Tensor decomposition and non-local means based spectral CT image denoising.

BACKGROUNDAs one type of the state-of-the-art detectors, photon counting detectors are used in spectral computed tomography (CT) to classify the received photons into several energy channels and generate multichannel projections simultaneously. However, FBP reconstructed images contain severe noise due to the low photon counts in each energy channel.OBJECTIVEA spectral CT image denoising method based on tensor-decomposition and non-local means (TDNLM) is proposed.METHODSIn a CT image, it is widely accepted that there exists self-similarity over the spatial domain. In addition, because a multichannel CT image is obtained from the same object at different energies, images among different channels are highly correlated. Motivated by these two characteristics of the spectral CT images, tensor decomposition and non-local means are employed to recover fine structures in spectral CT images. Moreover, images in all energy channels are added together to form a high signal-to-noise ratio image, which is applied to encourage the signal preservation of the TDNLM. The combination of TD, NLM and the guidance of a high-quality image enhances the low-dose spectral CT, and a parameter selection strategy is designed to achieve the optimal image quality.RESULTSThe effectiveness of the developed algorithm is validated on both numerical simulations and realistic preclinical applications. The root mean square error (RMSE) and the structural similarity (SSIM) are used to quantitatively assess the image quality. The proposed method successfully restored high-quality images (average RMSE=0.0217 cm - 1 and SSIM=0.987) from noisy spectral CT images (average RMSE=0.225 cm - 1 and SSIM=0.633). In addition, RMSE of each decomposed material component is also remarkably reduced. Compared to the state-of-the-art iterative spectral CT reconstruction algorithms, the proposed method achieves comparable performance with dramatically reduced computational cost, resulting in a speedup of >50.CONCLUSIONSThe outstanding denoising performance, the high computational efficiency and the adaptive parameter selection strategy make the proposed method practical for spectral CT applications.

[1]  C. McCollough,et al.  Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications. , 2015, Radiology.

[2]  M. Macari,et al.  Dual energy CT: preliminary observations and potential clinical applications in the abdomen , 2008, European Radiology.

[3]  Ge Wang,et al.  Combination of current-integrating/photon-counting detector modules for spectral CT , 2013, Physics in medicine and biology.

[4]  Ge Wang,et al.  Spectral CT Reconstruction With Image Sparsity and Spectral Mean , 2016, IEEE Transactions on Computational Imaging.

[5]  Jing Wang,et al.  Statistical image reconstruction for low-dose CT using nonlocal means-based regularization , 2014, Comput. Medical Imaging Graph..

[6]  Xuanqin Mou,et al.  Dictionary Learning and Low Rank based Multi-energy CT Reconstruction , 2014 .

[7]  Qian Wang,et al.  Low-dose spectral CT reconstruction using image gradient ℓ 0-norm and tensor dictionary. , 2018, Applied mathematical modelling.

[8]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Anthony P. H. Butler,et al.  Image Reconstruction for Hybrid True-Color Micro-CT , 2012, IEEE Transactions on Biomedical Engineering.

[10]  Xun Jia,et al.  Four-dimensional cone beam CT reconstruction and enhancement using a temporal nonlocal means method. , 2012, Medical physics.

[11]  M. Drangova,et al.  Implementation of dual- and triple-energy cone-beam micro-CT for postreconstruction material decomposition. , 2008, Medical physics.

[12]  Eric L. Miller,et al.  Tensor-Based Formulation and Nuclear Norm Regularization for Multienergy Computed Tomography , 2013, IEEE Transactions on Image Processing.

[13]  Lei Zhu,et al.  Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization. , 2014, Medical physics.

[14]  K. Taguchi,et al.  Vision 20/20: Single photon counting x-ray detectors in medical imaging. , 2013, Medical physics.

[15]  Shih-Ming Yang,et al.  A fast method for image noise estimation using Laplacian operator and adaptive edge detection , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[16]  B. Münch,et al.  Stripe and ring artifact removal with combined wavelet--Fourier filtering. , 2009, Optics express.

[17]  Steve B. Jiang,et al.  Low-dose 4DCT reconstruction via temporal nonlocal means. , 2010, Medical physics.

[18]  Ge Wang,et al.  Multi-energy CT reconstruction based on Low Rank and Sparsity with the Split-Bregman Method (MLRSS) , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[19]  S. Osher,et al.  Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM) , 2011, Inverse problems.

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

[21]  Jin Liu,et al.  Discriminative feature representation: an effective postprocessing solution to low dose CT imaging , 2017, Physics in medicine and biology.

[22]  Patrick J La Rivière,et al.  Joint reconstruction of multi-channel, spectral CT data via constrained total nuclear variation minimization , 2014, Physics in medicine and biology.

[23]  Yu Zou,et al.  Analysis of fast kV-switching in dual energy CT using a pre-reconstruction decomposition technique , 2008, SPIE Medical Imaging.

[24]  Jeffrey A. Fessler,et al.  Iterative image reconstruction for dual-energy X-ray CT using regularized material sinogram estimates , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[25]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[26]  Eric C Frey,et al.  Development of a 4-D digital mouse phantom for molecular imaging research. , 2004, Molecular imaging and biology : MIB : the official publication of the Academy of Molecular Imaging.

[27]  Jing Huang,et al.  Spectral CT Image Restoration via an Average Image-Induced Nonlocal Means Filter , 2016, IEEE Transactions on Biomedical Engineering.

[28]  L. Xing,et al.  Using edge-preserving algorithm with non-local mean for significantly improved image-domain material decomposition in dual-energy CT , 2016, Physics in medicine and biology.

[29]  Hengyong Yu,et al.  Comparison studies of different regularizers for spectral computed tomography , 2016, Optical Engineering + Applications.

[30]  Zhiqiang Chen,et al.  A tensor PRISM algorithm for multi-energy CT reconstruction and comparative studies. , 2014, Journal of X-ray science and technology.

[31]  Qi Wang,et al.  3-D nonlocal means filter with noise estimation for hyperspectral imagery denoising , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[32]  Jong Chul Ye,et al.  Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty , 2015, IEEE Transactions on Medical Imaging.

[33]  Pierrick Coupé,et al.  An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images , 2008, IEEE Transactions on Medical Imaging.

[34]  Zhengrong Liang,et al.  Assessment of prior image induced nonlocal means regularization for low‐dose CT reconstruction: Change in anatomy , 2017, Medical physics.

[35]  Hengyong Yu,et al.  Tensor decomposition and nonlocal means based spectral CT reconstruction , 2016, Optical Engineering + Applications.

[36]  A. J. Bell,et al.  Bio-medical X-ray imaging with spectroscopic pixel detectors , 2008 .

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

[38]  Steve B. Jiang,et al.  A comprehensive study on the relationship between the image quality and imaging dose in low-dose cone beam CT , 2011, Physics in medicine and biology.

[39]  Mark A Anastasio,et al.  Sparsity-regularized image reconstruction of decomposed K-edge data in spectral CT , 2014, Physics in Medicine and Biology.