A z gain nonuniformity correction for multislice volumetric CT scanners

This paper presents a calibration and correction method for detector cell gain variations. A key functionality of current CT scanners is to offer variable slice thickness to the user. To provide this capability in multislice volumetric scanners, while minimizing costs, it is necessary to combine the signals of several detector cells in z, when the desired slice thickness is larger than the minimum provided by a single cell. These combined signals are then pre-amplified, digitized, and transmitted to the system for further processing. The process of combining the output of several detector cells with nonuniform gains can introduce numerical errors when the impinging x-ray signal presents a variation along z over the range of combined cells. These numerical errors, which by nature are scan dependent, can lead to artifacts in the reconstructed images, particularly when the numerical errors vary from channel-to-channel (as the filtered-backprojection filter includes a high-pass filtering along the channel direction, within a given slice). A projection data correction algorithm has been developed to subtract the associated numerical errors. It relies on the ability of calibrating the individual cell gains. For effectiveness and data flow reasons, the algorithm works on a single slice basis, without slice-to-slice exchange of information. An initial error vector is calculated by applying a high-pass filter to the projection data. The essence of the algorithm is to correlate that initial error vector, with a calibration vector obtained by applying the same high-pass filter to various z combinations of the cell gains (each combination representing a basis function for a z expansion). The solution of the least-square problem, obtained via singular value decomposition, gives the coefficients of a polynomial expansion of the signal z slope and curvature. From this information, and given the cell gains, the final error vector is calculated and subtracted from the projection data.