<scp>ASL 4D MRA</scp> Intracranial Vessel Segmentation With Deep Learning U‐Nets
The proposed 4DST network architecture offers an overall improvement in 4D MRA vessel segmentation performance over the compared methods and provides the framework for an end-to-end trainable model for spatio-temporal datasets. Additionally, 4DST requires minimal pre/post-processing steps, rendering it an attractive solution for pulsed ASL-based 4D MRA vessel segmentation.
Authors
- Sarah J. Moum (ORCID: https://orcid.org/0000-0002-8282-2619)
- Zhitao Li (ORCID: https://orcid.org/0000-0003-3462-6549)
- Chase Krumpelman
- Sameer A. Ansari (ORCID: https://orcid.org/0000-0001-8176-4616)
- Sang Hun Chung (ORCID: https://orcid.org/0000-0002-0605-9420)
- Zihan Wang (ORCID: https://orcid.org/0000-0001-7527-5955)
- Lirong Yan (ORCID: https://orcid.org/0000-0002-5173-7059)
- Tianrui Zhao (ORCID: https://orcid.org/0009-0007-0889-5629)
Institutions
- Northwestern University (US)
- Lurie Children's Hospital (US)
- McCormick (United States) (US)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2025-11-09
- DOI
- https://doi.org/10.1002/mrm.70173
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00