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Abstract #2819

Deep learning-based acceleration of compressed SENSE brain ASL MRI using 3D Cartesian TSE with improved spatial resolution

Yiming Wang1, Yajing Zhang2, Zhongping Zhang1, Wengu Su3, Zhongchang Ren2, and Yan Zhao2
1Philips Healthcare, Shanghai, China, 2MR R&D, Philips Healthcare, Suzhou, China, 3MR Application, Philips Healthcare, Suzhou, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Brain, ASL, 3D Cartesian TSE, CS-AI, Deep Learning

Motivation: Brain ASL images are frequently obtained at relatively low spatial resolutions, necessitating a desire for higher-resolution ASL MRI without extended scan times.

Goal(s): To evaluate the potential of employing CS-AI for accelerating higher-resolution brain ASL MRI

Approach: Acceleration of higher-resolution 3D Cartesian TSE ASL MRI was achieved using CS-AI at 2, 3, and 4-fold rates, and its performance was compared with SENSE.

Results: CS-AI-accelerated 3D brain ASL images exhibited good SNR and quality, surpassing those acquired with SENSE, without affecting CBF quantification.

Impact: This investigation may improve the clinical utility of brain ASL, particularly in quantifying perfusion alterations in small-sized lesions.

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