A new Mumford-Shah total variation minimization based model for sparse-view x-ray computed tomography image reconstruction

Total variation (TV) minimization for the sparse-view x-ray computer tomography (CT) reconstruction has been widely explored to reduce radiation dose. However, due to the piecewise constant assumption for the TV model, the reconstructed images often suffer from over-smoothness on the image edges. To mitigate this drawback of TV minimization, we present a Mumford-Shah total variation (MSTV) minimization algorithm in this paper. The presented MSTV model is derived by integrating TV minimization and Mumford-Shah segmentation. Subsequently, a penalized weighted least-squares (PWLS) scheme with MSTV is developed for the sparse-view CT reconstruction. For simplicity, the proposed algorithm is named as 'PWLS-MSTV.' To evaluate the performance of the present PWLS-MSTV algorithm, both qualitative and quantitative studies were conducted by using a digital XCAT phantom and a physical phantom. Experimental results show that the present PWLS-MSTV algorithm has noticeable gains over the existing algorithms in terms of noise reduction, contrast-to-ratio measure and edge-preservation.

[1]  C. McCollough,et al.  Radiation dose reduction in computed tomography: techniques and future perspective. , 2009, Imaging in medicine.

[2]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[3]  Gaohang Yu,et al.  Sparse-view x-ray CT reconstruction via total generalized variation regularization , 2014, Physics in medicine and biology.

[4]  A. Einstein,et al.  Estimating risk of cancer associated with radiation exposure from 64-slice computed tomography coronary angiography. , 2007, JAMA.

[5]  Raymond H. Chan,et al.  A Two-Stage Image Segmentation Method Using a Convex Variant of the Mumford-Shah Model and Thresholding , 2013, SIAM J. Imaging Sci..

[6]  Wufan Chen,et al.  Variance analysis of x-ray CT sinograms in the presence of electronic noise background. , 2012, Medical physics.

[7]  Hiroyuki Kudo,et al.  Image reconstruction for sparse-view CT and interior CT-introduction to compressed sensing and differentiated backprojection. , 2013, Quantitative imaging in medicine and surgery.

[8]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[9]  Jianhua Ma,et al.  Total Variation-Stokes Strategy for Sparse-View X-ray CT Image Reconstruction , 2014, IEEE Transactions on Medical Imaging.

[10]  Yan Li,et al.  A new image segmentation model with local statistical characters based on variance minimization , 2015 .

[11]  Jayant Shah,et al.  Free-discontinuity problems via functionals involving the L1-norm of the gradient and their approximations , 1999 .

[12]  Zhengrong Liang,et al.  Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior , 2012, Physics in medicine and biology.

[13]  Bo Chen,et al.  Noisy image segmentation based on wavelet transform and active contour model , 2011 .

[14]  Wensheng Chen,et al.  A novel adaptive partial differential equation model for image segmentation , 2014 .

[15]  Yang Gao,et al.  Low-dose X-ray Computed Tomography Image Reconstruction with a Combined Low-mas and Sparse-view Protocol References and Links , 2022 .

[16]  W. Segars,et al.  4D XCAT phantom for multimodality imaging research. , 2010, Medical physics.

[17]  Michael K. Ng,et al.  Variational Fuzzy Mumford--Shah Model for Image Segmentation , 2010, SIAM J. Appl. Math..

[18]  Nahum Kiryati,et al.  Semi-blind image restoration via Mumford-Shah regularization , 2006, IEEE Transactions on Image Processing.

[19]  Yunmei Chen,et al.  A novel method for 4D cone-beam computer-tomography reconstruction , 2015, Medical Imaging.

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

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

[22]  Zhengrong Liang,et al.  Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction , 2012, Physics in medicine and biology.

[23]  Jianhua Ma,et al.  Variance estimation of x-ray CT sinogram in radon domain , 2012, Medical Imaging.

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

[25]  Jayant Shah,et al.  A common framework for curve evolution, segmentation and anisotropic diffusion , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  C. McCollough,et al.  CT dose reduction and dose management tools: overview of available options. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.

[27]  D. Brenner,et al.  Computed tomography--an increasing source of radiation exposure. , 2007, The New England journal of medicine.

[28]  E. Sidky,et al.  Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT , 2009, 0904.4495.