Key result
Free-breathing deep-learning CMR matches standard breath-holding CMR for MR volume with a ~1-mL difference.
Why the study?
Breath-holding CMR imaging poses challenges in patients with comorbidities, and the accuracy of deep learning-based free-breathing CMR for mitral regurgitation quantification needed evaluation.
Does deep learning-based free-breathing CMR provide accurate quantification of ventricular parameters and mitral regurgitation compared to conventional breath-holding CMR in patients with degenerative MR?
Cross-Sectional (n=27)
Does deep learning-based free-breathing CMR provide accurate quantification of ventricular parameters and mitral regurgitation compared to conventional breath-holding CMR in patients with degenerative MR?
Mean Difference: -1
p-value: p=<0.001
Deep learning-based free-breathing CMR demonstrates comparable accuracy to conventional breath-holding CMR for quantifying degenerative mitral regurgitation, offering an alternative for patients unable to perform breath holds.
May enable free-breathing DL-CMR for MR patients unable to breath-hold; leaves open need for larger validation before routine use.
Background Cardiovascular magnetic resonance (CMR) imaging acquisition frequently relies on breath-holding, which poses challenges in patients with certain underlying comorbidities. A new technique consisting of deep learning–based accelerated 2D steady-state free precession (SSFP) cine imaging (Sonic DL Cine, GE HealthCare) allows for 1 heart beat free-breathing SSFP acquisition. This study aimed to demonstrate the accuracy of free-breathing acquisition SSFP using Sonic DL Cine and free-breathing phase contrast imaging versus standard breath holding for mitral regurgitation (MR) quantification in patients with degenerative MR. Methods Patients with degenerative MR were prospectively enrolled and underwent both breath-holding and free-breathing CMR. Short axis SSFP images and phase contrast images in the ascending aorta and proximal pulmonary artery were acquired using breath hold and free-breathing techniques to quantify ventricular volumes and blood flow in the ascending aorta and proximal pulmonary artery. MR volume and fraction were calculated for each patient using both techniques. Pearson’s correlation and Bland-Altman plots were utilized to compare the two groups. Results A total of 27 patients (64 ± 9 years; 67% male) underwent 33 CMR studies. There was no statistically significant difference in left ventricular end-diastolic volume (LVEDV), LV stroke volume (SV), aortic phase contrast, pulmonary artery phase contrast, MR volume or MR fraction between breath-holding and free-breathing CMR. Pearson’s correlation and Bland-Altman plots demonstrated excellent correlation and good agreement for LVEDV (r 2 = 0.96; mean difference 0.7 mL; p<0.001), LVESV (r 2 = 0.93; mean difference 2 mL; p<0.001), LVSV (r 2 = 0.96; mean difference -1 mL; p<0.001), MR volume (r 2 = 0.96; mean difference –1 mL; p<0.001), and MR fraction (r 2 = 0.93; mean difference –0.4 mL; p<0.001). Conclusion Compared to conventional breath-holding, free-breathing CMR is feasible and comparable in the evaluation of ventricular parameters and MR quantification in patients with degenerative MR. This CMR technique would allow clinicians to accurately quantify MR in patients who are unable to perform conventional breath hold CMR.
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Chamay et al. (2026) conducted a cross-sectional in Degenerative mitral regurgitation (n=27). Deep learning-based free-breathing CMR vs. Conventional breath-holding CMR was evaluated on Agreement of MR volume quantification (Mean difference -1 mL, p=<0.001). Deep learning-based free-breathing CMR demonstrated excellent agreement with conventional breath-holding CMR for quantifying MR volume (r2=0.96, mean difference -1 mL, p<0.001).
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