3D tomographic reconstruction using geometrical models

Xavier L. Battle; Gregory S. Cunningham; Kenneth M. Hanson

doi:10.1117/12.274121

25 April 1997 3D tomographic reconstruction using geometrical models

Xavier L. Battle, Gregory S. Cunningham, Kenneth M. Hanson

Author Affiliations +

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274121
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

We address the issue of reconstructing an object of constant interior density in the context of 3D tomography where there is prior knowledge about the unknown shape. We explore the direct estimation of the parameters of a chosen geometrical model from a set of radiographic measurements, rather than performing operations (segmentation for example) on a reconstructed volume. The inverse problem is posed in the Bayesian framework. A triangulated surface describes the unknown shape and the reconstruction is computed with a maximum a posteriori (MAP) estimate. The adjoint differentiation technique computes the derivatives needed for the optimization of the model parameters. We demonstrate the usefulness of the approach and emphasize the techniques of designing forward and adjoint codes. We use the system response of the University of Arizona Fast SPECT imager to illustrate this method by reconstructing the shape of a heart phantom.

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Xavier L. Battle, Gregory S. Cunningham, and Kenneth M. Hanson "3D tomographic reconstruction using geometrical models", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274121

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