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Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease.

ABSTRACT: BACKGROUND:Deep learning algorithms are increasingly used for automatic medical imaging analysis and cardiac chamber segmentation. Especially in congenital heart disease, obtaining a sufficient number of training images and data anonymity issues remain of concern. METHODS:Progressive generative adversarial networks (PG-GAN) were trained on cardiac magnetic resonance imaging (MRI) frames from a nationwide prospective study to generate synthetic MRI frames. These synthetic frames were subsequently used to train segmentation networks (U-Net) and the quality of the synthetic training images, as well as the performance of the segmentation network was compared to U-Net-based solutions trained entirely on patient data. RESULTS:Cardiac MRI data from 303 patients with Tetralogy of Fallot were used for PG-GAN training. Using this model, we generated 100,000 synthetic images with a resolution of 256?×?256 pixels in 4-chamber and 2-chamber views. All synthetic samples were classified as anatomically plausible by human observers. The segmentation performance of the U-Net trained on data from 42 separate patients was statistically significantly better compared to the PG-GAN based training in an external dataset of 50 patients, however, the actual difference in segmentation quality was negligible (

SUBMITTER: Diller GP

PROVIDER: S-EPMC7542728 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease.

Diller Gerhard-Paul GP Vahle Julius J Radke Robert R Vidal Maria Luisa Benesch MLB Fischer Alicia Jeanette AJ Bauer Ulrike M M UMM Sarikouch Samir S Berger Felix F Beerbaum Philipp P Baumgartner Helmut H Orwat Stefan S

BMC medical imaging 20201008 1

<h4>Background</h4>Deep learning algorithms are increasingly used for automatic medical imaging analysis and cardiac chamber segmentation. Especially in congenital heart disease, obtaining a sufficient number of training images and data anonymity issues remain of concern.<h4>Methods</h4>Progressive generative adversarial networks (PG-GAN) were trained on cardiac magnetic resonance imaging (MRI) frames from a nationwide prospective study to generate synthetic MRI frames. These synthetic frames we ...[more]

PMID: 33032536

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Project description:ImportancePrenatal diagnosis of complex congenital heart defects reduces mortality and morbidity in affected infants. However, fetal echocardiography can be limited by poor acoustic windows, and there is a need for improved diagnostic methods.ObjectiveTo assess the clinical utility of fetal cardiovascular magnetic resonance imaging in cases in which fetal echocardiography could not visualize all relevant anatomy.Design, setting, and participantsThis cohort study was conducted between January 20, 2017, and June 29, 2020, at Skåne University Hospital (Lund, Sweden), a tertiary center for pediatric cardiology and thoracic surgery. Participants were fetuses referred for fetal cardiovascular magnetic resonance examination by a pediatric cardiologist after an inconclusive echocardiograph.ExposuresFetal cardiovascular magnetic resonance examination requested by the patient's pediatric cardiologist.Main outcomes and measuresAny change in patient management because of diagnostic information gained from fetal cardiovascular magnetic resonance imaging.ResultsA total of 31 fetuses underwent cardiovascular magnetic resonance examination at a median gestational age of 36 weeks (range, 31-39 weeks). Overall, fetal cardiovascular magnetic resonance imaging had clinical utility, affecting patient management and/or parental counseling in 26 cases (84%). For aortic arch anatomy including signs of coarctation (20 fetuses), fetal cardiovascular magnetic resonance imaging added diagnostic information in 16 cases (80%). For assessment of univentricular vs biventricular outcome in borderline left ventricle, unbalanced atrioventricular septal defect, and pulmonary atresia with intact ventricular septum (15 fetuses), fetal cardiovascular magnetic resonance imaging visualized intracardiac anatomy and ventricular function, allowing assessment of outcome in 13 cases (87%). In 4 fetuses with hypoplastic left heart syndrome, fetal cardiovascular magnetic resonance imaging helped delivery planning in 3 cases (75%). Finally, fetal cardiovascular magnetic resonance imaging provided valuable information for parental counseling in 21 cases (68%).Conclusions and relevanceIn this cohort study, fetal cardiovascular magnetic resonance imaging added clinically useful information to what was available from echocardiography. These findings suggest that fetal CMR has the potential to affect clinical decision-making in challenging cases of congenital heart defects with inconclusive data from echocardiography. Fetal cardiovascular magnetic resonance imaging showed an association with clinical decision-making, including mode of delivery and early postnatal care, as well as with parental counseling.

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Dataset Information

Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease.

Publications

Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease.

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