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Abstract #4131

High Precision Deep learning 4D MRA Vessel Segmentation: Technical Development and Initial Clinical Evaluation on Arteriovenous Malformation

Sang Hun Chung1, Zihan Wang1, Tianrui Zhao1, Jianing Tang1, Yining He1, Sameer A Ansari2, Chase Krumpelman2, and Lirong Yan2
1Northwestern University, Chicago, IL, United States, 2Radiology, Northwestern University, Chicago, IL, United States

Synopsis

Keywords: Analysis/Processing, Segmentation, Cerebral arteriovenous malformation, Cerebral blood flow

Motivation: Vessel segmentation from 4DMRA may provide further information to aid in clinical diagnosis. However, currently most of the neural networks for MRA segmentation target static angiography.

Goal(s): To design a generalized neural network for 4DMRA intracranial vessel segmentation with minimal preprocessing.

Approach: A modified U-net architecture (4DST U-Net) was designed by leveraging both spatial [x,y,z] and temporal (t) dimensions for 4D MRA vessel segmentation. External validation on two AVMs and a healthy volunteer was tested for model generalizability.

Results: The proposed deep learning vessel segmentation method outperformed the other three models. External validation with AVM data correctly detected the AVM lesions.

Impact: This work developed a 4DST U-Net for 4D MRA vessel segmentation with minimal preprocessing. The generalizability of this neural network was demonstrated by the external validation on patients. Both features may facilitate a wider application of this technique across multi-sites.

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Keywords