A Scatter Calibration Technique for Dynamic Brain Imaging in High Resolution PET

Abstract

We describe a scatter calibration technique which improves the quantitative accuracy of the positron emission tomography data in specific scanning conditions: i.e., scans with high random fraction (RF) and/or low number of counts. Such a situation is often encountered in dynamic imaging on scanners with a large number of lines-of-response (LOR) such as the high resolution research tomograph (HRRT). In this paper, we first describe how high RFs and low number of counts affect the scatter scaling process. We then demonstrate experimentally, with phantom studies, the bias in the scatter estimate introduced by the commonly used tail-fitting technique employed in the single scatter simulation (SSS) method. A significant bias in scatter fraction (SF) was found for frames which contain a RF higher than 50% and/or with a number of counts less than 20 M. Finally, we present a new scatter scaling technique which compensates this bias. The scatter calibration technique is based on using the scatter estimate obtained from a reference frame, in which the bias due to high RFs and low number of counts is minimized, to calibrate the scatter in each dynamic frame. The calibration also separately accounts for the change in SF due to the pulse pile-up effect. A much more consistent and accurate SF value was assigned to each segment of the scatter sinogram thus leading to a more quantitative reconstructed image with a better axial uniformity after the scatter calibration. The new calibration technique was tested with phantom, monkey, and human data and was found to significantly improve the quantitative aspect of the early frames: such improvement is expected to positively affect the feasibility of rather novel image analysis methods, such as determination of image derived input function.