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dc.contributor.authorNicholas, Hindley
dc.date.accessioned2024-02-28T21:53:58Z
dc.date.available2024-02-28T21:53:58Z
dc.date.issued2024-02-29
dc.identifier.urihttps://hdl.handle.net/2123/32282
dc.description.abstractThe XCAT-CoMBAT data can be used to train deep neural networks in an open-source framework for AI-guided adaptive radiotherapy. This framework, called Voxelmap, seeks to learn patient-specific geometric correspondences between 3D internal organ motion and 2D images acquired during radiotherapy. By learning these correspondences, Voxelmap aids in delivering a therapeutic dose to targets while minimising exposure to the surrounding organs-at-risk; thereby reducing the toxic side-effects of radiotherapy. The XCAT data are used to train 3 separate networks designed to assist in x-ray guided treatments, while the CoMBAT data are used to train 2 networks designed to assist in MRI guided treatments. We hope that by making our code and data available to the broader academic community, this work will help catalyse advancements in next generation radiotherapy. (https://github.com/Image-X-Institute/Voxelmap)en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0en
dc.subjectImage-guided radiotherapyen
dc.subjectmotion managementen
dc.subjectvolumetric imagingen
dc.subjectdeep learningen
dc.titleXCAT-CoMBAT training dataen
dc.typeDataseten
dc.identifier.doi10.25910/jn20-kv68
dc.description.methodThe 4D-XCAT digital phantom was programmed with cardiac and respiratory traces for 8 unique anatomies. The CoMBAT phantom was then used to convert this x-ray-based data, into MR images.en
usyd.facultySeS faculties schools::Faculty of Medicine and Healthen
workflow.metadata.onlyNoen


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