Synthetic lumbar MRI can aid in diagnosis and treatment strategies based on self-pix networks

• Published in: Scientific reports Vol. 14; no. 1; pp. 20382 - 14
• Main Authors: Song, Ke, Zhu, Wendong, Zhang, Zhenxi, Liu, Bin, Zhang, Meiling, Tang, Tinglong, Liang, Jie, Wu, Weifei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647/245/1628; 631/1647/245/2222; 692/1537/805; 692/700/139; 692/700/1421; 692/700/3934; 692/700/478; Adult; Aged; Computed tomography; Diagnosis; Diagnosis and treatment strategy; Female; Fractures; Humanities and Social Sciences; Humans; Lumbar fracture; Lumbar Vertebrae - diagnostic imaging; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical diagnosis; Middle Aged; multidisciplinary; Neural networks; Neural Networks, Computer; Science; Science (multidisciplinary); Self-pix network; Spinal Fractures - diagnostic imaging; Spinal Fractures - therapy; Spine; Spine (lumbar); Synthetic MRI; Tomography, X-Ray Computed - methods; Lumbar fracture; Self-pix network; Diagnosis and treatment strategy; Synthetic MRI
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-71288-4

CT and MR tools are commonly used to diagnose lumbar fractures (LF). However, numerous limitations have been found in practice. The aims of this study were to innovate and develop a spinal disease-specific neural network and to evaluate whether synthetic MRI of the LF affected clinical diagnosis and treatment strategies. A total of 675 LF patients who met the inclusion and exclusion criteria were included in the study. For each participant, two mid-sagittal CT and T2-weighted MR images were selected; 1350 pairs of LF images were also included. A new Self-pix based on Pix2pix and Self-Attention was constructed. A total of 1350 pairs of CT and MR images, which were randomly divided into a training group (1147 pairs) and a test group (203 pairs), were fed into Pix2pix and Self-pix. The quantitative evaluation included PSNR and SSIM (PSNR1 and SSIM1: real MR images and Pix2pix-generated MR images; PSNR2 and SSIM2: real MR images and Self-pix-generated MR images). The qualitative evaluation, including accurate diagnosis of acute fractures and accurate selection of treatment strategies based on Self-pix-generated MRI, was performed by three spine surgeons. In the LF group, PSNR1 and PSNR2 were 10.884 and 11.021 ( p  < 0.001), and SSIM1 and SSIM2 were 0.766 and 0.771 ( p  < 0.001), respectively. In the ROI group, PSNR1 and PSNR2 were 12.350 and 12.670 ( p  = 0.004), and SSIM1 and SSIM2 were 0.816 and 0.832 ( p  = 0.005), respectively. According to the qualitative evaluation, Self-pix-generated MRI showed no significant difference from real MRI in identifying acute fractures ( p  = 0.689), with a good sensitivity of 84.36% and specificity of 96.65%. No difference in treatment strategy was found between the Self-pix-generated MRI group and the real MRI group ( p  = 0.135). In this study, a disease-specific GAN named Self-pix was developed, which demonstrated better image generation performance compared to traditional GAN. The spine surgeon could accurately diagnose LF and select treatment strategies based on Self-pix-generated T2 MR images.