On intermediate view estimation in computed tomography

In many applications in computed tomography, practical limitations in data acquisition restrict the number of projections (views). The use of the standard convolution backprojection algorithm for reconstruction from an inadequate number of projections results in view aliasing artifacts. One approach to alleviating the effects of such artifacts consists of artificially increasing the number of views, by estimating a set of intermediate views. Two possible methods of estimating the intermediate views are interpolation and reprojection. In this paper, a study of the two is considered. Based on the merits and demerits of the two methods, a combination of the two methods is investigated. Specifically, a reconstruction from the available sinogram augmented by intermediate view reprojections, and the projections interpolated from the original views and the reprojections, provide an additional improvement with respect to view aliasing artifacts. The advantage of computing reprojections over smaller regions of interest is discussed. When the number of available projections is reasonably high but not adequate to produce an artifact-free reconstruction, estimating the intermediate views by interpolation provides an improvement without much additional degradation, at minimal computational cost.

[1]  B A Groh,et al.  Tomotherapeutic portal imaging for radiation treatment verification. , 1998, Physics in medicine and biology.

[2]  Peter M. Joseph,et al.  View sampling requirements in fan beam computed tomography. , 1980 .

[3]  Rangaraj M. Rangayyan,et al.  Geometric Deconvolution: A Meta-Algorithm for Limited View Computed Tomography , 1983, IEEE Transactions on Biomedical Engineering.

[4]  K. Tam,et al.  Tomographical imaging with limited-angle input , 1981 .

[5]  D. Holdsworth,et al.  Use of a C-arm system to generate true three-dimensional computed rotational angiograms: preliminary in vitro and in vivo results. , 1997, AJNR. American journal of neuroradiology.

[6]  K. P. Prasad,et al.  Fast interpolation algorithm using FFT , 1986 .

[7]  Xiaochuan Pan,et al.  Comparison of angular interpolation approaches in few-view tomography using statistical hypothesis testing , 1999, Medical Imaging.

[8]  D W Holdsworth,et al.  Techniques to alleviate the effects of view aliasing artifacts in computed tomography. , 1999, Medical physics.

[9]  Bruce D. Smith Cone-beam tomography: recent advances and a tutorial review , 1990 .

[10]  Avinash C. Kak,et al.  Principles of computerized tomographic imaging , 2001, Classics in applied mathematics.

[11]  L. Rabiner,et al.  A digital signal processing approach to interpolation , 1973 .

[12]  Yves Trousset,et al.  3D Reconstruction of High Contrast Objects Using a Multi-scale Detection / Estimation Scheme , 1990 .

[13]  Rodney A. Brooks,et al.  The use of phantom views to reduce CT streaks due to insufficient angular sampling , 1982 .

[14]  T Sato,et al.  Tomographic image reconstruction from limited projections using iterative revisions in image and transform spaces. , 1981, Applied optics.

[15]  Gabor T. Herman,et al.  Image reconstruction from projections : the fundamentals of computerized tomography , 1980 .

[16]  D. Saint-Felix,et al.  Comparison Of 3-D Tomographic Algorithms For Vascular Reconstruction , 1988, Medical Imaging.

[17]  P. Gilbert Iterative methods for the three-dimensional reconstruction of an object from projections. , 1972, Journal of theoretical biology.

[18]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[19]  Euclid Seeram,et al.  Computed Tomography: Physical Principles, Clinical Applications, and Quality Control , 1994 .

[20]  G. Herman,et al.  Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography. , 1970, Journal of theoretical biology.

[21]  Klaus Mueller,et al.  Anti-Aliased 3D Cone-Beam Reconstruction of Low-Contrast Objects with Algebraic Methods , 1999, IEEE Trans. Medical Imaging.

[22]  Nassir Navab,et al.  Enhanced 3D-reconstruction algorithms for C-Arm based interventional procedures , 2000, IEEE Trans. Medical Imaging.

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

[24]  Klaus Mueller,et al.  Fast Implementation of Algebraic Methods for 3D Reconstruction from Cone-Beam Data , 1999, IEEE Trans. Medical Imaging.

[25]  A P Dhawan,et al.  Algorithms for limited-view computed tomography: an annotated bibliography and a challenge. , 1985, Applied optics.

[26]  D. Parker Optimal short scan convolution reconstruction for fan beam CT , 1982 .

[27]  G. Schwierz,et al.  Sampling and Discretization Problems in X-ray-CT , 1981 .

[28]  Xiaochuan Pan,et al.  Quasi Band-Limited Properties of Radon Transforms and Their Implications for Increasing Angular Sampling Densities , 1998, IEEE Trans. Medical Imaging.

[29]  S. Deans The Radon Transform and Some of Its Applications , 1983 .

[30]  Gene Gindi,et al.  OPTICAL FEATURE EXTRACTION VIA THE RADON TRANSFORM. , 1984 .