MRI of prostate cancer (PCa) was performed using a projection onto convex sets (POCS) super-resolution reconstruction algorithm to evaluate and analyze the treatment of prostate-specific antigen (PSA) and provide a theoretical reference for clinical practice. A total of 110 patients with PCa were selected as the study subjects. First, the modified POCS algorithm was used to reconstruct the MRI images, and the gradient interpolation algorithm was used instead of the traditional bilinear algorithm to preserve the edge information. The diagnostic and therapeutic effects of MRI examination, PSA examination, and MRI combined with PSA based on a super-resolution reconstruction algorithm were then compared. The simulation results showed that the POCS algorithm was superior to the bilinear interpolation results and was superior to the common POCS algorithm. After adding noise artificially, the restoration algorithm was effective and could preserve the details in the image. The performance indexes of PSA in the diagnosis of PCa were 75.4%, 60.1%, 70.08%, 72.2%, and 60.3%, respectively; the performance indexes of MRI in the diagnosis of PCa were 84.6%, 61.4%, 71.11%, 73.08%, and 61.9%, respectively; and the performance indexes of MRI combined with PSA based on the super-resolution reconstruction algorithm in the diagnosis of PCa were 96.05%, 88.3%, 95.1%, 93.6%, and 92.7%, respectively. The indicators of MRI combined with PSA based on the super-resolution reconstruction algorithm were significantly higher than those of the other two methods ( P  < 0.05). The signal-to-noise ratio of MRI of PCa based on the super-resolution reconstruction algorithm has been greatly improved, with good clarity, which can improve the diagnostic accuracy of PCa patients and has certain advantages in the examination. MRI based on the super-resolution reconstruction algorithm has a high value in the diagnosis and treatment of PCa.

Objective. The study aimed to explore the application value of picture archiving and communication system (PCAS) of MRI images based on radial basis function (RBF) neural network algorithm combined with the radiology information system (RIS). Methods. 551 patients who required MRI examination in a hospital from May 2016 to May 2021 were selected as research subjects. Patients were divided into two groups according to their own wishes. Those who were willing to use the RBF neural network algorithm-based PCAS of MRI images combined with RIS were set as the combined group, involving a total of 278 cases; those who were unwilling were set as the regular group, involving a total of 273 cases. The RBF neural network algorithm-based PCAS of MRI images combined with RIS was trained and tested for classification performance and then used for comparison analysis. Result. The actual output (0.031259–0.038515) of all test samples was almost the same as the target output (0.000000) ( P  > 0.05). In the first 50,000 learnings, the iteration error of the RBF neural network dropped rapidly and finally stabilized at 0.038. The classification accuracy of the RBF neural network algorithm-based PCAS of MRI images combined with RIS for the head was 94.28%, that of abdomen was 97.22%, and it was 93.10% for knee joint, showing no statistically significant differences ( P  > 0.05), and the total classification accuracy was as high as 95%. The time spent in the examination in the combined group was about 2 hours, and that in the regular group was about 4 hours ( P  > 0.05). The satisfaction of the combined group (96.76%) was significantly higher than that of the control group (46.89%) ( P  > 0.05). Conclusion. The RBF neural network has good classification performance for MRI images. To incorporate intelligent algorithms into the medical information system can optimize the system. RBF has good application prospects in the medical information system, and it is worthy of continuous exploration.