Dictionary learning-based spatiotemporal regularization for 3D dense speckle tracking

Allen Lu; Maria Zontak; Nripesh Parajuli; John C. Stendahl; Nabil Boutagy; Melissa Eberle; Matthew O'Donnell; Albert J. Sinusas; James S. Duncan

doi:10.1117/12.2250695

13 March 2017 Dictionary learning-based spatiotemporal regularization for 3D dense speckle tracking

Allen Lu, Maria Zontak, Nripesh Parajuli, John C. Stendahl, Nabil Boutagy, Melissa Eberle, Matthew O'Donnell, Albert J. Sinusas, James S. Duncan

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Proceedings Volume 10139, Medical Imaging 2017: Ultrasonic Imaging and Tomography; 1013904 (2017) https://doi.org/10.1117/12.2250695
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Speckle tracking is a common method for non-rigid tissue motion analysis in 3D echocardiography, where unique texture patterns are tracked through the cardiac cycle. However, poor tracking often occurs due to inherent ultrasound issues, such as image artifacts and speckle decorrelation; thus regularization is required. Various methods, such as optical flow, elastic registration, and block matching techniques have been proposed to track speckle motion. Such methods typically apply spatial and temporal regularization in a separate manner. In this paper, we propose a joint spatiotemporal regularization method based on an adaptive dictionary representation of the dense 3D+time Lagrangian motion field. Sparse dictionaries have good signal adaptive and noise-reduction properties; however, they are prone to quantization errors. Our method takes advantage of the desirable noise suppression, while avoiding the undesirable quantization error. The idea is to enforce regularization only on the poorly tracked trajectories. Specifically, our method 1.) builds data-driven 4-dimensional dictionary of Lagrangian displacements using sparse learning, 2.) automatically identifies poorly tracked trajectories (outliers) based on sparse reconstruction errors, and 3.) performs sparse reconstruction of the outliers only. Our approach can be applied on dense Lagrangian motion fields calculated by any method. We demonstrate the effectiveness of our approach on a baseline block matching speckle tracking and evaluate performance of the proposed algorithm using tracking and strain accuracy analysis.

Conference Presentation

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Allen Lu, Maria Zontak, Nripesh Parajuli, John C. Stendahl, Nabil Boutagy, Melissa Eberle, Matthew O'Donnell, Albert J. Sinusas, and James S. Duncan "Dictionary learning-based spatiotemporal regularization for 3D dense speckle tracking", Proc. SPIE 10139, Medical Imaging 2017: Ultrasonic Imaging and Tomography, 1013904 (13 March 2017); https://doi.org/10.1117/12.2250695

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