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Abstract #3848

3D CNN-based synthetic CT generation for head and neck radiotherapy using a clinically used turbo spin-echo T2-weighted sequence

Mateusz C. Florkow1, Anna M. Dinkla2, Frank Zijlstra1, Matteo Maspero2, Mark H.F. Savenije2, Patricia A.H. Doornaert2, Marijn van Stralen1,3, Marielle E.P. Philippens2, Cornelis A.T. van den Berg2, and Peter R. Seevinck1,3

1Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, 2Radiotherapy Department, University Medical Center Utrecht, Utrecht, Netherlands, 3MRIguidance, Utrecht, Netherlands

Deep learning-based synthetic CT (sCT) generation models are often based on T1-weighted gradient echo sequences. However, these sequences are generally not used for tumor/organs-at-risk delineation. In this study, we trained a U-Net type neural network using T2-weighted turbo spin-echo images from the clinical radiotherapy treatment planning protocol originally used for tumor/organs-at-risk contouring. The use of clinical images preserves scan times and facilitates soft tissue delineation on the source images of the sCTs, avoiding registrations. We showed that sCTs generated by the trained model provide accurate dosimetric results while limiting CT-induced streaking dental artefacts.

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