RDB CycleGAN-CBAM: an integrated deep learning model for low-dose ct image denoising

Computed Tomography (CT) is one of the largest contributors to radiation exposure from medical imaging, which can induce DNA damage and increase cancer risk. Reducing CT radiation dose to improve patient safety inherently increases image noise and artifacts. Generative adversarial networks (GANs) have shown promise for unsupervised low-dose CT (LDCT) denoising. Building on this, RDBCycleGAN-CBAM, a CycleGAN-based model that integrates residual dense blocks (RDBs) and convolutional block attention modules (CBAM), was developed to effectively denoise quarter-dose CT images while preserving structural detail. The model was trained on unpaired quarter-dose and full-dose CT scans from the NIH-AAPM-Mayo dataset using adversarial (LSGAN), cycle-consistency, and identity losses. Evaluation on held-out test slices was performed using PSNR and SSIM as the primary image-quality metrics. The results demonstrate that the proposed RDBCycleGAN-CBAM method demonstrated improvements that were competitive with values reported in previous studies, achieving mean improvements of + 3.973 dB in PSNR and + 0.053 in SSIM relative to quarter-dose inputs. Shapiro-Wilk tests for PSNR and SSIM motivated the use of the nonparametric Wilcoxon signed-rank test, by which highly significant improvements across both metrics (PSNR and SSIM) were demonstrated. The very large rank-biserial correlation values (1.0) indicate that nearly all test images experienced substantial quality improvement. Furthermore, the narrow bootstrap confidence intervals for the mean differences suggest that these improvements are consistent across the dataset. These findings support further investigation of RDBCycleGAN-CBAM as a vendor-neutral image-domain denoising approach, while external validation and reader studies are required to establish its clinical utility and potential for dose reduction.