Cane beam volume CT image artifacts caused by defective cells in x-ray flat panel imagers and the artifact removal using a wavelet-analysis-based algorithm

The application of x-ray flat panel imagers (FPIs) in cone beam volume CT ( CBVCT) has attracted increasing attention. However, due to a deficient semi conductor array manufacturing process, defective cells unavoidably exist in x-ray FPIs. These defective cells cause their corresponding image pixels i n a projection image to behave abnormally in signal gray level, and result in severe streak and ring artifacts in a CBVCT image reconstructed from the projection images. Since a three-dimensional (3-D) back-projection is invo lved in CBVCT, the formation of the streak and ring artifacts is different from that in the two-dimensional (2-D) fan beam CT. In this paper, a geomet ric analysis of the abnormality propagation in the 3D back-projection is pr esented, and the morphology of the streak and ring artifacts caused by the abnormality propagation is investigated through both computer simulation an d phantom studies. In order to calibrate those artifacts, a 2D wavelet-anal ysis-based statistical approach to correct the abnormal pixels is proposed. The approach consists of three steps: (1) the location-invariant defective cells in an x-ray FPI are recognized by applying 2-D wavelet analysis on f lat-field images, and a comprehensive defective cell template is acquired; (2) based upon the template, the abnormal signal gray level of the projecti on image pixels corresponding to the location-invariant defective cells is replaced with the interpolation of that of their normal neighbor pixels; (3 ) that corresponding to the isolated location-variant defective cells are c orrected using a narrow-windowed median filter. The CBVCT images of a CT lo w-contrast phantom are employed to evaluate this proposed approach, showing that the streak and ring artifacts can be reliably eliminated. Tile novelt y and merit of the approach are the incorporation of the wavelet analysis w hose intrinsic multi-resolution analysis and localizability make the recogn ition algorithm robust under variable: x-ray exposure levels between 30% an d 70% of the dynamic range of an x-ray FPI. (C) 2001 American Association o f Physicists in Medicine.