Patient-specific scatter correction for flat-panel detector-based cone-beam CT imaging.

A patient-specific scatter correction algorithm is proposed to mitigate scatter artefacts in cone-beam CT (CBCT). The approach belongs to the category of convolution-based methods in which a scatter potential function is convolved with a convolution kernel to estimate the scatter profile. A key step in this method is to determine the free parameters introduced in both scatter potential and convolution kernel using a so-called calibration process, which is to seek for the optimal parameters such that the models for both scatter potential and convolution kernel is able to optimally fit the previously known coarse estimates of scatter profiles of the image object. Both direct measurements and Monte Carlo (MC) simulations have been proposed by other investigators to achieve the aforementioned rough estimates. In the present paper, a novel method has been proposed and validated to generate the needed coarse scatter profile for parameter calibration in the convolution method. The method is based upon an image segmentation of the scatter contaminated CBCT image volume, followed by a reprojection of the segmented image volume using a given x-ray spectrum. The reprojected data is subtracted from the scatter contaminated projection data to generate a coarse estimate of the needed scatter profile used in parameter calibration. The method was qualitatively and quantitatively evaluated using numerical simulations and experimental CBCT data acquired on a clinical CBCT imaging system. Results show that the proposed algorithm can significantly reduce scatter artefacts and recover the correct CT number. Numerical simulation results show the method is patient specific, can accurately estimate the scatter, and is robust with respect to segmentation procedure. For experimental and in vivo human data, the results show the CT number can be successfully recovered and anatomical structure visibility can be significantly improved.