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Respiratory motion during radiotherapy causes uncertainty in the tumor'slocation, which is typically addressed by an increased radiation area and adecreased dose. As a result, the treatments' efficacy is reduced. The recentlyproposed hybrid MR-linac scanner holds the promise to efficiently deal withsuch respiratory motion through real-time adaptive MR-guided radiotherapy(MRgRT). For MRgRT, motion-fields should be estimated from MR-data and theradiotherapy plan should be adapted in real-time according to the estimatedmotion-fields. All of this should be performed with a total latency ofmaximally 200 ms, including data acquisition and reconstruction. A measure ofconfidence in such estimated motion-fields is highly desirable, for instance toensure the patient's safety in case of unexpected and undesirable motion. Inthis work, we propose a framework based on Gaussian Processes to infer 3Dmotion-fields and uncertainty maps in real-time from only three readouts ofMR-data. We demonstrated an inference frame rate up to 69 Hz including dataacquisition and reconstruction, thereby exploiting the limited amount ofrequired MR-data. Additionally, we designed a rejection criterion based on themotion-field uncertainty maps to demonstrate the framework's potential forquality assurance. The framework was validated in silico and in vivo on healthyvolunteer data (n=5) acquired using an MR-linac, thereby taking into accountdifferent breathing patterns and controlled bulk motion. Results indicateend-point-errors with a 75th percentile below 1mm in silico, and a correctdetection of erroneous motion estimates with the rejection criterion.Altogether, the results show the potential of the framework for application inreal-time MR-guided radiotherapy with an MR-linac.

Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy