• Progress in Medical Physics
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• v.27 no.3
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• pp.105-110
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• 2016

Sparse angular sampling has been studied recently owing to its potential to decrease the radiation exposure from computed tomography (CT). In this study, we investigated the analytic reconstruction algorithm in sparse angular sampling using the sinogram interpolation method for improving image quality and computation speed. A prototype of the spectral CT system, which has a 64-pixel Cadmium Zinc Telluride (CZT)-based photon-counting detector, was used. The source-to-detector distance and the source-to-center of rotation distance were 1,200 and 1,015 mm, respectively. Two energy bins (23~33 keV and 34~44 keV) were set to obtain two reconstruction images. We used a PMMA phantom with height and radius of 50.0 mm and 17.5 mm, respectively. The phantom contained iodine, gadolinium, calcification, and lipid. The Feld-kamp-Davis-Kress (FDK) with the sinogram interpolation method and Maximum Likelihood Expectation Maximization (MLEM) algorithm were used to reconstruct the images. We evaluated the signal-to-noise ratio (SNR) of the materials. The SNRs of iodine, calcification, and liquid lipid were increased by 167.03%, 157.93%, and 41.77%, respectively, with the 23~33 keV energy bin using the sinogram interpolation method. The SNRs of iodine, calcification, and liquid state lipid were also increased by 107.01%, 13.58%, and 27.39%, respectively, with the 34~44 keV energy bin using the sinogram interpolation method. Although the FDK algorithm with the sinogram interpolation did not produce better results than the MLEM algorithm, it did result in comparable image quality to that of the MLEM algorithm. We believe that the sinogram interpolation method can be applied in various reconstruction studies using the analytic reconstruction algorithm. Therefore, the sinogram interpolation method can improve the image quality in sparse-angular sampling and be applied to CT applications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.601-609
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• 2012

In this paper, a limited-view CT image reconstruction method was studied to reduce the scan times and the X-ray dose for the patients. To reduce streak artifacts which is caused by insufficient number of views, we introduce a sinogram interpolation method based on image matching. Image matching is achieved using the characteristics of the neighboring views including intensity, gradient and distance between the pixels. Interpolation is performed using the image matching results.. A numerical phantom and Al-acryl phantom were used for evaluating the effectiveness of the proposed interpolation method. The results showed that streak artifacts were reduced in the reconstructed images while the details of the images were preserved. Moreover, maximum 5% improvements in terms of PSNR were observed.

• Journal of the Korean Society of Radiology
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• v.11 no.7
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• pp.655-661
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• 2017

Sparse view CT has been widely used to reduce radiation dose to patient in radiation therapy. In this work, we performed sinogram restoration from sparse sampling data by using inpainting method for simulation and experiment. Sinogram restoration was performed in accordance with sampling angle and restoration method, and their results were validated with root mean square error (RMSE) and image profiles. Simulation and experiment are designed to fan beam scan for various projection angles. Sparse data in sinogram were restored by using linear interpolation and inpainting method. Then, the restored sinogram was reconstructed with filtered backprojection (FBP) algorithm. The results showed that RMSE and image profiles were depended on the projection angles and restoration method. Based on the simulation and experiment, we found that inpainting method could be improved for sinogram restoration in comparison to linear interpolation method for estimating RMSE and image profiles.

• Journal of Biomedical Engineering Research
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• v.30 no.6
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• pp.476-481
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• 2009

The most troublesome artifacts in micro computed tomography (micro-CT) are ring artifacts. The ring artifacts are caused by non-uniform sensitivity and defective pixels of the x-ray detector. These ring artifacts seriously degrade the quality of CT images. In flat-panel detector based micro-CT systems, the ring artifacts are hardly removed by conventional correction methods of digital radiography, because very small difference of detector pixel signals may make severe ring artifacts. This paper presents a novel method to remove ring artifacts in flat-panel detector based micro-CT systems. First, the bad lines of a sinogram which are caused by defective pixels of the detector are identified, and then, they are corrected using a cubic spline interpolation technique. Finally, a ring artifacts free image is reconstructed from the corrected projections. We applied the method to various kinds of objects and found that the image qualities were much improved.

• Progress in Medical Physics
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• v.24 no.4
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• pp.303-314
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• 2013

Image quality of computed tomography (CT) is very vulnerable to metal artifacts. Recently, the thickness and background normalization techniques have been introduced. Since they provide flat sinograms, it is easy to determine metal traces and a simple linear interpolation would be enough to describe the missing data in sinograms. In this study, we have developed a theory describing two normalization methods and compared two methods with respect to various sizes and numbers of metal inserts by using simple numerical simulations. The developed theory showed that the background normalization provide flatter sinograms than the thickness normalization, which was validated with the simulation results. Numerical simulation results with respect to various sizes and numbers of metal inserts showed that the background normalization was better than the thickness normalization for metal artifact corrections. Although the residual artifacts still existed, we have showed that the background normalization without the segmentation procedure was better than the thickness normalization for metal artifact corrections. Since the background normalization without the segmentation procedure is simple and it does not require any users' intervention, it can be readily installed in conventional CT systems.