Keywords: Machine Learning/Artificial Intelligence, Susceptibility, 3DEPI, SWI, QSM, Deep-Learning
Motivation: There is a strong clinical desire to accelerate the established SWI protocols. The clinical adoption of quantitative susceptibility maps (QSM) is hindered in part by long scan time and cumbersome offline QSM processing.
Goal(s): This study aims to substantially accelerate the SWI/QSM acquisitions while providing high-quality inline SWI/QSM images.
Approach: A flow-compensated, CAIPIRINHA-accelerated 3D echo-planar imaging (3DEPI) sequence was used to create 1-minute protocols with matching resolution to conventional sequences. Deep-learning reconstruction and super-resolution were used to enhance image quality.
Results: Compared with the established approach, the 1-minute 3D-EPI protocols provided 4× to 6× speed-up, while the DL reconstruction provided superior image quality.
Impact: Besides the general improvement in throughput, the 1-minute SWI protocols with improved image quality may enhance the role of MRI-SWI in acute care. The high-quality in-line QSM and susceptibility map weighted images (SWMI) will facilitate their clinical evaluation and adoption.
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