SNR-weighted Sinogram Smoothing with Improved Noise-resolution Properties for Low-dose X-ray Computed Tomography

To treat the noise in low-dose x-ray CT projection data more accurately, analysis of the noise properties of the data and development of a corresponding efficient noise treatment method are two major problems to be addressed. In order to obtain an accurate and realistic model to describe the x-ray CT system, we acquired thousands of repeated measurements on different phantoms at several fixed scan angles by a GE high-speed multi-slice spiral CT scanner. The collected data were calibrated and log-transformed by the sophisticated system software, which converts the detected photon energy into sinogram data that satisfies the Radon transform. From the analysis of these experimental data, a nonlinear relation between mean and variance for each datum of the sinogram was obtained. In this paper, we integrated this nonlinear relation into a penalized likelihood statistical framework for a SNR (signal-to-noise ratio) adaptive smoothing of noise in the sinogram. After the proposed preprocessing, the sinograms were reconstructed with unapodized FBP (filtered backprojection) method. The resulted images were evaluated quantitatively, in terms of noise uniformity and noise-resolution tradeoff, with comparison to other noise smoothing methods such as Hanning filter and Butterworth filter at different cutoff frequencies. Significant improvement on noise and resolution tradeoff and noise property was demonstrated.