Meeting Banner
Abstract #4607

Deep learning for magnetic resonance vessel wall image: image reconstruction, stenosis diagnosis and plaque calculation

fan fu1, zengping lin2, xiong yang 2, and biao li1
1Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, 2Central Research Institute, United Imaging Healthcare Group Co., shanghai, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Vessel Wall, magnetic resonance vessel wall image, reconstruction, segmentation

Motivation: Studies have rarely investigated image reconstruction, stenosis detection and plaque calculation from magnetic resonance vessel wall images(MRVWI) because accurate interpretation is error prone and labor-intensive.

Goal(s): To develop an automated CNN-based method for accurate image reconstruction, stenosis detection and plaque calculation in MRVWI and compare its performance with radiologists.

Approach: A deep learning algorithm was constructed and trained using MRVWI were collected from four tertiary hospitals.

Results: The overall reconstruction achieves a qualification rate of 92.3%. This algorithm reduces the diagnosis time of radiologists from 22.08min to 12.79 min.There was high agreement between the algorithm and radiologists on plaque calculation (kappa=.8)

Impact: A deep learning algorithm for magnetic resonance vessel wall interpretation accurately determined image reconstruction, vessel stenosis and plaque calculation, which achieved automatic postprocessing and had equivalent diagnostic performance when compared with experienced radiologists.

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