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Abstract #2597

CT-based Synthetic pelvic T1-weighted MR image generation using a deep convolutional neural network (CNN)

Reza Kalantar1, Jessica M Winfield1,2, Christina Messiou1,3, Dow-Mu Koh1,3, and Matthew D Blackledge1
1Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom, 2The Royal Marsden Hospital, London, United Kingdom, 3Department of Radiology, The Royal Marsden Hospital, London, United Kingdom

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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