Due to the finite size of detector unit, the area integral model (AIM) is closer to reality than the line integral model (LIM) when X-ray beams passing through a sample cast to CCD. A simultaneous algebraic reconstruc...
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Due to the finite size of detector unit, the area integral model (AIM) is closer to reality than the line integral model (LIM) when X-ray beams passing through a sample cast to CCD. A simultaneous algebraic reconstruction technique (sart) algorithm for AIM is proposed for improved image quality. First, a cone-beam to parallel-beam rearranged step was performed to simplify the model computation. Then, a judgement formula was used to quickly find the cells to be traversed by a strip-shaped X-ray beam and an effective method was presented to calculate intersection region. Via the comparison, both simulation and experiment show that this area-based sart has better reconstruction quality than traditional line-based sart. The algorithm is demonstrated the ability to micro-CT reconstruction. This AIM approach could be adapted for other iterative algorithms in fan-beam or small cone- angle beam CT geometry.
Optical tomography imaging is widely used in target-detection, aerospace precision instrumentation and geological material detection for its non-contact, long-distance and high-precision imaging characteristics. Due t...
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ISBN:
(纸本)9781510636323
Optical tomography imaging is widely used in target-detection, aerospace precision instrumentation and geological material detection for its non-contact, long-distance and high-precision imaging characteristics. Due to the different application range and structural design of the tomography system, the inevitable inadequate projection data and the offset of the rotation center may occur in the actual acquisition, which may cause artifacts and unclearness in the reconstructed image. In this paper, based on the research of image reconstruction algorithm, the paper compares filter back projection algorithm with iterative algorithm and analyzes effects of the reconstruction process with iterative algorithm under multiple parameters. Determining the appropriate weighting model, iteration number and relaxation factor, etc. Combined with high quality initial image and convex set constraints, an optimized sart algorithm is proposed. The experiment uses the optimized sart algorithm for image reconstruction. By comparing the image evaluation parameters with sharpness and average gradient, it is verified that the construction image with the optimized sart is better and clearer than those with the unoptimized sart and the simple filtered back projection algorithm.
Context: There has been considerable progress in the instrumentation for data measurement and computer methods for generating images of measured PET data. These computer methods have been developed to solve the invers...
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Context: There has been considerable progress in the instrumentation for data measurement and computer methods for generating images of measured PET data. These computer methods have been developed to solve the inverse problem, also known as the "image reconstruction from projections'' problem. Aim: In this paper, we propose a modified Simultaneous Algebraic Reconstruction Technique (sart) algorithm to improve the quality of image reconstruction by incorporating total variation (TV) minimization into the iterative sart algorithm. Methodology: The sart updates the estimated image by forward projecting the initial image onto the sinogram space. Then, the difference between the estimated sinogram and the given sinogram is back-projected onto the image domain. This difference is then subtracted from the initial image to obtain a corrected image. Fast total variation (FTV) minimization is applied to the image obtained in the sart step. The second step is the result obtained from the previous FTV update. The sart and the FTV minimization steps run iteratively in an alternating manner. Fifty iterations were applied to the sart algorithm used in each of the regularization-basedmethods. In addition to the conventional sart algorithm, spatial smoothing was used to enhance the quality of the image. All images were sized at 128 x 128 pixels. Results: The proposed algorithm successfully accomplished edge preservation. A detailed scrutiny revealed that the reconstruction algorithms differed;for example, the sart and the proposed FTV-sart algorithm effectively preserved the hot lesion edges, whereas artifacts and deviations were more likely to occur in the ART algorithm than in the other algorithms. Conclusions: Compared to the standard sart, the proposed algorithm is more robust in removing background noise while preserving edges to suppress the existent image artifacts. The quality measurements and visual inspections show a significant improvement in image quality compared to th
The use of iterative algorithms in tomographic reconstruction always leads to a frequency adapted rate of convergence in that low frequencies are accurately reconstructed after a few iterations, while high frequencies...
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The use of iterative algorithms in tomographic reconstruction always leads to a frequency adapted rate of convergence in that low frequencies are accurately reconstructed after a few iterations, while high frequencies sometimes requiremanymore computations. In this paper, we propose to build frequency adapted (FA) algorithms based on a condition of incomplete backprojection and propose an FA simultaneous algebraic reconstruction technique (FA-sart) algorithm as an example. The results obtained with the FA-sart algorithm demonstrate a very fast convergence on a highly detailed phantom when compared to the original sart algorithm. Though the use of such an FA algorithm may seem difficult, we specify in which case it is relevant and propose several ways to improve the reconstruction process with FA algorithms. Copyright (C) 2006 Vincent Israel-Jost et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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