Keywords: Machine Learning/Artificial Intelligence, Cancer, Denoising, Self-SupervisedIn MR guided radiation therapy, images of patients are acquired daily. However, scan times are long to acquire images with an acceptable SNR for treatment planning and adaptation. In this study we test a self-supervised machine learning based approach for denoising data that can utilize previous scans of the same patient to improve quality of the denoised image. Results on a numerical phantom and clinical images are presented and compared to a popular non-machine learning denoising algorithm.
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