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Abstract #5218

Whole-Brain T2 Mapping with Accelerated Stack-of-Stars Acquisition Using Unsupervised Model-Based Implicit Neural Representation Networks

Tianyi Xiao1, Bei Liu1, Huajun She1, and Yiping P. Du1
1National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging

Motivation: Reducing the scan time for T2 mapping is of clinical significance.

Goal(s): To develop a fast sampling sequence and an undersampling reconstruction algorithm to significantly reduce acquisition time.

Approach: A rapid sampling sequence based on a golden-angle stack-of-stars trajectory is proposed, and a novel unsupervised deep learning algorithm specifically designed for this sequence is introduced. This algorithm utilizes multi-resolution hash encoding with implicit neural representation based on a physical model to significantly shorten scan time.

Results: Whole-brain T2 mapping is achieved within a scan time of less than 1.4 minutes.

Impact: A T2 quantitative sequence that accelerates scanning using radial trajectory is presented. An unsupervised deep-learning algorithm employing multi-resolution hash-encoding implicit neural representation is introduced to reconstruct T2 maps from undersampled data. This approach has potential to significantly shorten acquisition time.

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