Enhancing low-dose CT liver imaging with self-attention RRDB-ESRGAN: A SR approach for improved diagnostic accuracy

Computed tomography (CT) plays a critical role in diagnosing liver diseases by enabling detailed visualization of internal anatomical structures. However, to minimize radiation exposure, low-dose CT (LDCT) protocols are often adopted; these inevitably lead to increased image noise and reduced spatial resolution, which adversely affect diagnostic quality. To address these limitations, we propose a novel super-resolution (SR) framework based on an enhanced version of the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN), specifically designed for LDCT liver imaging. Our method integrates Residual-in-Residual Dense Blocks (RRDBs) within the generator to effectively capture complex anatomical features and hierarchical textures. A self-attention module is incorporated to selectively emphasize diagnostically salient regions, facilitating more accurate restoration of critical structures. The discriminator network, trained using an adversarial learning strategy, guides the generator toward producing images with enhanced perceptual realism. We evaluate our Self-Attention Residual-in-Residual Dense Block ESRGAN (Self-Attention RRDB-ESRGAN) model using three publicly available datasets: the Liver Tumor Segmentation Challenge (LiTS17), 3DIRCADb, and the Medical Segmentation Decathlon (Task 03_Liver). Quantitative metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean opinion score (MOS), demonstrate the superior performance of our model in restoring high-fidelity images from low-dose inputs. The Self-Attention RRDB-ESRGAN achieved an average PSNR of 41.579 dB and an SSIM of 0.9472, significantly outperforming existing methods, while experienced radiologists rated the perceptual quality with an average MOS of 4.05 out of 5. These findings highlight the effectiveness and clinical applicability of the proposed framework for enhancing LDCT liver images and improving diagnostic accuracy.