Keywords: Stroke, Stroke
Motivation: Stroke patients commonly face challenges during clinical magnetic resonance imaging (MRI) examinations due to loss of consciousness and involuntary movements. This study aims to address these challenges using a self-developed ultra-fast, multiple overlapping-echo detachment (MOLED) quantitative magnetic resonance technology.
Goal(s): Through this technology, we seek to quantitatively detect potential damage to the motor-related normal-appearing corticospinal tract (NA-CST) following stroke.
Approach: Total 79 patients underwent routine scan and MOLED. A deep learning network was utilized for quantitative image reconstruction.
Results: MOLED T2 imaging showed high accuracy and repeatability, was unaffected by head motion, correlated with motor dysfunction severity, and predicted motor impairment post-stroke.
Impact: The MOLED technique quickly and accurately quantifies imaging in stroke patients with involuntary movements and helps monitor post-stroke motor impairment progression.
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