Optimized Backprojection Filtration Algorithm for Postoperative Reduction and Analysis of Respiratory Infection-Related Factors of Pelvic Fractures by CT Imaging

Abstract

To explore the computed tomography (CT) imaging characteristics and BPF algorithm fine lung CT image efficiency for the diagnosis of pelvic fracture patients and assist clinicians to carry out the disease care and treatment, CT images based on optimized back-projection filtering (BPF) algorithm were utilized to diagnose postoperative reduction of pelvic fractures and penetrating lung infection caused by long-term bed rest. A total of 100 patients with pelvic fracture were selected and all of them underwent pelvic fracture surgery and were rolled into conventional CT diagnosis group (conventional group) and BPF algorithm optimized CT image diagnosis group (BPF group). One group used conventional CT images to guide pelvic reduction and detect lung infections, and the other used BPF algorithm to optimize the images. The results showed that the BPF group was superior to the conventional CT group in both image clarity and shadow area, and the peak signal-to-noise ratio (PSNR) was significantly better than that of the conventional group ( $P<0.05$ ). Nine more cases were detected in the algorithm group than in the conventional group, and the incidence of complications was 48% in the conventional group and 28% in the BPF group, with a statistical difference of 20% between the two groups ( $P<0.05$ ). In addition, the satisfaction of returning patients was 96% in the BPF group and 77% in the conventional group ( $P<0.05$ ). The diagnosis of pulmonary infection was more obvious in the BPF group, indicating that BPF optimization of the CT image was suitable for clinical diagnosis and had a practical application value.