Wavelet-based multiscale anisotropic diffusion in cone-beam CT breast imaging denoising for x-ray dose reduction

Abstract

The real-time flat panel detector-based cone beam CT breast imaging (FPD-CBCTBI) has attracted increasing attention for its merits of early detection of small breast cancerous tumors, 3-D diagnosis, and treatment planning with glandular dose levels not exceeding those of conventional film-screen mammography. In this research, our motivation is to further reduce the x-ray exposure level for the cone beam CT scan while retaining acceptable image quality for medical diagnosis by applying efficient denoising techniques. In this paper, the wavelet-based multiscale anisotropic diffusion algorithm is applied for cone beam CT breast imaging denoising. Experimental results demonstrate that the denoising algorithm is very efficient for cone bean CT breast imaging for noise reduction and edge preservation. The denoising results indicate that in clinical applications of the cone beam CT breast imaging, the patient’s radiation dose can be reduced by up to 60% while obtaining acceptable image quality for diagnosis.