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Abstract #3706

Deep Learning Augmented PROPELLER Reconstruction for Improved MRI Motion Correction

Sixing Liu1, Lifeng Mei1, Shoujin Huang1, and Mengye Lyu1
1College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China

Synopsis

Keywords: PET/MR, Machine Learning/Artificial Intelligence, deep learningApplying the Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) technique is one of the strategies to mitigate motion artifacts in MR images. However, due to technical limitations, existing method estimates motion parameters with unsatisfactory results, motion artifacts will still be presented in the final image. Deep learning algorithms are expected to optimize the motion parameter estimation part of PROPELLER technique. We develop a PROPELLER imaging technique incorporating a deep learning model that can provide accurate results and greatly shorten the elapsed time.

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