CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer

Abstract

Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient’s anatomy as in CBCT, while having the image quality of CT. As CBCT and CT are acquired at different time points, it is challenging to obtain paired images with aligned anatomy for supervised training. To address this limitation, the study incorporated a registration network (RegNet) into GAN during training. RegNet can dynamically estimate the correct labels, allowing supervised learning with noisy labels. The study developed and evaluated the approach using imaging data from 146 patients with head and neck cancer. The results showed that GAN trained with RegNet performed better than those trained without RegNet. Specifically, in the UNIT model trained with RegNet, the mean absolute error (MAE) was reduced from 40.46 to 37.21, the root mean-square error (RMSE) was reduced from 119.45 to 108.86, the peak signal-to-noise ratio (PSNR) was increased from 28.67 to 29.55, and the structural similarity index (SSIM) was increased from 0.8630 to 0.8791. The sCT generated from the model had fewer artifacts and retained the anatomical information as in CBCT.

Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer

“…These anatomical variations could potentially impact the training of the U-Net algorithm, which relies on paired image data. Therefore, exploring unsupervised models such as GANs could enhance the model's performance, particularly when dealing with unpaired image data (intra-individual co-registration) [18,[24][25][26][27][28][29][30]. Furthermore, the process of rigidly registering the CT and CBCT images might not have been adequate in establishing the required image similarity for network training.…”

Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications, and Challenges

“…The suggested approach utilizes image-to-image translation to facilitate registration and incorporates a geometry-consistent training scheme and a multi-scale registration network with partial sharing. Suwanraksa et al [61] Proposed a GAN with a registration network (RegNet) to improve the quality of synthetic CT (sCT) generated from Cone-Beam Computed Tomography (CBCT). The incorporation of RegNet led to reduced errors, improved image metrics, and sCT images maintaining anatomical accuracy compared to GANs without RegNet .…”

CBCT-to-CT Synthesis for Cervical Cancer Adaptive Radiotherapy via U-Net-Based Model Hierarchically Trained with Hybrid Dataset

“…Hardware-based methods attempt to decrease the influence caused by scattering by utilizing an anti-scatter grid when acquiring CBCT images [15,16]. Image post-processing methods mainly consist of deformation of pCT [17][18][19][20][21], an estimation of scatter kernels [22], Monte Carlo simulations of scatter distribution [23,24], histogram matching [25], and deep learning-based methods [26][27][28][29][30][31]. Deformation of pCT is one of the commonly used methods, which is based on deformable registration between pCT and CBCT.…”