Volumetric myocardial mechanics from 3D+t ultrasound data with multi-model tracking

  • Authors:
  • Yang Wang;Bogdan Georgescu;Helene Houle;Dorin Comaniciu

  • Affiliations:
  • Siemens Corporate Research, Princeton, NJ;Siemens Corporate Research, Princeton, NJ;Siemens Ultrasound, Mountain View, CA;Siemens Corporate Research, Princeton, NJ

  • Venue:
  • STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
  • Year:
  • 2010

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Abstract

Global and regional cardiac deformation provides important information on myocardial (dys-)function in a variety of clinical settings. Recent developments in the field of echocardiography have allowed the cardiologist to quantify cardiac deformation in a non-invasive manner. Unstitched volumetric data can be captured in a high frame rate by real-time ultrasound imaging. However, most existing methods for measuring myocardial mechanics are often limited to measurements in one or two dimensions. Since myocardial tissue is virtually incompressible, the ventricular wall contains the same volume during the cardiac cycle and, thus, deforms in three dimensions. In this paper, we propose an automatic method to estimate the regional 3D myocardial mechanics on ultrasound images by recovering the 3D non-rigid deformation of the myocardium. The key advantage of our method is fusing multiple information, such as speckle patterns, image gradients, boundary detection, and motion prediction, to achieve a robust tracking on 3D+t ultrasound data. Preliminary results in both in-vitro and in-vivo experiments confirmed these findings in a quantitative manner, as the motion and mechanical parameters, such as displacement and strain, estimated by our method are close to both the ground-truth data and the clinical evaluation. The proposed method is efficient and achieves high speed performance of less than 1 second per frame for volumetric ultrasound data.