Vision Transformer Reconstruction for Super-Resolution and Artifact Reduction in 1.5-Tesla Fast-Spin-Echo Pelvic MRI: A Diagnostic-Accuracy Study

Abstract

Introduction: Fast-spin-echo (FSE) MRI is the reference modality for pelvic evaluation, but high-resolution acquisition is slow and motion-prone. Deep-learning reconstruction may recover diagnostic quality from short, motion-tolerant acquisitions, yet most evidence relies on image-similarity indices rather than radiologist performance. We validated a Vision-Transformer (ViT) reconstruction for simultaneous super-resolution and motion-artifact reduction in 1.5-Tesla T2-weighted FSE pelvic MRI.

Methods: In this retrospective diagnostic-accuracy study (STARD 2015) at a tertiary hospital in Palembang, Indonesia, 450 examinations (development n=360; test n=90) were analysed. A U-shaped shifted-window ViT reconstructed high-resolution images from retrospectively degraded low-resolution/motion-corrupted inputs. Two blinded radiologists scored each test case under native low-resolution, UNet- and ViT-reconstructed conditions against a histopathology/expert-consensus reference standard. Sensitivity, specificity, predictive values, AUC, likelihood ratios (95% CI), inter-reader kappa, DeLong and McNemar tests, and multivariable logistic regression were computed.

Results: Target prevalence was 53.3%. ViT reconstruction achieved sensitivity 93.8% (95% CI 83.2–97.9), specificity 88.1% (75.0–94.8), AUC 0.943 (0.894–0.992), LR+ 7.87 and LR− 0.071, versus AUC 0.881 (UNet) and 0.751 (low-resolution); ViT vs low-resolution DeLong p<0.001, McNemar p<0.001. Inter-reader agreement rose from kappa 0.49 to 0.87. ViT gave the best fidelity (PSNR 34.82 dB; SSIM 0.941; p<0.001 vs UNet) at 0.15 s/slice. Sub-centimetre lesions (OR 3.84, p=0.006) and severe motion (OR 2.97, p=0.029) independently predicted error.

Conclusion: A shifted-window Vision Transformer recovered diagnostic-quality pelvic FSE MRI from short, motion-tolerant acquisitions, significantly improving radiologist lesion detection and inter-reader agreement over convolutional reconstruction. The real-time, PACS-compatible pipeline is promising for high-throughput pelvic MRI and warrants prospective validation.