Paper
21 March 2012 A preliminary investigation of reduced-view image reconstruction from low dose breast CT data
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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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Junguo Bian, Xiao Han, Kai Yang, Emil Y. Sidky, John M. Boone, and Xiaochuan Pan "A preliminary investigation of reduced-view image reconstruction from low dose breast CT data", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831325 (21 March 2012); https://doi.org/10.1117/12.911325
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Cited by 3 scholarly publications.
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KEYWORDS
Reconstruction algorithms

Breast

Image restoration

Computed tomography

Data modeling

Algorithm development

Data acquisition

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