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Abstract #0798

Deep learning-based 4D synthetic CT for lung radiotherapy

Matteo Maspero1,2, Kirsten M. Kerkering2,3, Tom Bruijnen1,2, Mark H. F. Savenije1,2, Joost J. C. Verhoeff1, Christoph Kolbitsch3, and Cornelis A. T. van den Berg1,2
1Radiotherapy, Division of Imaging & Oncology, UMC Utrecht, Utrecht, Netherlands, 2Center for Computational imaging group for MR diagnostic & therapy, Center for Image Sciences, UMC Utrecht, Utrecht, Netherlands, 3Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany

The feasibility of generating synthetic CT for lung tumours from 4D MRI was investigated. A combination of multi-view 2D networks proved to be robust against image artefact and generated sCTs that enabled dose calculation on midposition sCTs. The proposed approach facilitates adaptive MR-guided radiotherapy reducing the time from patient positioning to irradiation and enables quality assurance with dose accumulation based on 4D MRI.

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