Multiresolution spatiotemporal mechanical model of the heart as a prior to constrain the solution for 4D models of the heart.

Published

Journal Article

In several nuclear cardiac imaging applications (SPECT and PET), images are formed by reconstructing tomographic data using an iterative reconstruction algorithm with corrections for physical factors involved in the imaging detection process and with corrections for cardiac and respiratory motion. The physical factors are modeled as coefficients in the matrix of a system of linear equations and include attenuation, scatter, and spatially varying geometric response. The solution to the tomographic problem involves solving the inverse of this system matrix. This requires the design of an iterative reconstruction algorithm with a statistical model that best fits the data acquisition. The most appropriate model is based on a Poisson distribution. Using Bayes Theorem, an iterative reconstruction algorithm is designed to determine the maximum a posteriori estimate of the reconstructed image with constraints that maximizes the Bayesian likelihood function for the Poisson statistical model. The a priori distribution is formulated as the joint entropy (JE) to measure the similarity between the gated cardiac PET image and the cardiac MRI cine image modeled as a FE mechanical model. The developed algorithm shows the potential of using a FE mechanical model of the heart derived from a cardiac MRI cine scan to constrain solutions of gated cardiac PET images.

Full Text

Duke Authors

Cited Authors

  • Gullberg, GT; Veress, AI; Shrestha, UM; Liu, J; Ordovas, K; Segars, WP; Seo, Y

Published Date

  • June 2019

Published In

Volume / Issue

  • 11072 /

PubMed ID

  • 31413426

Pubmed Central ID

  • 31413426

International Standard Serial Number (ISSN)

  • 0277-786X

Digital Object Identifier (DOI)

  • 10.1117/12.2534906

Language

  • eng

Conference Location

  • United States