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

ABSTRACT Background Hyperpolarized 129 Xe 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. Purpose To present a comprehensive evaluation of transformer and hybrid CNN‐transformer architectures integrating dual‐domain (k‐space and image) processing for HP 129 Xe MRI reconstruction. Study Type Retrospective. Population 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). Field Strength/Sequence 3 T; 3D fast gradient‐recalled echo. Assessment 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. Statistical Tests Friedman test with post hoc Dunn's test and Benjamini‐Hochberg correction for multiple comparisons. Significance level: p < 0.05. Results 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. Data Conclusion KTMR demonstrated superior performance for HP 129 Xe MRI reconstruction at high acceleration factors. Evidence Level 3. Technical Efficacy Stage 1.