| Autor: |
Thomas M. Vollbrecht, Christopher Hart, Shuo Zhang, Christoph Katemann, Alois M. Sprinkart, Alexander Isaak, Ulrike Attenberger, Claus C. Pieper, Daniel Kuetting, Annegret Geipel, Brigitte Strizek, Julian A. Luetkens |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Frontiers in Cardiovascular Medicine, Vol 11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2297-055X |
| DOI: |
10.3389/fcvm.2024.1323443 |
| Popis: |
PurposeThis study aims to evaluate deep learning (DL) denoising reconstructions for image quality improvement of Doppler ultrasound (DUS)-gated fetal cardiac MRI in congenital heart disease (CHD).MethodsTwenty-five fetuses with CHD (mean gestational age: 35 ± 1 weeks) underwent fetal cardiac MRI at 3T. 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 DL 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.ResultsFetal cardiac cine MRI was successful in 23 fetuses (92%), with two studies excluded due to extensive fetal motion. The 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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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