A Multi-Parametric MRI Deep Learning Fusion Model for Grading Arterial Transit Artifacts

Motivation: ATAs are essential indicators of collateral pathways in cerebral perfusion anomalies. However, the conventional grading systems for ATA suffer from subjectivity, which may subjectively leads to variabilityGoal(s): We aim to standardize ATA grading by a deep learning fusion model that combines information from ASL and DWIApproach: A deep learning fusion model was developed, which applies two 3D CNNs to extract respective feature map of each modality; this model combines the high-level feature maps to fuse the multi-sequence MRI informationResults: The fusion model shows significant improvements over a single modality model, achieving an AUC value of 0.895Impact: The good ATA evaluation performance of the deep learning fusion model shows its clinical potential in assisting neuroradiologists in conducting the treatment and prognosis analysis for patients with ischemic stroke