Optimization of an adaptive SPECT system with the scanning linear estimator

Nasrin Ghanbari; Eric Clarkson; Matthew A. Kupinski; Xin Li

doi:10.1117/12.2195782

27 August 2015 Optimization of an adaptive SPECT system with the scanning linear estimator

Nasrin Ghanbari, Eric Clarkson, Matthew A. Kupinski, Xin Li

Proceedings Volume 9594, Medical Applications of Radiation Detectors V; 95940A (2015) https://doi.org/10.1117/12.2195782
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

The adaptive single-photon emission computed tomography (SPECT) system studied here acquires an initial scout image to obtain preliminary information about the object. Then the configuration is adjusted by selecting the size of the pinhole and the magnification that optimize system performance on an ensemble of virtual objects generated to be consistent with the scout data. In this study the object is a lumpy background that contains a Gaussian signal with a variable width and amplitude. The virtual objects in the ensemble are imaged by all of the available configurations and the subsequent images are evaluated with the scanning linear estimator to obtain an estimate of the signal width and amplitude. The ensemble mean squared error (EMSE) on the virtual ensemble between the estimated and the true parameters serves as the performance figure of merit for selecting the optimum configuration. The results indicate that variability in the original object background, noise and signal parameters leads to a specific optimum configuration in each case. A statistical study carried out for a number of objects show that the adaptive system on average performs better than its nonadaptive counterpart.

Citation Download Citation

Nasrin Ghanbari, Eric Clarkson, Matthew A. Kupinski, and Xin Li "Optimization of an adaptive SPECT system with the scanning linear estimator", Proc. SPIE 9594, Medical Applications of Radiation Detectors V, 95940A (27 August 2015); https://doi.org/10.1117/12.2195782

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KEYWORDS

Imaging systems

Single photon emission computed tomography

Monte Carlo methods

Error analysis

Image quality

Interference (communication)

Sensors

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