Anzeige
Erschienen in:
2017 | Supplement | Buchkapitel
Learning-Based Spatiotemporal Regularization and Integration of Tracking Methods for Regional 4D Cardiac Deformation Analysis
verfasst von : Allen Lu, Maria Zontak, Nripesh Parajuli, John C. Stendahl, Nabil Boutagy, Melissa Eberle, Imran Alkhalil, Matthew O’Donnell, Albert J. Sinusas, James S. Duncan
Erschienen in: Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Dense cardiac motion tracking and deformation analysis from echocardiography is important for detection and localization of myocardial dysfunction. However, tracking methods are often unreliable due to inherent ultrasound imaging properties. In this work, we propose a new data-driven spatiotemporal regularization strategy. We generate 4D Lagrangian displacement patches from different input sources as training data and learn the regularization procedure via a multi-layered perceptron (MLP) network. The learned regularization procedure is applied to initial noisy tracking results. We further propose a framework for integrating tracking methods to produce better overall estimations. We demonstrate the utility of this approach on block-matching, surface tracking, and free-form deformation-based methods. Finally, we quantitatively and qualitatively evaluate our performance on both tracking and strain accuracy using both synthetic and in vivo data.