Purpose: A new cone-beam CT scanner for image-guided radiotherapy (IGRT) can independently rotate the source and the detector along circular trajectories. Existing reconstruction algorithms are not suitable for this s...
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Purpose: A new cone-beam CT scanner for image-guided radiotherapy (IGRT) can independently rotate the source and the detector along circular trajectories. Existing reconstruction algorithms are not suitable for this scanning geometry. The authors propose and evaluate a three-dimensional (3D) filtered-backprojection reconstruction for this situation. Methods: The source and the detector trajectories are tuned to image a field-of-view (FOV) that is offset with respect to the center-of-rotation. The new reconstruction formula is derived from the Feldkamp algorithm and results in a similar three-step algorithm: projection weighting, ramp filtering, and weighted backprojection. Simulations of a Shepp Logan digital phantom were used to evaluate the new algorithm with a 10 cm-offset FOV. A real cone-beam CT image with an 8.5 cm-offset FOV was also obtained from projections of an anthropomorphic head phantom. Results: The quality of the cone-beam CT images reconstructed using the new algorithm was similar to those using the Feldkamp algorithm which is used in conventional cone-beam CT. The real image of the head phantom exhibited comparable image quality to that of existing systems. Conclusions: The authors have proposed a 3D filtered-backprojection reconstruction for scanners with independent source and detector rotations that is practical and effective. This algorithm forms the basis for exploiting the scanner's unique capabilities in IGRT protocols. (C) 2016 American Association of Physicists in Medicine.
In this paper, we discuss the implementation of the filtered-backprojection (FBP) algorithm for tomographic image reconstruction using Walsh transform to exploit its fast computational ability. Walsh transform is the ...
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In this paper, we discuss the implementation of the filtered-backprojection (FBP) algorithm for tomographic image reconstruction using Walsh transform to exploit its fast computational ability. Walsh transform is the fastest unitary transform known so far. The major advantage of Walsh transform is that it involves only real additions and subtractions whereas Fourier transform involves complex multiplications and additions. Implementation of the proposed algorithm necessitates the design of an appropriate filter in Walsh domain. In this research, the known Fourier filter coefficients have been transformed into Walsh domain, thereby the 1 x N Fourier filter coefficients were converted into an N x N sparse matrix with nonzero elements in a special pattern. The proposed algorithm has been implemented by taken into account of the special nature of the Walsh domain filter coefficients and tested for its performance using the well-known 'Shepp-Logan head phantom' test image. The results demonstrate that the reconstruction strategy has comparable performance with a significant reduction of computing time. For example, with a 128 x 128-pixel image and 180 views, the speedup achieved is fourfold, with reconstructions qualitatively and visually the same as that of FBP algorithm in the Fourier domain. (C) 2006 Elsevier Inc. All rights reserved.
The long object problem is practically important and theoretically challenging. To solve the long object problem, spiral cone-beam CT was first proposed in 1991, and has been extensively studied since then. As a main ...
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The long object problem is practically important and theoretically challenging. To solve the long object problem, spiral cone-beam CT was first proposed in 1991, and has been extensively studied since then. As a main feature of the next generation medical CT, spiral cone-beam CT has been greatly improved over the past several years, especially in terms of exact image reconstruction methods. Now, it is well established that volumetric images can be exactly and efficiently reconstructed from longitudinally truncated data collected along a rather general scanning trajectory. Here we present an overview of some key results in this area.
In this paper, we present an efficient parallel system with an interconnection network customized for Positron Emission Tomography (PET) image reconstruction. The proposed parallel reconstruction system has two distin...
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In this paper, we present an efficient parallel system with an interconnection network customized for Positron Emission Tomography (PET) image reconstruction. The proposed parallel reconstruction system has two distinguished features. On feature is that the interconnection network is optimal for both filteredbackprojection and EM algorithms, rather than only for one of them. The other feature is that with only four-connectivity in contrast to log N-connectivity for a hypercube, the proposed parallel algorithms may accomplish the same performance in terms of order statistics as achieved by the optimal algorithms on a hypercube. The proposed parallel system has been realized using transputers. (C) 1998 Elsevier Science B.V. All rights reserved.
By reformulating Grangeat's algorithm for the circular orbit, it is discovered that an arbitrary function to be reconstructed, f((r) over right arrow), can be expressed as the sum of three terms:f((r) over right a...
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By reformulating Grangeat's algorithm for the circular orbit, it is discovered that an arbitrary function to be reconstructed, f((r) over right arrow), can be expressed as the sum of three terms:f((r) over right arrow)=f(M0)((r) over right arrow)+f(M1)+((r) over right arrow)+f(N)((r) over right arrow) where f(M0)((r) over right arrow) corresponds to the Feldkamp reconstruction, f(M1)((r) over right arrow) represents the information derivable from the circular scan but not utilized in Feldkamp's algorithm, and f(N)((r) over right arrow) represents the information which cannot be derived from the circular scanning geometry. Thus, a new cone-beam reconstruction algorithm for the circular orbit is proposed as follows: (1) compute f(M0) ((r) over right arrow) using Feldkamp's algorithm, (2) compute f(M1)((r) over right arrow) using the formula developed in this paper, and (3) estimate f(N)((r) over right arrow) using a priori knowledge such as that suggested in Grangeat's algorithm. This study shows that by including the f(M1)((r) over right arrow) term, the new algorithm provides more accurate reconstructions than those of Feldkamp even without the f(N)((r) over right arrow) estimation.
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