Deep Learning Denoising Reconstruction for Improved Image Quality in Fetal Cardiac Cine MRI

Abstract

Abstract Purpose: To evaluate deep learning (DL) denoising reconstructions for image quality improvement of Doppler ultrasound (DUS)-gated fetal cardiac MRI in congenital heart disease (CHD). Methods: Twenty-five fetuses with CHD (mean gestational age: 35±1 weeks) underwent fetal cardiac MRI at 3 Tesla. Cine imaging was acquired using a balanced steady-state free precession (bSSFP) sequence with Doppler ultrasound gating. Images were reconstructed using both compressed SENSE (bSSFP CS) and a pre-trained convolutional neural network trained for deep-learning denoising (bSSFP DL). Images were compared qualitatively based on a 5-point Likert scale (from 1=non-diagnostic to 5=excellent) and quantitatively by calculating the apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (aCNR). Diagnostic confidence was assessed for the atria, ventricles, foramen ovale, valves, great vessels, aortic arch, and pulmonary veins. Results: Fetal cardiac cine MRI was successful in 23 fetuses (92%). Image quality of bSSFP DL cine reconstructions compared with standard bSSFP CS cine images was rated significantly higher regarding contrast (3 [interquartile range: 2-4] vs 5 [4-5], P<0.001) and endocardial edge definition (3 [2-4] vs 4 [4-5], P<0.001), whereas the level of artifacts deemed comparable (4 [3-4.75] vs 4 [3-4], P=0.40). bSSFP DL images had higher aSNR and aCNR compared with the bSSFP CS images (aSNR: 13.4±6.9 vs 8.3±3.6, P<0.001; aCNR: 26.6±15.8 vs 14.4±6.8, P<0.001). Diagnostic confidence of the bSSFP DL images was superior for the evaluation of cardiovascular structures (e.g., atria and ventricles: P=0.003). Conclusion: DL image denoising provides superior quality for DUS-gated fetal cardiac cine imaging of CHD compared to standard CS image reconstruction.