Keywords: Flow, Flow, AI/Machine Learning; 4DflowMRI; phase offset; background offset
Motivation: Several approaches for background offset correction in 4D flow MRI exist, but none has proven fully effective. Best results are obtained with a post in-vivo phantom measurement as correction, but this, however, doubles the scan time.
Goal(s): To develop an automatic, deep learning-based background offset correction method for cardiovascular 4D flow MRI data, that uses static phantom measurements as ground truth.
Approach: The method consists in training a convolutional neural network with static-phantom measurement as ground truth. Results were compared to polynomial fit correction methods.
Results: The proposed method outperformed the conventional polynomial fit methods, and was comparable to the ground-truth phantom-based correction.
Impact: The proposed fully automated CNN-based background offset correction method outperformed the conventional background offset correction methods that use a polynomial fit to static tissue. This method has potential for significantly improving the data quality of cardiovascular 4D flow MRI.
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