A post-processing algorithm for spectral CT material selective images using learned dictionaries

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.

[1]  L. MacDonald,et al.  Quantitative material characterization from multi-energy photon counting CT. , 2013, Medical physics.

[2]  A. Macovski,et al.  Energy-selective reconstructions in X-ray computerised tomography , 1976, Physics in medicine and biology.

[3]  W. Kalender,et al.  An algorithm for noise suppression in dual energy CT material density images. , 1988, IEEE transactions on medical imaging.

[4]  Mulugeta Gebregziabher,et al.  Dual-energy CT of the heart for diagnosing coronary artery stenosis and myocardial ischemia-initial experience , 2008, European Radiology.

[5]  A Fenster,et al.  An accurate method for direct dual-energy calibration and decomposition. , 1990, Medical physics.

[6]  J. Leipsic,et al.  Reduced iodine load at CT pulmonary angiography with dual-energy monochromatic imaging: comparison with standard CT pulmonary angiography--a prospective randomized trial. , 2012, Radiology.

[7]  Ge Wang,et al.  Few-view image reconstruction with dual dictionaries , 2012, Physics in medicine and biology.

[8]  K. Stierstorfer,et al.  Density and atomic number measurements with spectral x-ray attenuation method , 2003 .

[9]  Yoram Bresler,et al.  MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning , 2011, IEEE Transactions on Medical Imaging.

[10]  Marc Kachelrieß,et al.  Image-based dual energy CT using optimized precorrection functions: A practical new approach of material decomposition in image domain. , 2009, Medical physics.

[11]  Rui Liu,et al.  An adaptive reconstruction algorithm for spectral CT regularized by a reference image , 2016, Physics in medicine and biology.

[12]  Lei Zhang,et al.  Low-Dose X-ray CT Reconstruction via Dictionary Learning , 2012, IEEE Transactions on Medical Imaging.

[13]  L Zhu,et al.  TU-F-18A-02: Iterative Image-Domain Decomposition for Dual-Energy CT. , 2014, Medical physics.

[14]  Michael Elad,et al.  Image Sequence Denoising via Sparse and Redundant Representations , 2009, IEEE Transactions on Image Processing.

[15]  K. Deng,et al.  Clinical evaluation of dual-energy bone removal in CT angiography of the head and neck: comparison with conventional bone-subtraction CT angiography. , 2009, Clinical radiology.

[16]  Zhicong Yu,et al.  Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography , 2016, Physics in medicine and biology.

[17]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[18]  Lei Zhu,et al.  Dual energy CT with one full scan and a second sparse-view scan using structure preserving iterative reconstruction (SPIR). , 2016 .

[19]  Jin Peng,et al.  Liver virtual non-enhanced CT with dual-source, dual-energy CT: a preliminary study , 2010, European Radiology.

[20]  Ernst J. Rummeny,et al.  Targeted dual-energy single-source CT for characterisation of urinary calculi: experimental and clinical experience , 2011, European Radiology.

[21]  Thomas Koehler,et al.  Simultaneous de-noising in phase contrast tomography , 2012 .

[22]  Timo Berkus,et al.  Empirical dual energy calibration (EDEC) for cone-beam computed tomography. , 2007 .

[23]  James T Dobbins,et al.  Quantitative , 2020, Psychology through Critical Auto-Ethnography.

[24]  P. Joseph,et al.  Noise considerations in dual energy CT scanning. , 1979, Medical physics.

[25]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[26]  Xuanqin Mou,et al.  Tensor-Based Dictionary Learning for Spectral CT Reconstruction , 2017, IEEE Transactions on Medical Imaging.

[27]  C. McCollough,et al.  Virtual monochromatic imaging in dual-source dual-energy CT: radiation dose and image quality. , 2011, Medical physics.

[28]  Lei Zhu,et al.  Iterative image-domain decomposition for dual-energy CT. , 2014, Medical physics.

[29]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[30]  Lei Zhu,et al.  Noise Suppression for Dual-Energy CT Through Entropy Minimization , 2015, IEEE Transactions on Medical Imaging.

[31]  Kai Mei,et al.  Dictionary-based image denoising for dual energy computed tomography , 2016, SPIE Medical Imaging.

[32]  R. Brooks.  A Quantitative Theory of the Hounsfield Unit and Its Application to Dual Energy Scanning , 1977, Journal of computer assisted tomography.

[33]  J Herzen,et al.  Basis material decomposition in spectral CT using a semi-empirical, polychromatic adaption of the Beer-Lambert model. , 2017, Physics in medicine and biology.

[34]  R. Alvarez.  Estimator for photon counting energy selective x-ray imaging with multibin pulse height analysis. , 2011, Medical physics.

[35]  Huazhong Shu,et al.  Artifact Suppressed Dictionary Learning for Low-Dose CT Image Processing , 2014, IEEE Transactions on Medical Imaging.