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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