An improved nonlinear diffusion in Laplacian pyramid domain for cone beam CT denoising during image-guided vascular intervention

摘要

Cone-beam computed tomography (CBCT) acquisition during endovascular aneurysm repair is an emergent technology with more and more applications. It may provide 3-D information to achieve guidance of intervention. However, there is growing concern on the overall radiation doses delivered to patients, thus a low dose protocol is called when scanning. But CBCT images with a low dose protocol are degraded, resulting in streak artifacts and decreased contrast-to-noise ratio (CNR). In this paper, a Laplacian pyramid-based nonlinear diffusion is proposed to improve the quality of CBCT images. We first transform the CBCT image into its pyramid domain, then a modified nonlinear diffusion is performed in each level to remove noise across edges while keeping edges as far as possible. The improved diffusion coefficient is a function of the gradient magnitude image; the threshold in the modified diffusion function is estimated using the median absolute deviation (MAD) estimator; the time step is automatically determined by iterative image changes and the iteration is stopped according to mean absolute error between two adjacent diffusions. Finally, we reconstruct the Laplacian pyramid using the processed pyramid images in each level. Results from simulation show that the filtered image from the proposed method has the highest peak signal-noise ratio (81.92), the highest correlation coefficient (99.77) and the lowest mean square error (27.61), compared with the other four methods. In addition, it has highest contrast-to-noise ratio and sharpness in ROIs. Results from real CBCT images show that the proposed method shows better smoothness in homogeneous regions meanwhile keeps bony structures clear. Simulation and patient studies show that the proposed method has a good tradeoff between noise/artifacts suppression and edge preservation.