Abstract

Accurate, comprehensive, quantification of regional left ventricular (LV) deformation is crucial for detection, risk stratification, and management of patients with ischemic heart disease. In this BRP, four partners from two academic institutions and industry will work together to develop and validate an integrated imaging/ image analysis system that will accurately, robustly and reproducibly quantify regional LV strain and strain rate from four-dimensional (3 spatial dimensions and time) echocardiographic (4DE) image sequences. Intramural displacement will be estimated using a phase-sensitive-correlation-based speckle tracking approach being developed by a team lead by Matthew O'Donnell at the University of Michigan. This information will be derived from radiofrequency (RF) signal data acquired from an ultrasound array, giving access to beam-formed acoustic data, using an approach being developed by a team lead by Jeff Powers, Ph.D. from Philips Medical Systems. Displacement information at the myocardial surface will be derived from B-mode images using a shape-tracking strategy being developed by a team lead by James Duncan, Ph.D. at Yale University, who will also serve as the PI of the BRP. Intramural and surface displacement information will be combined using an integrated segmentation/deformation estimator based on a biomechanical model (also being developed at Yale), to provide comprehensive, 4D estimates of myocardial strains, strain rates and material parameters. The approach will be validated/evaluated using phantoms and in vivo testing in collaboration with a team lead by Yale cardiologist Albert Sinusas, M.D. In vivo evaluation will include experiments based on acute and chronic canine models of ischemic injury for the quantification of transmurality of injury, subsequent LV remodeling and response to ACE inhibitor therapy. Clinical feasibility will be established in human studies. The 4DE-derived indices of LV deformation will be shown to be comparable to those derived from Magnetic Resonance (MR) tagging.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL082640-04
Application #
7632202
Study Section
Special Emphasis Panel (ZRG1-SBIB-A (50))
Program Officer
Buxton, Denis B
Project Start
2006-09-01
Project End
2011-05-31
Budget Start
2009-06-01
Budget End
2010-05-31
Support Year
4
Fiscal Year
2009
Total Cost
$1,425,832
Indirect Cost

  Institution

Name
Yale University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520

  Publications

Compas, Colin B; Wong, Emily Y; Huang, Xiaojie et al. (2015) Correction to ""Radial Basis Functions for Combining Shape and Speckle Tracking in 4D Echocardiography"". IEEE Trans Med Imaging 34:690
Parajuli, Nripesh; Compas, Colin B; Lin, Ben A et al. (2015) Sparsity and Biomechanics Inspired Integration of Shape and Speckle Tracking for Cardiac Deformation Analysis. Funct Imaging Model Heart 9126:57-64
Compas, Colin B; Wong, Emily Y; Huang, Xiaojie et al. (2014) Radial basis functions for combining shape and speckle tracking in 4D echocardiography. IEEE Trans Med Imaging 33:1275-89
Huang, Xiaojie; Dione, Donald P; Compas, Colin B et al. (2014) Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. Med Image Anal 18:253-71
Huang, Xiaojie; Dione, Donald P; Lin, Ben A et al. (2013) Segmentation of 4D echocardiography using stochastic online dictionary learning. Med Image Comput Comput Assist Interv 16:57-65
Huang, Xiaojie; Dione, Donald P; Compas, Colin B et al. (2012) A dynamical appearance model based on multiscale sparse representation: segmentation of the left ventricle from 4D echocardiography. Med Image Comput Comput Assist Interv 15:58-65
Pearlman, P C; Tagare, H D; Lin, B A et al. (2012) Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor. Med Image Anal 16:351-60
Pearlman, Paul C; Tagare, Hemant D; Lin, Ben A et al. (2011) Segmentation of 3D RF echocardiography using a multiframe spatio-temporal predictor. Inf Process Med Imaging 22:37-48
Zhu, Yun; Papademetris, Xenophon; Sinusas, Albert J et al. (2010) Segmentation of the left ventricle from cardiac MR images using a subject-specific dynamical model. IEEE Trans Med Imaging 29:669-87
Zhu, Yun; Papademetris, Xenophon; Sinusas, Albert J et al. (2010) A coupled deformable model for tracking myocardial borders from real-time echocardiography using an incompressibility constraint. Med Image Anal 14:429-48

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