Deep plug-and-play denoising prior with total variation regularization for low-dose CT - Ma, Yinjin; Zhang, Yajuan; Chen, Lin; Jiang, Qiang; Shi, Fengjuan; Wei, Biao (Frontiers in Physics, 2025)
X-ray computed tomography (CT) is widely used in clinical practice for screening and diagnosing patients, as it enables the acquisition of high-resolution images of internal tissues and organs in a non-invasive manner. However, this has led to growing concerns about the cumulative radiation risks associated with X-ray exposure. Low-dose CT (LDCT) reduces radiation doses but results in increased noise and artifacts, significantly affecting diagnostic accuracy. LDCT image denoising remains a challenging task in medical image processing. To enhance LDCT image quality and leverage the flexibility and effectiveness of plug-and-play denoising methods, this paper proposes a novel deep plug-and-play denoising method. Specifically, we first introduce a deep residual block convolutional neural network (DRBNet) with residual noise learning. We then train the DRBNet using a hybrid loss function combining L1 and multi-scale structural similarity (M-SSIM) losses, while regularizing the training with total variation (TV). After training, the DRBNet is integrated as a deep denoiser prior into a half-quadratic splitting-based method to solve the LDCT image denoising problem. Experimental results demonstrate that the proposed plug-and-play method, using the DRBNet prior, outperforms state-of-the-art methods in terms of noise reduction, artifact suppression, and preservation of textural and edge information when compared to standard normal-dose CT (NDCT) scans. A blind reader study with two experienced radiologists further confirms that our method surpasses other denoising approaches in terms of clinical image quality.