Meeting Banner
Abstract #1007

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

Yuchi Tian1, Yi Li1, and Xiaoyun Liang1
1Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Shanghai, China

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence

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 variability

Goal(s): We aim to standardize ATA grading by a deep learning fusion model that combines information from ASL and DWI

Approach: 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 information

Results: The fusion model shows significant improvements over a single modality model, achieving an AUC value of 0.895

Impact: 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

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords

model