Image artifacts and noise reduction algorithm for cone-beam computed tomography with low-signal projections.

This study aims to investigate and test a new image reconstruction algorithm applying to the low-signal projections to generate high quality images by reducing the artifacts and noise in the cone-beam computed tomography (CBCT). For the low-signal and noisy projections, a multiple sampling method is first utilized in projection domain to suppress environmental noise, which guarantees the accuracy of the data for reconstruction, simultaneously. Next, a fuzzy entropy based method with block matching 3D (BM3D) filtering algorithm is employed to improve the image quality to reduce artifacts and noise in image domain. Then, simulation studies on polychromatic spectrum were performed to evaluate the performance of the proposed new algorithm. Study results demonstrated significant improvement in the signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images reconstructed using the new algorithm. SNRs and CNRs of the new images were averagely 40% and 20% higher than those of the previous images reconstructed using the traditional algorithms, respectively. As a result, since the new image reconstruction algorithm effectively reduced the artifacts and noise, and produced images with better contour and grayscale distribution, it has the potential to improve image quality using the original CBCT data with the low and missing signals.

[Beam hardening correction method for X-ray computed tomography based on subsection beam hardening curves].

After researching the forming principle of X-ray beam hardening and analyzing the usual methods of beam hardening correction, a beam hardening correction model was established, in which the independent variable was the projection gray, and so the computing difficulties in beam hardening correction can be reduced. By considering the advantage and disadvantage of fitting beam hardening curve to polynomial, a new expression method of the subsection beam hardening curves based on polynomial was proposed. In the method, the beam hardening data were fitted firstly to a polynomial curve which traverses the coordinate origin, then whether the got polynomial curve surged in the fore-part or back-part of the fitting range was judged based on the polynomial curvature change. If the polynomial fitting curve surged, the power function curve was applied to replace the surging parts of the polynomial curve, and the C1 continuity was ensured at the joints of the segment curves. The experimental results of computed tomography (CT) simulation show that the method is well stable in the beam hardening correction for the ideal CT images and CT images with added noises, and can mostly remove the beam hardening artifact at the same time.