Keywords: Arterial Spin Labelling, Machine Learning/Artificial Intelligence
Motivation: Hemodynamic disturbance is one of the neuropathological characteristics of Parkinson's disease (PD). Multi-delay arterial spin labeling (m-ASL) MRI can optimize the accuracy of cerebral blood flow (CBF) quantification by taking into account arterial transit time (ATT).
Goal(s): We aimed to comprehensively explore the detailed abnormalities of hemodynamics in PD and verify the application of m-ASL in PD diagnosis.
Approach: Voxel-based analysis and machine learning approach were applied to this study.
Results: Our findings identified impaired hemodynamics in PD with regional abnormalities of CBF, ATT and cerebral blood volume, providing complementary depictions of perfusion disruption in PD and highlighting the clinical feasibility of m-ASL.
Impact: Our results provided complementary depictions of perfusion disruption in PD, and validated the promise of m-ASL in the investigation of underlying neurodegeneration and the clinical diagnosis of PD, providing an effective neuroimaging biomarkers for the diagnosis of neurodegenerative diseases.
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