Determinants of Image Quality in Respiratory Triggered Free Breathing Lung MRI at 0.55T in Adults

Abstract Background Mid-field MRI (0.55 T) has gained attention for pulmonary imaging due to reduced susceptibility artifact, but image quality remains variable across patient populations, limiting its clinical adoption. Purpose To identify technical and clinical factors associated with poor image quality in 0.55 T lung MRI in adults with various pulmonary diseases. Materials and Methods Adults with major pulmonary disease (e.g., infection, pulmonary fibrosis, or cancer) who were scheduled for chest CT or PET/CT at a single healthcare site were prospectively recruited from January to August 2023 to undergo same-day 0.55 T MRI. Exclusion criteria included inability to communicate in English, overlapping pulmonary diagnoses already represented, or declining consent. Respiratory-triggered T2-weighted BLADE and T1-weighted UTE sequences were acquired on a Siemens MAGNETOM Free.Max (0.55 T) scanner. Six radiologists independently graded overall image quality (1 = poor, 2 = fine, 3 = excellent). Respiratory metrics were quantified, including tidal depth (TD), respiratory rate (RR), and respiration length. BMI and body surface area were calculated. One-way ANOVA was used to test the association between these factors and image quality. Interreader agreement was assessed using Fleiss kappa and the intraclass correlation coefficient. Results Twenty-eight participants (mean age, 59 years +/- 19; 17 women) were evaluated. Fibrotic interstitial lung disease was linked to degraded image quality. Deeper TD (P = 0.04), longer respiration length (P = 0.002), and higher BMI (P = 0.02) were significant predictors of degradation on univariate analysis. RR and body surface area were not significantly associated (P 0.05). Conclusion This preliminary study suggests that BMI, pulmonary fibrosis, and deep/slow breathing patterns may be associated with degraded respiratory triggered 0.55 T lung MRI. If confirmed in larger, more diverse cohorts, these findings could help identify patients at risk for lower-quality imaging and inform strategies to optimize image quality in clinical practice.