(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose: Development, optimization, validation and practical implementation of deep learning models for synthetic CT (SynCT) applied to radiotherapy.
Methods: T1-T2-weighted MR sets were used to investigate two Generative Adversarial Networks (GAN) - cGAN and CycleGAN. 50/50 training and 10/10 validation sets from CNS/H&N cases were processed. CT and MR voxel resolution was 1 mm³. CNS data was acquired using mask on MR and CT. H&N data had 3/5-point mask on for CT and no mask for MR (no RF-coil clearance), resulting in a significant inter-procedure neck displacement. Multiple parameters were tested to optimize the networks/models: a) resolution (128-256-512), b) data formatting (transverse-sagittal-coronal), c) sample size (25-50 sets), d) contrast (T1/T2/mixed). For CycleGAN, training was done with paired/unpaired sets. CT-to-SynCT sets were compared by a) similarity metrics MAE/PSNR, b) RT plan comparison and c) 3D-boney surface rendering (registration/conformality). For clinical deployment, GAN models were implemented on an automated imaging pipeline (QUANTMR) for live SynCT generation. QUANTMR also enforced image quality validation of MR data using a predictive model.
Results: Our results showed that a good SynCT model can be achieved after training on only 25 cases. Training up to 400 epochs showed that approximately 200 epochs were sufficient to achieve a robust solution and avoid overfitting. PSNR/MAE values were 34/0.004 and 31/0.006 for cGAN and CycleGAN, respectively, when paired sets were used for training. Unpaired training did not significantly improved the model performance for CNS, but showed a superior performance in H&N. Metrics showed a modest improvement when training was increased to include 50 cases.
Conclusion: GANs for SynCT need to be carefully chosen to match the data available for training. Reliable models can be built with tens of CT-MR sets given adequate data quality and preparation. The study successfully developed and implemented a GAN-based infrastructure for SynCT in CNS-H&N RT.
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