Room: Stars at Night Ballroom 2-3
Purpose: Deep learning methods have been used to achieve excellent accuracies in generating brain and pelvic synthetic CTs (sCTs) for MR-only radiotherapy. However, few such methods have been investigated for abdominal sCT generation. In this work, we compared two deep learning approaches, conditional generative adversarial network (cGAN) and cycle-consistent GAN (cycle-GAN), for abdominal sCT generation based on a single 0.35T MR scan.
Methods: CT and MR images of 12 abdominal cancer patients were studied retrospectively. N4 bias field correction and histogram-based normalization were performed on the MR images to minimize inter-patient intensity variations. All CTs were deformably registered to MR images to generate paired MR/deformed CT (dCT) for cGAN training. Since cycle-GAN does not require paired images for training, we investigated two training schemes: one using unpaired MR and dCT images, and the other using unpaired MR and non-deformed CT images. Model performance was evaluated by computing the mean absolute error (MAE) and peak signal to noise ratio (PSNR) between each sCT and dCT under a four-fold cross-validation protocol. Statistical tests were conducted to compare these metrics.
Results: The average MAEs were 88.0Â±15.7 HU, 99.1Â±17.8 HU, and 113.9Â±20.4 HU for cGAN, cycle-GAN trained with dCTs, and cycle-GAN trained with non-deformed CTs, respectively. The corresponding average PSNRs were 27.5Â±1.5, 26.8Â±1.3, and 25.7Â±1.4, respectively. Statistically significant differences were observed for all pairwise metric comparisons except for the PSNRs between cGAN and cycle-GAN trained with dCTs.
Conclusion: cGANs generated the most accurate abdominal sCTs. Cycle-GANs trained with dCTs performed better than cycle-GANs trained with non-deformed CTs. Since Cycle-GANs can be trained unsupervised, they may have access to training datasets larger than those available for cGANs, which may counterbalance the accuracy advantage of cGANs demonstrated in this 12-patient study. Further investigation is required to assess performance differences with larger training sets.
Funding Support, Disclosures, and Conflict of Interest: Varian master research agreement
Not Applicable / None Entered.