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Proceedings Paper

Reconstruction from a flexible number of projections in cone-beam computed tomography via active shape models
Author(s): Peter B. Noël; Jason J. Corso; Jinhui Xu; Kenneth R. Hoffmann; Sebastian Schafer; Alan M. Walczak
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Paper Abstract

With a steady increase of CT interventions, population dose is increasing. Thus, new approaches must be developed to reduce the dose. In this paper, we present a means for rapid identification and reconstruction of objects of interest in reconstructed data. Active shape models are first trained on sets of data obtained from similar subjects. A reconstruction is performed using a limited number of views. As each view is added, the reconstruction is evaluated using the active shape models. Once the object of interest is identified, the volume of interest alone is reconstructed, saving reconstruction time. Note that the data outside of the objects of interest can be reconstructed using fewer views or lower resolution providing the context of the region of interest data. An additional feature of our algorithm is that a reliable segmentation of objects of interest is achieved from a limited set of projections. Evaluations were performed using simulations with Shepp-Logan phantoms and animal studies. In our evaluations, regions of interest are identified using about 33 projections on average. The overlap of the identified regions with the true regions of interest is approximately 91%. The identification of the region of interest requires about 1/5 of the time required for full reconstruction, the time for reconstruction of the region of interest is currently determined by the fraction of voxels in the region of interest (i.e, voxels in region of interest/voxels in full volume). The algorithm has several important clinical applications, e.g., rotational angiography, digital tomosynthesis mammography, and limited view computed tomography.

Paper Details

Date Published: 27 March 2009
PDF: 7 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725949 (27 March 2009); doi: 10.1117/12.811751
Show Author Affiliations
Peter B. Noël, SUNY at Buffalo (United States)
Jason J. Corso, SUNY at Buffalo (United States)
Jinhui Xu, SUNY at Buffalo (United States)
Kenneth R. Hoffmann, SUNY at Buffalo (United States)
Sebastian Schafer, SUNY at Buffalo (United States)
Alan M. Walczak, SUNY at Buffalo (United States)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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