On the effect of training database size for MR-based synthetic CT generation in the head

• Published in: Computerized medical imaging and graphics Vol. 107; p. 102227
• Main Authors: Estakhraji, Seyed Iman Zare, Pirasteh, Ali, Bradshaw, Tyler, McMillan, Alan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.07.2023
• Subjects: Fine-Tuning; Generative adversarial networks (GAN), Synthetic CT generation; Humans; Image Processing, Computer-Assisted - methods; Internal Medicine; Magnetic Resonance Imaging - methods; Magnetic Resonance Spectroscopy; MR-guided radiotherapy; Other; Radiotherapy, Image-Guided; Tomography, X-Ray Computed; MR-guided radiotherapy; Generative adversarial networks (GAN), Synthetic CT generation; Fine-Tuning
• ISSN: 0895-6111; 1879-0771; 1879-0771
• DOI: 10.1016/j.compmedimag.2023.102227

Generation of computed tomography (CT) images from magnetic resonance (MR) images using deep learning methods has recently demonstrated promise in improving MR-guided radiotherapy and PET/MR imaging. To investigate the performance of unsupervised training using a large number of unpaired data sets as well as the potential gain in performance after fine-tuning with supervised training using spatially registered data sets in generation of synthetic computed tomography (sCT) from magnetic resonance (MR) images. A cycleGAN method consisting of two generators (residual U-Net) and two discriminators (patchGAN) was used for unsupervised training. Unsupervised training utilized unpaired T1-weighted MR and CT images (2061 sets for each modality). Five supervised models were then fine-tuned starting with the generator of the unsupervised model for 1, 10, 25, 50, and 100 pairs of spatially registered MR and CT images. Four supervised training models were also trained from scratch for 10, 25, 50, and 100 pairs of spatially registered MR and CT images using only the residual U-Net generator. All models were evaluated on a holdout test set of spatially registered images from 253 patients, including 30 with significant pathology. sCT images were compared against the acquired CT images using mean absolute error (MAE), Dice coefficient, and structural similarity index (SSIM). sCT images from 60 test subjects generated by the unsupervised, and most accurate of the fine-tuned and supervised models were qualitatively evaluated by a radiologist. While unsupervised training produced realistic-appearing sCT images, addition of even one set of registered images improved quantitative metrics. Addition of more paired data sets to the training further improved image quality, with the best results obtained using the highest number of paired data sets (n=100). Supervised training was found to be superior to unsupervised training, while fine-tuned training showed no clear benefit over supervised learning, regardless of the training sample size. Supervised learning (using either fine tuning or full supervision) leads to significantly higher quantitative accuracy in the generation of sCT from MR images. However, fine-tuned training using both a large number of unpaired image sets was generally no better than supervised learning using registered image sets alone, suggesting the importance of well registered paired data set for training compared to a large set of unpaired data. •The value and limit of unsupervised dual GANs in the head is investigated•Study on fine-tuning’s power to improve with limited paired and large unpaired data.•12 models trained for unsupervised, semi-supervised, and supervised training.•Supervised learning (fine tuning/full supervision) yields higher accuracy.•Fine-tuned training with unpaired data not better than registered data alone.