Deep learning-based algorithms for low-dose CT imaging: A review.

The computed tomography (CT) technique is extensively employed as an imaging modality in clinical settings. The radiation dose of CT, however, is significantly high, thereby raising concerns regarding the potential radiation damage it may cause. The reduction of X-ray exposure dose in CT scanning may result in a significant decline in imaging quality, thereby elevating the risk of missed diagnosis and misdiagnosis. The reduction of CT radiation dose and acquisition of high-quality images to meet clinical diagnostic requirements have always been a critical research focus and challenge in the field of CT. Over the years, scholars have conducted extensive research on enhancing low-dose CT (LDCT) imaging algorithms, among which deep learning-based algorithms have demonstrated superior performance. In this review, we initially introduced the conventional algorithms for CT image reconstruction along with their respective advantages and disadvantages. Subsequently, we provided a detailed description of four aspects concerning the application of deep neural networks in LDCT imaging process: preprocessing in the projection domain, post-processing in the image domain, dual-domain processing imaging, and direct deep learning-based reconstruction (DLR). Furthermore, an analysis was conducted to evaluate the merits and demerits of each method. The commercial and clinical applications of the LDCT-DLR algorithm were also presented in an overview. Finally, we summarized the existing issues pertaining to LDCT-DLR and concluded the paper while outlining prospective trends for algorithmic advancement.

Determination of Hepatic Iron Deposition in Drug-Induced Liver Fibrosis in Rats by Confocal Micro-XRF Spectrometry.

Liver fibrosis is the intermediate process and inevitable stage of the development of chronic liver disease into cirrhosis. Reducing the degree of liver fibrosis plays an extremely important role in treating chronic liver disease and preventing liver cirrhosis and liver cancer. The formation of liver fibrosis is affected by iron deposition to a certain extent, and excessive iron deposition further induces liver cirrhosis and liver cancer. Herein, confocal microbeam X-ray fluorescence (µ-XRF) was used to determine the intensity and biodistribution of iron deposition at different time points in the process of liver fibrosis induced by thioacetamide (TAA) in rats. To our best knowledge, this is the first study using confocal µ-XRF to analyze hepatic iron deposition in hepatic fibrosis. The results showed that there are minor and trace elements such as iron, potassium, and zinc in the liver of rats. Continuous injection of TAA solution resulted in increasing liver iron deposition over time. The intensity of iron deposition in liver tissue was also significantly reduced after bone mesenchymal stem cells (BMSCs) were injected. These findings indicated that confocal µ-XRF can be used as a nondestructive and quantitative method of evaluating hepatic iron deposition in hepatic fibrosis, and iron deposition may play an important role in the progression of hepatic fibrosis induced by TAA.