461-02-011 ISMRM Abstract

Robust quantification of CBF and ATT in multi-delay PCASL with fewer PLDs and averages using a CNN-Transformer framework

Primary: Acquisition & Reconstruction - AI methods

Secondary: Contrast Mechanisms - Arterial Spin Labelling

461-02-011 · AI To Make Protocols, Plan, QC, and Correct Motion · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row B

Keywords: Deep learning PCASL Arterial transit time (ATT) Cerebral blood flow (CBF)

Accepted

Shibiao Wang¹, Yining He^1,2,3, Sang Hun Chung³, Lirong Yan ⁴

¹Northwestern University, Chicago, United States of America

²Biomedical Engineering, Northwestern University, Chicago, United States of America

³Radiology, Northwestern University, Chicago, United States of America

⁴Northwestern University Feinberg School of Medicine, Chicago, United States of America

Presenting Author: Lirong Yan

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References

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2. Pinto, Joana, et al. "Modelling spatiotemporal dynamics of cerebral blood flow using multiple-timepoint arterial spin labelling MRI." Frontiers in Physiology 14 (2023): 1142359. https://doi.org/10.3389/fphys.2023.1142359 [doi]

3. Zhang, Logan X., et al. "Examination of optimized protocols for pCASL: Sensitivity to macrovascular contamination, flow dispersion, and prolonged arterial transit time." Magnetic resonance in medicine 86.4 (2021): 2208-2219. doi: 10.1002/mrm.28839 [doi]

4. Uniken Venema, S.M., Bhogal, A., Dankbaar, J.W. et al. Benefits and challenges of multi-delay arterial spin labeling in clinical practice: measuring perfusion and cerebrovascular reactivity in intracranial steno-occlusive disease. Insights Imaging 16, 197 (2025). https://doi.org/10.1186/s13244-025-02077-4 [doi]

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6. N. J. Luciw, Z. Shirzadi, S. E. Black, M. Goubran, and B. J. MacIntosh, “Automated generation of cerebral blood flow and arterial transit time maps from multiple delay arterial spin-labeled MRI,” Magn. Reson. Med., vol. 88, no. 1, pp. 406–417, 2022. https://doi.org/10.1002/mrm.29674 [doi]

7. Li, Thomas Z et al. “Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography.” Proceedings of SPIE--the International Society for Optical Engineering vol. 12464 (2023): 1246412. doi:10.1117/12.2653911 [doi]

8. Li, Jun, et al. "Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives." Medical image analysis 85 (2023): 102762. https://doi.org/10.1016/j.media.2023.102762 [doi]

9. Shou, Qinyang, et al. "Transformer‐based deep learning denoising of single and multi‐delay 3D arterial spin labeling." Magnetic resonance in medicine 91.2 (2024): 803-818. doi: 10.1002/mrm.29887 [doi]

10. Huber, Peter J. “Robust estimation of a location parameter.” Breakthroughs in statistics: Methodology and distribution. New York, NY: Springer New York, 1992. 492-518. Doi: 10.1007/978-1-4612-4380-9_35 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/461-02-011