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Abstract #3793

Fast synthetic CT generation using a conditional Generative Adversarial Network and Dixon imaging for general pelvis MR-based Radiotherapy planning

Mark HF Savenije1,2, Matteo Maspero1,2,3, Anna M Dinkla1,2, Peter R Seevinck2,3, and Cornelis AT van den Berg1,2

1Radiotherapy Department, UMC Utrecht, Utrecht, Netherlands, 2Center for Image Sciences, UMC Utrecht, Utrecht, Netherlands, 3Image Science Institute, UMC Utrecht, Utrecht, Netherlands

To enable MR-only radiotherapy planning and accurate MR-based dose calculations, substitutes of CT images, so-called synthetic CT (sCT) images, need to be generated. In this work, we assessed whether sCT images generated by a 2D conditional Generative Adversarial Network (cGAN) using a 3D dual echo SPGR MR sequence were suited for radiation treatment planning for general pelvis cancer patients. Image evaluation showed comparable performance among prostate, rectum and cervix patients. Dose planning calculations demonstrated that accurate MR-based dose calculation on sCT images generated by the cGAN after training is feasible for treatment planning in prostate cancer patients. In addition, the generation of the sCT is fast (< 6 s) and ideally suited for applications where time duration is essential, e.g. MR-guided radiotherapy planning.

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