Radiation dose reduction in computed tomography perfusion using spatial-temporal Bayesian methods

Ruogu Fang; Ashish Raj; Tsuhan Chen; Pina C. Sanelli

doi:10.1117/12.911563

23 February 2012 Radiation dose reduction in computed tomography perfusion using spatial-temporal Bayesian methods

Ruogu Fang, Ashish Raj, Tsuhan Chen, Pina C. Sanelli

Proceedings Volume 8313, Medical Imaging 2012: Physics of Medical Imaging; 831345 (2012) https://doi.org/10.1117/12.911563
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

In current computed tomography (CT) examinations, the associated X-ray radiation dose is of significant concern to patients and operators, especially CT perfusion (CTP) imaging that has higher radiation dose due to its cine scanning technique. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) parameter as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably increase data noise and degrade CT perfusion maps greatly if no adequate noise control is applied during image reconstruction. To capture the essential dynamics of CT perfusion, a simple spatial-temporal Bayesian method that uses a piecewise parametric model of the residual function is used, and then the model parameters are estimated from a Bayesian formulation of prior smoothness constraints on perfusion parameters. From the fitted residual function, reliable CTP parameter maps are obtained from low dose CT data. The merit of this scheme exists in the combination of analytical piecewise residual function with Bayesian framework using a simpler prior spatial constrain for CT perfusion application. On a dataset of 22 patients, this dynamic spatial-temporal Bayesian model yielded an increase in signal-tonoise-ratio (SNR) of 78% and a decrease in mean-square-error (MSE) of 40% at low dose radiation of 43mA.

Citation Download Citation

Ruogu Fang, Ashish Raj, Tsuhan Chen, and Pina C. Sanelli "Radiation dose reduction in computed tomography perfusion using spatial-temporal Bayesian methods", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831345 (23 February 2012); https://doi.org/10.1117/12.911563

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