Image denoising based on multiscale singularity detection for cone beam CT breast imaging

It was recently reported that the real-time flat panel detector-based cone-beam computed tomography (CBCT) breast imaging can help improve the delectability of small breast tumors with an X-ray dose comparable to that of the conventional mammography. In this paper, an efficient denoising algorithm is proposed to further reduce the X-ray exposure level required by a CBCT scan to acquire acceptable image quality. The proposed wavelet-based denoising algorithm possesses three significant characteristics: 1) wavelet coefficients at each scale are classified into two categories: irregular coefficients, and edge-related and regular coefficients; 2) noise in irregular coefficients is reduced as much as possible without producing artifacts to the denoised images; and 3) for the edge-related and regular coefficients, if they are at the first decomposition level, they are further denoised, otherwise, no modifications are made to them so as to obtain good visual quality for diagnosis. By applying the proposed denoising algorithm to the filtered projection images, the X-ray exposure level necessary for the CBCT scan can be reduced by up to 60% while obtaining clinically acceptable image quality. This denoising result indicates that in the clinical application of CBCT breast imaging, the patient radiation dose can be significantly reduced.