A deep learning method trained on synthetic 3D tagged MR images predicted cardiac displacements with median deviations of 0.72, 0.81, and 1.12 mm in x, y, and z directions compared to manual tracking.
A neural network trained on synthetic data enables rapid, automated, and accurate 3D displacement and strain analysis of cardiac tagged MRI.
BACKGROUND: Three-dimensional (3D) tagged magnetic resonance (MR) imaging enables in-vivo quantification of cardiac motion. While deep learning methods have been developed to analyze these images, they have been restricted to two-dimensional datasets. We present a deep learning approach specifically designed for displacement analysis of 3D cardiac tagged MR images. METHODS: We developed two neural networks to predict left-ventricular motion throughout the cardiac cycle. Networks were trained using synthetic 3D tagged MR images, generated by combining a biophysical left-ventricular model with an analytical MR signal model. Network performance was initially validated on synthetic data, including assessment of signal-to-noise ratio sensitivity. The networks were then retrospectively evaluated on an in-vivo external validation human dataset and an in-vivo porcine study. RESULTS: For the external validation dataset, predicted displacements deviated from manual tracking by median (interquartile range) values of 0.72 (1.17), 0.81 (1.64), and 1.12 (4.17) mm in x, y, and z directions, respectively. In the porcine dataset, strain measurements showed median (interquartile range) differences from manual annotations of 0.01 (0.04), 0.01 (0.06), and -0.01 (0.18) for circumferential, longitudinal, and radial components, respectively. These strain values are within physiological ranges and demonstrate superior performance of the network approach compared to existing 3D tagged image analysis methods. CONCLUSION: The method enables rapid analysis times of approximately 10 s per cardiac phase, making it suitable for large cohort investigations.
Buoso et al. (Wed,) conducted a other in Cardiac motion analysis. Deep learning neural networks (SAXNet and LAXNet) trained on synthetic data vs. Manual tracking and annotations was evaluated on Displacement deviation from manual tracking. A deep learning method trained on synthetic 3D tagged MR images predicted cardiac displacements with median deviations of 0.72, 0.81, and 1.12 mm in x, y, and z directions compared to manual tracking.