Abstract #0798

Deep learning-based 4D synthetic CT for lung radiotherapy

Matteo Maspero^1,2, Kirsten M. Kerkering^2,3, Tom Bruijnen^1,2, Mark H. F. Savenije^1,2, Joost J. C. Verhoeff¹, Christoph Kolbitsch³, and Cornelis A. T. van den Berg^1,2

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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