Deep learning reconstruction of undersampled cine bSSFP images up to acceleration factor 5 yielded diagnostically adequate image quality and comparable biventricular volumetric indices (P=0.447).
Observational (n=15)
Does deep learning-based reconstruction of undersampled cine bSSFP images preserve image quality and biventricular volumetric accuracy compared to fully sampled acquisitions in patients with pectus excavatum?
Deep learning-based reconstruction of cine cardiac MRI allows up to 5-fold acceleration without significantly compromising diagnostic image quality or biventricular volumetric measurements.
p-value: p=0.447
BACKGROUND: Breath-holding (BH) for cine balanced steady state free precession (bSSFP) imaging is challenging for patients with impaired BH capacity. Deep learning-based reconstruction (DLR) of undersampled k-space promises to shorten BHs while preserving image quality and accuracy of ventricular assessment. PURPOSE: To perform a systematic evaluation of DLR of cine bSSFP images from undersampled k-space over a range of acceleration factors. STUDY TYPE: Retrospective. SUBJECTS: Fifteen pectus excavatum patients (mean age 16.8 ± 5.4 years, 20% female) with normal cardiac anatomy and function and 12-second BH capability. FIELD STRENGTH/SEQUENCE: 1.5-T, cine bSSFP. ASSESSMENT: Retrospective DLR was conducted by applying compressed sensitivity encoding (C-SENSE) acceleration to systematically undersample fully sampled k-space cine bSSFP acquisition data over an acceleration/undersampling factor (R) considering a range of 2 to 8. Quality imperceptibility (QI) measures, including structural similarity index measure, were calculated using images reconstructed from fully sampled k-space as a reference. Image quality, including contrast and edge definition, was evaluated for diagnostic adequacy by three readers with varying levels of experience in cardiac MRI (>4 years, >18 years, and 1 year). Automated DL-based biventricular segmentation was performed commercially available software by cardiac radiologists with more than 4 years of experience. STATISTICAL TESTS: Tukey box plots, linear mixed effects model, analysis of variance (ANOVA), weighted kappa, Kruskal-Wallis test, and Wilcoxon signed-rank test were employed as appropriate. A P-value <0.05 was considered statistically significant. RESULTS: There was a significant decrease in the QI values and edge definition scores as R increased. Diagnostically adequate image quality was observed up to R = 5. The effect of R on all biventricular volumetric indices was non-significant (P = 0.447). DATA CONCLUSION: The biventricular volumetric indices obtained from the reconstruction of fully sampled cine bSSFP acquisitions and DLR of the same k-space data undersampled by C-SENSE up to R = 5 may be comparable. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.
Pednekar et al. (Thu,) conducted a observational in Pectus excavatum (n=15). Deep learning-based reconstruction (DLR) of undersampled k-space vs. Fully sampled k-space cine bSSFP acquisition was evaluated on Biventricular volumetric indices (p=0.447). Deep learning reconstruction of undersampled cine bSSFP images up to acceleration factor 5 yielded diagnostically adequate image quality and comparable biventricular volumetric indices (P=0.447).