A post-processing algorithm for spectral CT material selective images using learned dictionaries. (27th February 2017)

• Record Type: Journal Article
• Authors: Mechlem, Korbinian; Allner, Sebastian; Ehn, Sebastian; Mei, Kai; Braig, Eva; Münzel, Daniela; Pfeiffer, Franz; Noël, Peter B
• Is Part Of: Biomedical physics & engineering express. Volume 3:Number 2(2017)
• Publication Date: 2017-02-27
• Subjects: dictionary learning -- spectral CT -- denoising; Medical physics -- Periodicals; Biophysics -- Periodicals; Biomedical engineering -- Periodicals; Medical sciences -- Periodicals; 610.153
• DOI: 10.1088/2057-1976/aa6045
• Languages: English
• ISSNs: 2057-1976

Abstract

Abstract: In spectral computed tomography (spectral CT), the additional information about the energy dependence of the linear attenuation coefficients can be exploited to produce material selective images. These images have proven to be useful for various applications such as quantitative imaging or clinical diagnosis. However, noise amplification on material decomposed images remains a fundamental problem which limits the utility of basis material images. In this work, we present a new post-processing algorithm for material selective images which is based on dictionary denoising and specifically tailored to take the properties of the basis material images into account. Dictionary denoising is a powerful noise reduction technique which separates image features from noise by modeling small image patches as a sparse linear combination of dictionary atoms. These dictionary atoms are learned from training images prior to the denoising process. We have adapted the dictionary denoising algorithm to make use of the structural correlation as well as the anti-correlated noise which is typically present in material selective images. Dictionary denoising is first applied to the virtual monochromatic image for which the anti-correlated noise maximally cancels out (minimum noise image) in order to identify the structures and edges of the material selective images. In a second step, the basis material images are compiled by finding local linear transformations between the minimum noise image and the basis material images. Numerical simulations as well as an experimental measurement show that our algorithm achieves improved image quality compared to two other post-processing methods, namely conventional dictionary denoising and bilateral filtering. As a post-processing method, it can be combined with image-based as well as projection-based material decomposition techniques. Our algorithm therefore has the potential to improve the usability of basis material images for various tasks such as artifact reduction, quantitative imaging and clinical diagnosis.