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Proceedings Paper

A preliminary investigation of reduced-view image reconstruction from low dose breast CT data
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Paper Abstract

Analytic-based algorithms such as the FDK algorithm is used currently for image reconstruction from data acquired with prototypes of dedicated breast CT scanners. In general, analytic-based algorithms require data collected at a large number (~500) of views. In current breast-CT scans, imaging dose delivered to the patient is about the same as that used in a typical two-view mammography exam. This highly limited total imaging dose, when distributed over a large number of views in breast CT, can result in low-SNR data. There exists a renewed interest in developing optimization-based (i.e., iterative) algorithms for image reconstruction from low-SNR data and/or from sparse-view data collected at a reduced number of views. Results of recent studies on optimization-based algorithms from CT data suggest that the algorithms may reconstruct images of quality higher than than analytic-based algorithms from low-SNR data and/or from sparse-view data. In this work, we investigated image reconstruction from low-SNR patient-breast-CT data collected at a large number (~500), as well as at reduced numbers, of views. The result of the study appears to indicate that optimization-based reconstructions can yield breast-CT images from low-SNR data comparable to, or better than, the corresponding FDK reconstructions.

Paper Details

Date Published: 21 March 2012
PDF: 6 pages
Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831325 (21 March 2012); doi: 10.1117/12.911325
Show Author Affiliations
Junguo Bian, The Univ. of Chicago (United States)
Xiao Han, The Univ. of Chicago (United States)
Kai Yang, Univ. of California at Davis (United States)
Emil Y. Sidky, The Univ. of Chicago (United States)
John M. Boone, Univ. of California at Davis (United States)
Xiaochuan Pan, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 8313:
Medical Imaging 2012: Physics of Medical Imaging
Norbert J. Pelc; Robert M. Nishikawa; Bruce R. Whiting, Editor(s)

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