Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model.

Background: Deep learning is the state-of-the-art approach for automated segmentation of the left ventricle (LV) and right ventricle (RV) in cardiovascular magnetic resonance (CMR) images. However, these models have been mostly trained and validated using CMR datasets of structurally normal hearts or cases with acquired cardiac disease, and are therefore not well-suited to handle cases with congenital cardiac disease such as tetralogy of Fallot (TOF). We aimed to develop and validate a dedicated model with improved performance for LV and RV cavity and myocardium quantification in patients with repaired TOF.

Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI.

Semantic segmentation using convolutional neural networks (CNNs) is the state-of-the-art for many medical image segmentation tasks including myocardial segmentation in cardiac MR images. However, the predicted segmentation maps obtained from such standard CNN do not allow direct quantification of regional shape properties such as regional wall thickness. Furthermore, the CNNs lack explicit shape constraints, occasionally resulting in unrealistic segmentations.

Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach.

T1 and ECV mapping are quantitative methods for myocardial tissue characterization using cardiac MRI, and are highly relevant for the diagnosis of diffuse myocardial diseases. Since the maps are calculated pixel-by-pixel from a set of MRI images with different T1-weighting, it is critical to assure exact spatial correspondence between these images. However, in practice, different sources of motion e.