• Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.113-118
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• 2015

An important problem in low-dose CT is the image quality degradation caused by photon starvation. There are a lot of algorithms in sinogram domain or image domain to solve this problem. In view of strong self-similarity contained in the special sinusoid-like strip data in the sinogram space, we propose a novel non-local filtering, whose average weights are related to both the image FBP (filtered backprojection) reconstructed from restored sinogram data and the image directly FBP reconstructed from noisy sinogram data. In the process of sinogram restoration, we apply a non-local method with smoothness parameters adjusted adaptively to the variance of noisy sinogram data, which makes the method much effective for noise reduction in sinogram domain. Simulation experiments show that our proposed method by filtering in both image and projection domains has a better performance in noise reduction and details preservation in reconstructed images.

• 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.

• 대한방사선방어학회:학술대회논문집
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• 2010.04a
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• pp.56-57
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• 2010

• Nuclear Medicine and Molecular Imaging
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• v.42 no.1
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• pp.52-60
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• 2008

Purpose: To establish the methods for sinogram formation and correction in order to appropriately apply the filtered backprojection (FBP) reconstruction algorithm to the data acquired using PET scanner with multiple scintillation crystal layers. Materials and Methods: Formation for raw PET data storage and conversion methods from listmode data to histogram and sinogram were optimized. To solve the various problems occurred while the raw histogram was converted into sinogram, optimal sampling strategy and sampling efficiency correction method were investigated. Gap compensation methods that is unique in this system were also investigated. All the sinogram data were reconstructed using 20 filtered backprojection algorithm and compared to estimate the improvements by the correction algorithms. Results: Optimal radial sampling interval and number of angular samples in terms of the sampling theorem and sampling efficiency correction algorithm were pitch/2 and 120, respectively. By applying the sampling efficiency correction and gap compensation, artifacts and background noise on the reconstructed image could be reduced. Conclusion: Conversion method from the histogram to sinogram was investigated for the FBP reconstruction of data acquired using multiple scintillation crystal layers. This method will be useful for the fast 20 reconstruction of multiple crystal layer PET data.

• Journal of the Korean Society of Radiology
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• v.9 no.3
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• pp.139-145
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• 2015

When examining a patient using SPECT, gamma rays emitted from the body decrease or scatter. And when they reach the detector they spread in accordance with physical characteristics and geometric shapes of the scanner, quantitative analysis was difficult. For exact quantitative analysis of gamma rays emitted from the body, so that they must be considered to correction about PVE(partial volume effect). In this paper, sinogram filter was implemented to solve comprehensive PVE of SPECT. According to the results in which implemented filter was applied, partial volume effect caused by SPECT was removed. To compare proposed method and conventional method, PSNR(Peak Signal to Noise Ratio) was executed. As a result, proposed method was indicated as 7dB, conventional method was indicated as 14db respectively. dB(decibel) level of the proposed methods is lower, since the MSE(mean square error) becomes greater because scattered ray was removed, PSNR value is low. Therefore, by applying the proposed method for removing the PVE of SPECT imaging method, the image quality is improved.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.431-441
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• 2020

The purpose of this study was to construct a model of MVCT(Megavoltage Computed Tomography) dose calculation by using Dosimetry Check™, a program that radiation treatment dose verification, and establish a protocol that can be accumulated to the radiation treatment dose distribution. We acquired sinogram of MVCT after air scan in Fine, Normal, Coarse mode. Dosimetry Check™(DC) program can analyze only DICOM(Digital Imaging Communications in Medicine) format, however acquired sinogram is dat format. Thus, we made MVCT RC-DICOM format by using acquired sinogram. In addition, we made MVCT RP-DICOM by using principle of generating MLC(Multi-leaf Collimator) control points at half location of pitch in treatment RP-DICOM. The MVCT imaging dose in fine mode was measured by using ionization chamber, and normalized to the MVCT dose calculation model, the MVCT imaging dose of Normal, Coarse mode was calculated by using DC program. As a results, 2.08 cGy was measured by using ionization chamber in Fine mode and normalized based on the measured dose in DC program. After normalization, the result of MVCT dose calculation in Normal, Coarse mode, each mode was calculated 0.957, 0.621 cGy. Finally, the dose resulting from the process for acquisition of MVCT can be accumulated to the treatment dose distribution for dose evaluation. It is believed that this could be contribute clinically to a more realistic dose evaluation. From now on, it is considered that it will be able to provide more accurate and realistic dose information in radiation therapy planning evaluation by using Tomotherapy.

• 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.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.311-319
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• 2001

Statistical reconstruction methods in the context of a Bayesian framework have played an important role in emission tomography since they allow to incorporate a priori information into the reconstruction algorithm. Given the ill-posed nature of tomographic inversion and the poor quality of projection data, the Bayesian approach uses regularizers to stabilize solutions by incorporating suitable prior models. In this work we show that, while the quantitative performance of the standard filtered backprojection (FBP) algorithm is not as good as that of Bayesian methods, the application of spline-regularized smoothing to the sinogram space can make the FBP algorithm improve its performance by inheriting the advantages of using the spline priors in Bayesian methods. We first show how to implement the spline-regularized smoothing filter by deriving mathematical relationship between the regularization and the lowpass filtering. We then compare quantitative performance of our new FBP algorithms using the quantitation of bias/variance and the total squared error (TSE) measured over noise trials. Our numerical results show that the second-order spline filter applied to FBP yields the best results in terms of TSE among the three different spline orders considered in our experiments.