Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI - Vollbrecht, Thomas M.; Hart, Christopher; Zhang, Shuo; Katemann, Christoph; Sprinkart, Alois M.; Isaak, Alexander; Attenberger, Ulrike; Pieper, Claus C.; Kuetting, Daniel; Geipel, Annegret; Strizek, Brigitte; Luetkens, Julian A. (Frontiers in Cardiovascular Medicine, 2024)
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 sensing (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 contrastto-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%) with 2 studies excluded due to extensive fetal motion. Image quality of bSSFP DL cine reconstructions was rated superior to standard bSSFP CS cine images in terms of 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), while the extent of artifacts was found to be comparable (4 [3-4.75] vs 4 [3-4], P=0.40). bSSFP DL images had higher aSNR and aCNR compared with