Benchmarking Hybrid CNN-Transformer Versus Pure Transformer Architectures for Accelerated Hyperpolarized 129Xe MRI Reconstruction.

Hyperpolarized 129Xe MRI faces technical challenges including low signal-to-noise ratio and breath-hold constraints. Current literature focuses on proprietary deep learning methods or image-domain enhancements. To present a comprehensive evaluation of transformer and hybrid CNN-transformer architectures integrating dual-domain (k-space and image) processing for HP 129Xe MRI reconstruction. Retrospective. Two hundred five participants (22 healthy [male and female, 18-85 years], 26 COPD [male and female, 50-85 years], 90 asthma [male and female, 18-70 years], 67 long-COVID [male and female, 18-70 years]) yielding 1640 2D slices. Dataset split: 80% training (1312 slices), 10% validation (164 slices), 10% test (164 slices). 3 T; 3D fast gradient-recalled echo. Five architectures were compared: KTMR (hybrid transformer-CNN), KIKI-net (pure CNN), ReconFormer, SwinMR, and MR-IPT (pure transformer) at acceleration factors of 3, 7, and 10. Performance was assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean squared error (NMSE). Ventilation defect percentage (VDP) agreement with semi-automated analysis was evaluated. Friedman test with post hoc Dunn's test and Benjamini-Hochberg correction for multiple comparisons. Significance level: p < 0.05. At 10-fold acceleration, KTMR produced PSNR of 36.4 ± 2.8 dB and SSIM of 0.88 ± 0.12, significantly outperforming KIKI-net (32.5 ± 3.4 dB, 0.81 ± 0.12), ReconFormer (29.7 ± 2.6 dB, 0.76 ± 0.12), SwinMR (30.5 ± 2.8 dB, 0.76 ± 0.09), and MR-IPT (28.8 ± 2.4 dB, 0.74 ± 0.11). VDP measurements showed mean bias of 1.94% at 3-fold, 2.12% at 7-fold, and 2.69% at 10-fold acceleration. KTMR demonstrated superior performance for HP 129Xe MRI reconstruction at high acceleration factors. 3. Stage 1.