• Journal of Biomedical Engineering Research
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• v.32 no.3
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• pp.257-263
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• 2011

Sparse-view CT imaging is considered to be a solution to reduce x-ray dose of CT. Sparse-view CT imaging may have severe streak artifacts that could compromise the image qualities. We have compared quality of sparseview images reconstructed with two representative iterative reconstruction techniques, SIRT and TV-minimization, in terms of image error and edge preservation. In the comparison study, we have used the Shepp-Logan phantom image and real CT images obtained with a micro-CT. In both phantom image and real CT image tests, TV-minimization technique shows the best performance in error reduction and preserving edges. However, the excessive computation time of TV-minimization is a technical challenge for the practical use.

• Journal of Biomedical Engineering Research
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• v.35 no.5
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• pp.142-150
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• 2014

The key to model-based iterative reconstruction (MBIR) algorithms for transmission computed tomography lies in the ability to accurately model the data formation process from the emitted photons produced in the transmission source to the measured photons at the detector. Therefore, accurately modeling the system matrix that accounts for the data formation process is a prerequisite for MBIR-based algorithms. In this work we compared quantitative performance of the three representative ray-driven methods for calculating the system matrix; the ray-tracing method (RTM), the distance-driven method (DDM), and the strip-area based method (SAM). We implemented the ordered-subsets separable surrogates (OS-SPS) algorithm using the three different models and performed simulation studies using a digital phantom. Our experimental results show that, in spite of the more advanced features in the SAM and DDM, the traditional RTM implemented in the OS-SPS algorithm with an edge-preserving regularizer out-performs the SAM and DDM in restoring complex edges in the underlying object. The performance of the RTM in smooth regions was also comparable to that of the SAM or DDM.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.1
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• pp.41-46
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• 2023

Iterative reconstruction of CT takes a long time because projection and back-projection are alternatively repeated until taking a good image. To reduce the reconstruction time, we need a fast algorithm for calculating the projection which is a time-consuming step. In this paper, we proposed a new algorithm to calculate the line integral and the algorithm is approximately 10% faster than the well-known Siddon method (Jacobs version) and has a good image quality. Although the algorithm has been investigated for the case of parallel beams, it can be extended to the case of fan and cone beam geometries in the future.

• Journal of the Korean Mathematical Society
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• v.57 no.5
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• pp.1079-1101
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• 2020

This work is concerned with a geometric inverse problem in fluid mechanics. The aim is to reconstruct an unknown obstacle immersed in a Newtonian and incompressible fluid flow from internal data. We assume that the fluid motion is governed by the Stokes-Brinkmann equations in the two dimensional case. We propose a simple and efficient reconstruction method based on the topological sensitivity concept. The geometric inverse problem is reformulated as a topology optimization one minimizing a least-square functional. The existence and stability of the optimization problem solution are discussed. A topological sensitivity analysis is derived with the help of a straightforward approach based on a penalization technique without using the classical truncation method. The theoretical results are exploited for building a non-iterative reconstruction algorithm. The unknown obstacle is reconstructed using a levelset curve of the topological gradient. The accuracy and the robustness of the proposed method are justified by some numerical examples.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.4
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• pp.294-301
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• 2016

In this paper, we present methods to efficiently parallelize iterative 3D image reconstruction by exploiting trigeneous devices (three different types of device) at the same time: a CPU, an integrated GPU, and a discrete GPU. We first present a technique that exploits single instruction multiple data (SIMD) architectures in GPUs. Then, we propose a performance estimation model, based on which we can easily find the optimal data partitioning on trigeneous devices. We found that the performance significantly varies by up to 6.23 times, depending on how SIMD units in GPUs are accessed. Then, by using trigeneous devices and the proposed estimation models, we achieve optimal partitioning and throughput, which corresponds to a 9.4% further improvement, compared to discrete GPU-only execution.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.20-24
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• 2009

Objective: In this study, the differences between two reconstruction methods were analyzed by comparing image uniformity and contrast according to Iteration and Subset, which were altered through the Iterative method and True X method, used in Siemens' PET/CT studies. Methods: The Phantom images were obtained by exposure for two minutes per one bed. To determine the image uniformity, the Coefficient of variance was used. Also, in order to compare the contrast value, we measured and analyzed the ratio of the SUV mean of Phantom image's hot spheres and the background. Results: Under the same reconstruction conditions (Iteration and Subset) of CV, the Iterative method was higher than the True X method. In the comparison of the SUV mean ratio of the background and hot sphere, the True X method had a closer rate than the Iterative method. Conclusion: The newly developed True X reconstruction method is better than the previously used Iterative method in terms of uniformity and contrast. However, the date for this study was only obtained using the Phantom device. In order to obtain more accurate and useful information from the experiment, further research should be conducted. Also, it is necessary to find the appropriate standards for Iteration and Subset for further experimentation.

• Progress in Medical Physics
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• v.26 no.1
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• pp.28-35
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• 2015

In this study, we investigated the effects of an iterative reconstruction algorithm and an automatic exposure control (AEC) technique on image quality and radiation dose through phantom experiments with coronary computed tomography (CT) angiography protocols. We scanned the AAPM CT performance phantom using 320 multi-detector-row CT. At the tube voltages of 80, 100, and 120 kVp, the scanning was repeated with two settings of the AEC technique, i.e., with the target standard deviations (SD) values of 33 (the higher tube current) and 44 (the lower tube current). The scanned projection data were reconstructed also in two ways, with the filtered back projection (FBP) and with the iterative reconstruction technique (AIDR-3D). The image quality was evaluated quantitatively with the noise standard deviation, modulation transfer function, and the contrast to noise ratio (CNR). More specifically, we analyzed the influences of selection of a tube voltage and a reconstruction algorithm on tube current modulation and consequently on radiation dose. Reduction of image noise by the iterative reconstruction algorithm compared with the FBP was revealed eminently, especially with the lower tube current protocols, i.e., it was decreased by 46% and 38%, when the AEC was established with the lower dose (the target SD=44) and the higher dose (the target SD=33), respectively. As a side effect of iterative reconstruction, the spatial resolution was decreased by a degree that could not mar the remarkable gains in terms of noise reduction. Consequently, if coronary CT angiogprahy is scanned and reconstructed using both the automatic exposure control and iterative reconstruction techniques, it is anticipated that, in comparison with a conventional acquisition method, image noise can be reduced significantly with slight decrease in spatial resolution, implying clinical advantages of radiation dose reduction, still being faithful to the ALARA principle.

• 한국생물공학회:학술대회논문집
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• 2005.04a
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• pp.119-119
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• 2005

• Journal of Biomedical Engineering Research
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• v.24 no.5
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• pp.473-480
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• 2003

Iterative reconstruction methods have played a prominent role in emission computed tomography due to their remarkable advantages over the conventional filtered backprojection method. However, since iterative reconstructions typically are comprised of repeatedly projecting and backprojecting the data, the computational load required for reconstructing an image depends highly on the performance of the projector-backprojector pair used in the algorithm. In this work we compare quantitative performance of representative methods for implementing projector-backprojector pairs. To reduce the overall cost for the projection-backprojection operations for each method, we investigate how previously computed results can be reused so that the number of redundant calculations can be minimized. Our experimental results demonstrate that the ray tracing method not only outperforms other methods in computation time, but also provides improved reconstructions with good accuracy.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.211-220
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• 2015

Recently, model-based iterative reconstruction (MBIR) has played an important role in transmission tomography by significantly improving the quality of reconstructed images for low-dose scans. MBIR is based on the penalized-likelihood (PL) approach, where the penalty term (also known as the regularizer) stabilizes the unstable likelihood term, thereby suppressing the noise. In this work we further improve MBIR by using a more expressive regularizer which can restore the underlying image more accurately. Here we used a spline regularizer derived from a linear combination of the two-dimensional splines with first- and second-order spatial derivatives and applied it to a non-quadratic convex penalty function. To derive a PL algorithm with the spline regularizer, we used a separable paraboloidal surrogates algorithm for convex optimization. The experimental results demonstrate that our regularization method improves reconstruction accuracy in terms of both regional percentage error and contrast recovery coefficient by restoring smooth edges as well as sharp edges more accurately.