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Abstract #2719

Evaluation of 2D and 3D convolutional neural network methods for generating pelvic synthetic CT from T1-weighted MRI

Jie Fu1, Yingli Yang2, Kamal Singhrao1, Dan Ruan2, Daniel A. Low2, Anand P. Santhanam2, and John H. Lewis2

1David Geffen School of Medicine, UCLA, Los Angeles, CA, United States, 2Department of Radiation Oncology, UCLA, Los Angeles, CA, United States

Synthetic CT (sCT) must be generated directly from MRI scans to achieve MRI-only radiotherapy. We propose 2D and 3D convolution neural network models for generating pelvic sCT and evaluate their performance. Five-fold cross-validation is performed using paired T1-weighted MRI and CT scans from 20 patients. Our results show the 2D model generates accurate sCT for all patients in this study. The average mean absolute error (MAE) between CT and sCT across all patients is 38.0±3.9 HU in the 2D model. The average MAE is 55.9±28.4 HU in the 3D model. This large variation is possibly due to the limited number of 3D training volumes.

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