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DOI 10.1002/jmri.29081

Медицинская библиотека · Journal of magnetic resonance imaging : JMRI · 2023

Accelerated Cine Cardiac MRI Using Deep Learning-Based Reconstruction: A Systematic Evaluation.

Pednekar Amol, Kocaoglu Murat, Wang Hui, Tanimoto Aki, Tkach Jean A, Lang Sean, Taylor Michael D

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Аннотация

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.

Для цитирования: Pednekar Amol, Kocaoglu Murat, Wang Hui, Tanimoto Aki, Tkach Jean A, Lang Sean, Taylor Michael D. Accelerated Cine Cardiac MRI Using Deep Learning-Based Reconstruction: A Systematic Evaluation.. Journal of magnetic resonance imaging : JMRI. 2023. DOI: 10.1002/jmri.29081.

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