Optimization-based image reconstruction from sparse-view data in offset-detector CBCT

doi:10.1088/0031-9155/58/2/205

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Optimization-based image reconstruction from sparse-view data in offset-detector CBCT

2012-12-21

DOI：10.1088/0031-9155/58/2/205

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Abstract

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The field of view (FOV) of a cone-beam computed tomography (CBCT) unit in a single-photon emission computed tomography (SPECT)/CBCT system can be increased by offsetting the CBCT detector. Analytic-based algorithms have been developed for image reconstruction from data collected at a large number of densely sampled views in offset-detector CBCT. However, the radiation dose involved in a large number of projections can be of a health concern to the imaged subject. CBCT-imaging dose can be reduced by lowering the number of projections. As analytic-based algorithms are unlikely to reconstruct accurate images from sparse-view data, we investigate and characterize in the work optimization-based algorithms, including an adaptive steepest descent-weighted projection onto convex sets (ASD-WPOCS) algorithms, for image reconstruction from sparse-view data collected in offset-detector CBCT. Using simulated data and real data collected from a physical pelvis phantom and patient, we verify and characterize properties of the algorithms under study. Results of our study suggest that optimization-based algorithms such as ASD-WPOCS may be developed for yielding images of potential utility from a number of projections substantially smaller than those used currently in clinical SPECT/CBCT imaging, thus leading to a dose reduction in CBCT imaging.

Keywords:

CONE-BEAM CT

COMPUTED-TOMOGRAPHY

ALGORITHM

PERFORMANCE

PROJECTIONS

SYSTEM

BACKPROJECTION

IMPLEMENTATION

CONVERGENCE

QUALITY

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