Objective. This work aimed to study the application of iterative reconstruction algorithm-based computed tomography (CT) imaging in the diagnosis of gastric cancer (GC). Methods. 40 cases of GC patients diagnosed by gastroscopy biopsy and pathology in hospital were retrospectively analyzed. Scanning images of the upper abdomen were obtained after plain scanning and double-phase enhanced scanning. Then, the image was reconstructed by the iterative reconstruction algorithm, and the CT value under the algorithm was analyzed statistically. Results. It was revealed that the detection rate of both spiral CT and iterative reconstruction algorithm-based CT was 100%. After the iterative reconstruction algorithm, the image quality, image information, and image mean square error (MSE) were notably improved. The degree of tumor invasion (T) staging accuracy was 82.6%, lymph node metastasis (N) staging accuracy was 73.2%, and tumor node metastasis (TNM) staging accuracy was 79.1%. The accuracy of the iterative reconstruction algorithm-based CT was 90% for T staging, 83% for N staging, and 85.5% for TNM staging. Conclusion. Iterative reconstruction algorithm can effectively improve the spatial resolution of CT images in GC diagnosis, with high accuracy. It can provide reliable and objective imaging data for the diagnosis of GC clinically, which was worthy of further application in clinical practice.

In order to study the analysis of blood flow field characteristics of cerebral aneurysm patients before and after stent implantation based on CT images of ART-TV-PI, this paper firstly improved the ART-TV algorithm of algebraic reconstruction technology and obtained the ART-TV-PI algorithm, which was compared with the ART algorithm and ART-TV algorithm. Afterwards, the CT images based on the above three algorithms were used to analyze the changes of average blood flow velocity, average wall pressure, average wall deformation, and average shear force of 48 cases of cerebral aneurysm patients before and after stent implantation. The results showed that the mean square error and radiation dose of the ART-TV-PI algorithm (0.00012 and 1.65 mSv) were significantly lower than those of the ART algorithm (0.0031 and 3.09 mSv) and ART-TV algorithm (0.00082 and 2.52 mSv), and the signal-to-noise ratio (23.94) was significantly higher than those of the ART algorithm (11.32) and ART-TV algorithm (16.89), with statistically significant differences ( P < 0.05 ). The differences of mean blood flow velocity, mean wall pressure, mean wall deformation, and mean shear force before and after stent implantation among the three algorithms were not statistically significant ( P > 0.05 ), and the average index of the ART-TV-PI algorithm was the highest. Under the ART-TV-PI algorithm, the mean blood flow velocity (0.044 m/s), the mean wall pressure (71.7 Pa), the mean wall deformation (0.057 mm), and the mean shear force (889 Pa) after stent implantation were significantly lower than those before stent implantation (0.165 m/s, 160.8 Pa, 0.721 mm, and 2690 Pa), with average decreases of 73.3%, 55.4%, 92.1%, and 64.3%, respectively, and the differences were statistically significant ( P < 0.05 ). In conclusion, the images reconstructed by the ART-TV-PI algorithm have good image quality, which provides great convenience for surgical examination of cerebral aneurysm stent implantation and is worthy of clinical application.

This study was carried out to explore the diagnostic effect of magnetic resonance imaging (MRI) based on the low-rank matrix (LRM) denoising algorithm under gradient sparse prior for the tibial plateau fracture (TPF) combined with meniscus injury (TPF + MI). In this study, the prior information of the noise-free MRI image block was combined with the self-phase prior, the gradient prior of MRI was introduced to eliminate the ringing effect, and a new MRI image denoising algorithm was constructed, which was compared with the anisotropic diffusion fusion (ADF) algorithm. After that, the LRM denoising algorithm based on gradient sparse prior was applied to the diagnosis of 112 patients with TPF + MI admitted to hospital, and the results were compared with those of the undenoised MRI image. Then, the incidence of patients with all kinds of different meniscus injury parting was observed. A total of 66 cases (58.93%) of meniscus tears (MT) were found, including 56 cases (50.00%) of lateral meniscus (LM), 10 cases (8.93%) of medial meniscus (MM), 16 cases (14.29%) of meniscus contusion (MC), and 18 cases (16.07%) of meniscus degenerative injury (MDI). The incidences of MI in Schatzker subtypes were 0%, 53.33% (24/45), 41.67% (5/12), 76.47% (13/17), 78.94% (15/19), and 23.53% (4/17), showing no statistically significant difference ( P > 0.05 ), but the incidence of MT was 54.46% (61/112), which was greatly higher than that of MC (15.18% (17/112)), and the difference was statistically obvious ( P < 0.05 ). The diagnostic specificity (93.83%) and accuracy (95.33%) of denoised MRI images were dramatically higher than those of undenoised MRI images, which were 78.34% and 71.23%, respectively, showing statistically observable differences ( P < 0.05 ). In short, the algorithm in this study showed better denoising performance with the most retained image information. In addition, denoising MRI images based on the algorithm constructed in this study can improve the diagnostic accuracy of MI.