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Abstract #4770

Development and Evaluation of an Automated Pipeline for ASL Scan Planning and Labeling Plane Positioning

Zhe Zhang1, Hualu Han2, Huayin Hao2, Qing Wei2, Xu Wang2, Xiaolin Meng2, Ning Wei1, Wanlin Zhu1, Yue Suo1, Shuo Chen2, and Jing Jing1,3
1Tiantan Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 2United Imaging Healthcare, Shanghai, China, 3Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

Synopsis

Keywords: Perfusion, Arterial spin labelling

Motivation: Accurate labeling plane positioning is crucial for reliable ASL perfusion measurements. Manual positioning using angiography are complex and inconsistent. Traditional methods relying solely on structural distance often fail to achieve symmetric labeling efficiency.

Goal(s): Develop an automated ASL planning pipeline incorporating angiography for reliable and reproducible perfusion measurements.

Approach: The pipeline integrates deep learning for FOV planning, artery segmentation via VB-net, and gradient descent for plane positioning optimization. It was evaluated against traditional methods by experienced physicians on 217 subjects.

Results: The pipeline successfully positioned labeling planes in 97% of cases, versus 47% for traditional methods, and demonstrated better performance on tortuous arteries.

Impact: The proposed automated ASL planning pipeline achieves both standardized brain orientation and optimized labeling plane positioning, potentially reducing labeling inefficiencies on tortuous arteries and minimizing false-positive hypoperfusion cases.

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