Artifact Suppressed Nonlinear Diffusion Filtering for Low-Dose CT Image Processing

Computed tomography (CT) images with a low-dose protocol generally have severe mottle noise and streak artifacts. In this paper, we propose a novel diffusion method named “artifact suppressed nonlinear diffusion filtering (ASNDF),” to process low-dose CT (LDCT) images. Different from other diffusion filtering methods, the proposed ASNDF not only includes image gradient as the main cue to construct a diffusion coefficient function, but also incorporates the local variances of image to be diffused and residual image between two adjacent diffusions. In detail, the classical PM diffusion is first performed to get the initial residual image, and then from the second iteration, the LDCT image is processed according to the ASNDF processing. Simulated data, clinical data and rat data are conducted to evaluate the proposed method, and the comparison experiments with other competing methods show that the proposed ASNDF method makes an improvement in artifact suppression and structure preservation, and offers a sound alternative to process LDCT images from most current CT systems.

[1]  Miguel Castro,et al.  Edge-preserving denoising for intra-operative cone beam CT in endovascular aneurysm repair , 2017, Comput. Medical Imaging Graph..

[2]  Miguel Castro,et al.  An improved nonlinear diffusion in Laplacian pyramid domain for cone beam CT denoising during image-guided vascular intervention , 2018, BMC Medical Imaging.

[3]  Liu Xinchun,et al.  Edge-detection based on the local variance in angiographic images , 2000 .

[4]  Chen Yang,et al.  A New Weight for Nonlocal Means Denoising Using Method Noise , 2012, IEEE Signal Processing Letters.

[5]  D. McCauley,et al.  Low-dose CT of the lungs: preliminary observations. , 1990, Radiology.

[6]  Wufan Chen,et al.  Image denoising using modified Perona-Malik model based on directional Laplacian , 2013, Signal Process..

[7]  Cynthia M. McCollough,et al.  Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. , 2009, Medical physics.

[8]  Quan Zhang,et al.  A Novel Fractional-Order Differentiation Model for Low-Dose CT Image Processing , 2016, IEEE Access.

[9]  Wufan Chen,et al.  MTV: modified total variation model for image noise removal , 2011 .

[10]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[11]  M. Kalra,et al.  Strategies for CT radiation dose optimization. , 2004, Radiology.

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

[13]  Christine Toumoulin,et al.  Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means , 2012, Physics in medicine and biology.

[14]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Ryan D Lee,et al.  Common Image Artifacts in Cone Beam CT , 2011 .

[16]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[17]  L. Xing,et al.  Iterative image reconstruction for CBCT using edge-preserving prior. , 2008, Medical physics.

[18]  Tingting Zhao,et al.  Ultra‐low‐dose CT image denoising using modified BM3D scheme tailored to data statistics , 2018, Medical physics.

[19]  Du-Ming Tsai,et al.  An improved anisotropic diffusion model for detail- and edge-preserving smoothing , 2010, Pattern Recognit. Lett..

[20]  Javad Alirezaie,et al.  Low-dose computed tomography image denoising based on joint wavelet and sparse representation , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Quan Zhang,et al.  Noise Reduction for Low-dose CT Sinogram Based on Fuzzy Entropy: Noise Reduction for Low-dose CT Sinogram Based on Fuzzy Entropy , 2014 .

[22]  Armando Manduca,et al.  Adaptive nonlocal means filtering based on local noise level for CT denoising. , 2013, Medical physics.

[23]  Piotr J. Slomka,et al.  Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm , 2013, Medical Imaging.

[24]  Jing Wang,et al.  Multiscale Penalized Weighted Least-Squares Sinogram Restoration for Low-Dose X-Ray Computed Tomography , 2008, IEEE Transactions on Biomedical Engineering.

[25]  Zhiguo Gui,et al.  Noise reduction for low-dose X-ray computed tomography with fuzzy filter , 2012 .

[26]  Michael Elad,et al.  Improving K-SVD denoising by post-processing its method-noise , 2013, 2013 IEEE International Conference on Image Processing.

[27]  D. Brenner,et al.  Estimated risks of radiation-induced fatal cancer from pediatric CT. , 2001, AJR. American journal of roentgenology.

[28]  Jing Wang,et al.  Penalized weighted least-squares approach for low-dose x-ray computed tomography , 2006, SPIE Medical Imaging.

[29]  David A Jaffray,et al.  Patient dose from kilovoltage cone beam computed tomography imaging in radiation therapy. , 2006, Medical physics.

[30]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Hongbing Lu,et al.  Nonlinear sinogram smoothing for low-dose X-ray CT , 2004 .

[32]  Emil Y. Sidky,et al.  Analysis of discrete-to-discrete imaging models for iterative tomographic image reconstruction and compressive sensing , 2011 .

[33]  Shuo Li,et al.  Quantification of Full Left Ventricular Metrics via Deep Regression Learning With Contour-Guidance , 2019, IEEE Access.

[34]  Jing Wang,et al.  Iterative reconstruction for CT perfusion with a prior-image induced hybrid nonlocal means regularization: Phantom studies. , 2016, Medical physics.

[35]  E. Sidky,et al.  Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.

[36]  Rui Bernardes,et al.  Improved Adaptive Complex Diffusion Despeckling Filter References and Links , 2022 .

[37]  Alexander A. Zamyatin,et al.  Multi-resolution diffusion tensor filter for preserving noise power spectrum in low-dose CT imaging , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[38]  Jianhua Ma,et al.  Nonlocal Prior Bayesian Tomographic Reconstruction , 2008, Journal of Mathematical Imaging and Vision.

[39]  Max A. Viergever,et al.  Hybrid diffusion compared with existing diffusion schemes on simulated low dose CT scans , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[40]  Jing Wang,et al.  Deriving adaptive MRF coefficients from previous normal-dose CT scan for low-dose image reconstruction via penalized weighted least-squares minimization. , 2014, Medical physics.

[41]  Wufan Chen,et al.  Improving low-dose X-ray CT images by Weighted Intensity Averaging over Large-scale Neighborhoods , 2010, 2010 International Conference of Medical Image Analysis and Clinical Application.

[42]  Seok Jeong Lee,et al.  Image quality assessment of ultra low-dose chest CT using sinogram-affirmed iterative reconstruction , 2014, European Radiology.

[43]  Tsukasa Sasaki,et al.  Realization of reliable cerebral-blood-flow maps from low-dose CT perfusion images by statistical noise reduction using nonlinear diffusion filtering , 2008, Radiological physics and technology.

[44]  Max W. K. Law,et al.  Weighted Local Variance-Based Edge Detection and Its Application to Vascular Segmentation in Magnetic Resonance Angiography , 2007, IEEE Transactions on Medical Imaging.