A limited-angle CT reconstruction method based on anisotropic TV minimization

This paper presents a compressed sensing (CS)-inspired reconstruction method for limited-angle computed tomography (CT). Currently, CS-inspired CT reconstructions are often performed by minimizing the total variation (TV) of a CT image subject to data consistency. A key to obtaining high image quality is to optimize the balance between TV-based smoothing and data fidelity. In the case of the limited-angle CT problem, the strength of data consistency is angularly varying. For example, given a parallel beam of x-rays, information extracted in the Fourier domain is mostly orthogonal to the direction of x-rays, while little is probed otherwise. However, the TV minimization process is isotropic, suggesting that it is unfit for limited-angle CT. Here we introduce an anisotropic TV minimization method to address this challenge. The advantage of our approach is demonstrated in numerical simulation with both phantom and real CT images, relative to the TV-based reconstruction.

[1]  Li Zhang,et al.  An exact reconstruction algorithm in variable pitch helical cone-beam CT when PI-line exists , 2006 .

[2]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[3]  E. T. Quinto.  Tomographic reconstructions from incomplete data-numerical inversion of the exterior Radon transform , 1988 .

[4]  H. Tuy.  AN INVERSION FORMULA FOR CONE-BEAM RECONSTRUCTION* , 1983 .

[5]  Guang-Hong Chen,et al.  Temporal resolution improvement using PICCS in MDCT cardiac imaging. , 2009, Medical physics.

[6]  T. Rashid.  Discussions and Conclusion , 2014 .

[7]  Emmanuel J. Candès,et al.  Signal recovery from random projections , 2005, IS&T/SPIE Electronic Imaging.

[8]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[9]  Jie Tang,et al.  Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms , 2009, Physics in medicine and biology.

[10]  Li Zhang,et al.  Low-dose multiple-information retrieval algorithm for X-ray grating-based imaging , 2011 .

[11]  Li Zhang,et al.  Volumetric imaging from a multisegment straight-line trajectory and a practical reconstruction algorithm , 2007 .

[12]  Xiaochuan Pan,et al.  Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. , 2009, Medical physics.

[13]  Zhiqiang Chen,et al.  Anisotropic total variation for limited-angle CT reconstruction , 2010, IEEE Nuclear Science Symposuim & Medical Imaging Conference.

[14]  R. Gerchberg.  Super-resolution through Error Energy Reduction , 1974 .

[15]  Xuanqin Mou,et al.  Statistical Interior Tomography , 2010, IEEE Transactions on Medical Imaging.

[16]  Liang Li,et al.  An alternative derivation and description of Smith's data sufficiency condition for exact cone-beam reconstruction , 2008 .

[17]  Albert Macovski,et al.  Iterative Reconstruction-Reprojection: An Algorithm for Limited Data Cardiac-Computed Tomography , 1982, IEEE Transactions on Biomedical Engineering.

[18]  Hengyong Yu,et al.  Compressed sensing based interior tomography , 2009, Physics in medicine and biology.

[19]  Yuxiang Xing,et al.  Recent Advance in Exact ROI/VOI Image Reconstruction , 2010 .

[20]  Yuxiang Xing,et al.  Motion-compensated reconstruction method based on rigid motion model with multi-object , 2010 .

[21]  Paola Coan,et al.  X-ray phase-contrast imaging: from pre-clinical applications towards clinics , 2013, Physics in medicine and biology.

[22]  Eric Todd Quinto,et al.  Exterior and limited-angle tomography in non-destructive evaluation , 1998 .

[23]  Hengyong Yu,et al.  A soft-threshold filtering approach for reconstruction from a limited number of projections , 2010, Physics in medicine and biology.

[24]  M. Stamos,et al.  Enhanced imaging , 2015, Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland.

[25]  Jie Tang,et al.  Temporal resolution improvement using PICCS in MDCT cardiac imaging. , 2009 .

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

[27]  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.

[28]  Xiaochuan Pan,et al.  Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT , 2010, Physics in medicine and biology.

[29]  M. Kachelriess,et al.  Improved total variation-based CT image reconstruction applied to clinical data , 2011, Physics in medicine and biology.