Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary

Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of image gradient L0-norm, which is named as L0TDL. The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images. On the other hand, by introducing the L0-norm constraint in gradient image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The alternative direction minimization method (ADMM) is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed L0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.

[1]  Bin Yan,et al.  X-ray CT image reconstruction from few-views via total generalized p-variation minimization , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  Ioannis Sechopoulos,et al.  Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features , 2014, Medical Imaging.

[3]  Jan-Erik Scholtz,et al.  Advanced image-based virtual monoenergetic dual-energy CT angiography of the abdomen: optimization of kiloelectron volt settings to improve image contrast , 2016, European Radiology.

[4]  E. Hall,et al.  Lessons we have learned from our children: cancer risks from diagnostic radiology , 2002, Pediatric Radiology.

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

[6]  Yi Yang,et al.  Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[9]  Hairong Zheng,et al.  Joint Reconstruction of Multi-contrast Images and Multi-channel Coil Sensitivities , 2017 .

[10]  M. Jiang,et al.  Ordered-subset simultaneous algebraic reconstruction techniques (OS-SART) , 2004 .

[11]  Dinesh Rajan,et al.  Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution , 2013, IEEE Transactions on Image Processing.

[12]  Xuanqin Mou,et al.  Tensor-based dictionary learning for dynamic tomographic reconstruction , 2015, Physics in medicine and biology.

[13]  Byoung Wook Choi,et al.  Dual-energy CT-based iodine quantification for differentiating pulmonary artery sarcoma from pulmonary thromboembolism: a pilot study , 2016, European Radiology.

[14]  Chengxiang Wang,et al.  Edge-preserving reconstruction from sparse projections of limited-angle computed tomography using ℓ0-regularized gradient prior. , 2017, The Review of scientific instruments.

[15]  G Wang,et al.  MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT. , 2016, Medical physics.

[16]  Junfeng Yang,et al.  ALTERNATING DIRECTION ALGORITHMS FOR TOTAL VARIATION DECONVOLUTION IN IMAGE RECONSTRUCTION , 2009 .

[17]  M. Reiser,et al.  Material differentiation by dual energy CT: initial experience , 2007, European Radiology.

[18]  Bernard Ghanem,et al.  ℓ0TV: A new method for image restoration in the presence of impulse noise , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Bo Zhao,et al.  Tight-frame based iterative image reconstruction for spectral breast CT. , 2013, Medical physics.

[20]  Li Zeng,et al.  ℓ 0 Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography , 2015, PloS one.

[21]  Hengyong Yu,et al.  Swinging multi-source industrial CT systems for aperiodic dynamic imaging. , 2017, Optics express.

[22]  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.

[23]  Jeffrey A. Fessler,et al.  Statistical image reconstruction for polyenergetic X-ray computed tomography , 2002, IEEE Transactions on Medical Imaging.

[24]  Jie Tang,et al.  Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. , 2008, Medical physics.

[25]  Syed Zubair,et al.  Tensor dictionary learning with sparse TUCKER decomposition , 2013, 2013 18th International Conference on Digital Signal Processing (DSP).

[26]  U. Bick,et al.  Development of low-dose photon-counting contrast-enhanced tomosynthesis with spectral imaging. , 2011, Radiology.

[27]  Jian Yang,et al.  A Survey of Dictionary Learning Algorithms for Face Recognition , 2017, IEEE Access.

[28]  Wenxiang Cong,et al.  Material decomposition with dual energy CT , 2015, 2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC).

[29]  XuYi,et al.  Image smoothing via L0 gradient minimization , 2011 .

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

[31]  Jun Zhao,et al.  United Iterative Reconstruction for Spectral Computed Tomography , 2015, IEEE Transactions on Medical Imaging.

[32]  Hengyong Yu,et al.  BPF-type region-of-interest reconstruction for parallel translational computed tomography. , 2017, Journal of X-ray science and technology.

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

[34]  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.

[35]  Bo Wang,et al.  A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules , 2015, BMC Medical Imaging.

[36]  Yan Huang,et al.  Dynamic Texture Recognition via Orthogonal Tensor Dictionary Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[37]  Il-Min Kim,et al.  ℓ0-Norm Sparse Hyperspectral Unmixing Using Arctan Smoothing , 2016, Remote. Sens..

[38]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

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

[40]  M. Uder,et al.  Low-Dose CT of the Paranasal Sinuses: Minimizing X-Ray Exposure with Spectral Shaping , 2016, European Radiology.

[41]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

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

[43]  R Aamir,et al.  MARS spectral molecular imaging of lamb tissue: data collection and image analysis , 2013, 1311.4528.

[44]  Wenxiang Cong,et al.  Optimization of K-edge imaging with spectral CT. , 2012, Medical physics.

[45]  Xiangchu Feng,et al.  Variational and PCA based natural image segmentation , 2013, Pattern Recognit..

[46]  Dong Liang,et al.  Augmented Lagrangian-Based Sparse Representation Method with Dictionary Updating for Image Deblurring , 2013, SIAM J. Imaging Sci..

[47]  Jeffrey A. Fessler,et al.  Multi-Material Decomposition Using Statistical Image Reconstruction for Spectral CT , 2014, IEEE Transactions on Medical Imaging.

[48]  Jianbo Gao,et al.  Automatic spectral imaging protocol selection and iterative reconstruction in abdominal CT with reduced contrast agent dose: initial experience , 2016, European Radiology.

[49]  Yen-Wei Chen,et al.  K-CPD: Learning of overcomplete dictionaries for tensor sparse coding , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

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

[51]  Hengyong Yu,et al.  Locally linear constraint based optimization model for material decomposition , 2017, Physics in medicine and biology.

[52]  João Marcos Travassos Romano,et al.  Sparse blind deconvolution based on scale invariant smoothed ℓ0-norm , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).