doi]

6. Haber E, Modersitzki J. Intensity gradient based registration and fusion of multi-modal images. Lecture Notes in Computer Science. Published online 2006:726-733. doi:10.1007/11866763_89 [doi]

7. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging. 2001;20(1):45-57. doi:10.1109/42.906424 [doi]

8. Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage. 2011;56(3):907-922. doi:10.1016/j.neuroimage.2011.02.046 [doi]

9. Valcarcel AM, Linn KA, Vandekar SN, et al. MIMoSA: An automated method for intermodal segmentation analysis of multiple sclerosis brain lesions. Journal of Neuroimaging. 2018;28(4):389-398. doi:10.1111/jon.12506 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/303-01-002