Keywords: Flow, Segmentation, Analysis/Processing, Aortic Stenosis, Blood Vessels, Cardiovascular, Data Analysis, Data Processing, Flow, Machine Learning/Artificial Intelligence, Velocity & Flow, cine 2D Flow MRI
Motivation: Neural network-based segmentations of cine 2D Flow MRI can be compromised by cardiac-phase dependent inflow contrast variations and artifacts in magnitude data.
Goal(s): To evaluate if neural network-based segmentation is improved by incorporating both magnitude and phase data.
Approach: Masks of the ascending and descending aorta were manually generated from 213 cine 2D Flow MRI datasets. Separate convolutional neural networks (nnUNet) were trained using either magnitude data only or combined magnitude and phase data. Segmentation performance was assessed relative to manual segmentation masks.
Results: State-of-the-art cine 2D Flow MRI segmentation was achieved. The addition of phase data did not significantly benefit segmentation performance.
Impact: This work demonstrates state-of-the-art neural network-based segmentation of aortic vessels across the cardiac cycle using the nnUNet. The addition of phase data appears non-essential. The trained models will be made available, to promote reproducibility and accessibility in the field.
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.
Keywords