The MR-Linac exploits excellent soft-tissue contrast of MRI images with the option of daily re-planning for irradiation of pelvic tumours. However, this requires significant clinician interaction as contours need manual redefining for each radiotherapy fraction. Recently, machine learning-based segmentation approaches are being developed to automate this process. One major limitation of this approach is the lack of available fully-annotated MRI data for training. In this study, a deep learning model was developed and trained on CT/MR registered images of 21 patient datasets, generating synthetic T1-weighted MR images from input CT data, which could be useful towards disease segmentation on MRI.
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