Error bounds for iterative reprojection methods in computerized tomography

Trussel, H.J.; Orun-Ozturk, H.; Civanlar, M.R.

doi:10.1109/icassp.1987.1169768

Cited by 15 publications

(1 citation statement)

References 9 publications

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“…The projection g(r,0) can be devided into two parts: g1(r,0) and g2(r,0). g1(r,0) is formulated by measurable object data, g2(r ,0) is formulated by missing or distorting object data: g(r,0)=g1(r,0)+g2(r,0) (2) So the transform T( ) also can be devided into two parts: T1( ) and T2( ) which produce g1(r,0) and g (r ,0) respectively.…”

Section: The Iterative Reconstruction Algorithmmentioning

confidence: 99%

<title>Computer tomographic (CT) image reconstruction from limited information</title>

Wang

1995

SPIE Proceedings

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A new reconstruction algorithm is introduced to obtain high quality computerized tomography image from limited information.There are many practical cases that only limited projection data can be collected due to the physical constraints of hardware system and the object structure. In these cases , conventional reconstruction algorithms which require complete projections are often unacceptable because it causes severe streak artifacts and distortions. So image reconstruction from limited information is very important and many authors have contributed to it. But the methods presented are often time consuming , less efficient and some algorithm's convergency can not be guaranteed. For iterative algorithms , it is more important to guarantee the convergency and accelcrate the convergency speed.In this paper , an CT image reconstruction method which exploits the Helgason -Ludwig consistency condition is designed to consider obtainable information content about object and reconstruction error in the situation of finite number of projection. Based on the consideration , we developed a new iterative algorithm which can reconstruct high quality artifactiess CT image from limited information , and we design a new histOgram constraint obtained from apriori knowledge to guarantee the convergency and to acquire high convergency speed. The results of simulated tests show the great advantages of this new method.

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Section: The Iterative Reconstruction Algorithmmentioning

confidence: 99%

<title>Computer tomographic (CT) image reconstruction from limited information</title>

Wang

1995

SPIE Proceedings

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Numerical evaluation of methods for computing tomographic projections

Zhuang

Gopal

Hebert

1994

IEEE Trans. Nucl. Sci.

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A comparison of rotation-based methods for iterative reconstruction algorithms

Bella

Barclay

Eisner

et al. 1996

IEEE Trans. Nucl. Sci.

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Rotation-based projectors and backprojectors offer unique trade-offs between accuracy, computational complexity, and smoothing. We analyzed six different rotation methods for generating projections and iterative reconstructions of tomographic data. Nearest neighbor, upsampled nearest neighbor, bilinear interpolation, and bicubic interpolation were all used to determine the values at rotated grid points. A decomposition of the rotation transformation matrix into three components was also investigated. Linear and cubic interpolation were used in the three-pass method. For all of the methods, mean normalized square errors were computed. Our results demonstrate the tradeoffs associated with the different methods and suggest that the three-pass method with cubic interpolation offers a computationally efficient approach with accuracy between that of bilinear and bicubic interpolation.

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Error bounds for iterative reprojection methods in computerized tomography

Cited by 15 publications

References 9 publications

<title>Computer tomographic (CT) image reconstruction from limited information</title>

<title>Computer tomographic (CT) image reconstruction from limited information</title>

Numerical evaluation of methods for computing tomographic projections

A comparison of rotation-based methods for iterative reconstruction algorithms

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