| Title: | Improving robustness of automatic cardiac function quantification from cine magnetic resonance imaging using synthetic image data |
| Journal: | Scientific Reports |
| Published: | 14 Feb 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35165324/ |
| DOI: | https://doi.org/10.1038/s41598-022-06315-3 |
| URL: | https://www.nature.com/articles/s41598-022-06315-3.pdf |
| Citations: | 7 (6 in last 2 years) as of 8 Aug 2024 |
Publication 12534
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Abstract
Although having been the subject of intense research over the years, cardiac function quantification from MRI is still not a fully automatic process in the clinical practice. This is partly due to the shortage of training data covering all relevant cardiovascular disease phenotypes. We propose to synthetically generate short axis CINE MRI using a generative adversarial model to expand the available data sets that consist of predominantly healthy subjects to include more cases with reduced ejection fraction. We introduce a deep learning convolutional neural network (CNN) to predict the end-diastolic volume, end-systolic volume, and implicitly the ejection fraction from cardiac MRI without explicit segmentation. The left ventricle volume predictions were compared to the ground truth values, showing superior accuracy compared to state-of-the-art segmentation methods. We show that using synthetic data generated for pre-training a CNN significantly improves the prediction compared to only using the limited amount of available data, when the training set is imbalanced.</p>
8 Keywords
- Deep Learning
- Heart Ventricles
- Humans
- Image Processing, Computer-Assisted
- Magnetic Resonance Imaging, Cine
- Neural Networks, Computer
- Stroke Volume
- Ventricular Function, Left
13 Authors
- Bogdan A. Gheorghiță
- Lucian M. Itu
- Puneet Sharma
- Constantin Suciu
- Jens Wetzl
- Christian Geppert
- Mohamed Ali Asik Ali
- Aaron M. Lee
- Stefan K. Piechnik
- Stefan Neubauer
- Steffen E. Petersen
- Jeanette Schulz-Menger
- Teodora Chițiboi
1 Application
| Application ID | Title |
|---|---|
| 2964 | Description of cardiovascular phenotype in the UK Biobank population based on cardiovascular magnetic resonance and carotid ultrasound |
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