• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.9
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• pp.143-147
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• 2016

Recently, digital tomosynthesis system (DTS) has been developed to reduce overlap using conventional X-ray and to overcome high patient dose problem using computed tomography (CT). The purpose of this study was to develop image reconstruction algorithm and to evaluate image characteristics and dose with chest digital tomosynthesis (CDT) system. Image reconstruction was used for filtered back-projection (FBP) methods and system geometry was constructed ${\pm}10^{\circ}$, ${\pm}15^{\circ}$, ${\pm}20^{\circ}$, and ${\pm}30^{\circ}$ angular range for acquiring phantom images. Image characteristics carried out root mean square error (RMSE) and signal difference-to-noise ratio (SDNR), and dose is evaluated effective dose with ${\pm}20^{\circ}$ angular range. According to the results, the phantom image with slice thickness filter has superb RMSE and SDNR, and effective dose was 0.166 mSv. In conclusion, we demonstrated usefulness of developed CDT image reconstruction algorithm and we constructed CDT basic output data with measuring effective dose.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.18-23
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• 2008

In this paper, we propose the input image selection-method to improve the reconstructed high-resolution (HR) image quality. To obtain ideal super-resolution (SR) reconstruction image, all input images are well-registered. However, the registration is not ideal in practice. Due to this reason, the selection of input images with low registration error (RE) is more important than the number of input images in order to obtain good quality of a HR image. The suitability of a candidate input image can be determined by using statistical and restricted registration properties. Therefore, we propose the proper candidate input Low Resolution(LR) image selection-method as a pre-processing for the SR reconstruction in automatic manner. In video sequences, all input images in specified region are allowed to use SR reconstruction as low-resolution input image and/or the reference image. The candidacy of an input LR image is decided by the threshold value and this threshold is calculated by using the maximum motion compensation error (MMCE) of the reference image. If the motion compensation error (MCE) of LR input image is in the range of 0 < MCE < MMCE then this LR input image is selected for SR reconstruction, else then LR input image are neglected. The optimal reference LR (ORLR) image is decided by comparing the number of the selected LR input (SLRI) images with each reference LR input (RLRI) image. Finally, we generate a HR image by using optimal reference LR image and selected LR images and by using the Hardie's interpolation method. This proposed algorithm is expected to improve the quality of SR without any user intervention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.530-537
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• 2019

In this paper we propose a novel approach to representation of the 3D reconstruction problem by employing a concept of system function that is defined as the ratio of the output to the input signal. Akin to determination of system function (or system response), this paper determines system function by choosing (or defining) appropriate input and output signals. In other words, the 3D reconstruction using structured circular light patterns is reformulated as determination of system function from input and output signals. This paper introduces two algorithms for the reconstruction. The one defines the input and output signals as projected circular light patterns and the images overlaid with the patterns and captured by camera, respectively. The other one defines input and output signals as 3D coordinates of the object surface and the image captured by camera. The first one leads to the problem as identifying the system function and the second one leads to the problem as estimation of an input signal employing concept of modulation-demodulation theory. This paper substantiate the proposed approach by providing experimental results.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

• Journal of Microbiology and Biotechnology
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• v.24 no.8
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• pp.1065-1072
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• 2014

Doxorubicin, produced by Streptomyces peucetius ATCC 27952, is tightly regulated by dnrO, dnrN, and dnrI regulators. Genome mining of S. peucetius revealed the presence of the IclR (doxR) type family of transcription regulator mediating the signal-dependent expression of operons at the nonribosomal peptide synthetase gene cluster. Overexpression of doxR in native strain strongly repressed the drug production. Furthermore, it also had a negative effect on the regulatory system of doxorubicin, wherein the transcript of dnrI was reduced to the maximum level in comparision with the other two. Interestingly, the overexpression of the same gene also had strong inhibitory effects on the production of actinorhodin (blue pigment) and undecylprodigiosin (red pigment) in Streptomyces coelicolor M145, herboxidiene production in Streptomyces chromofuscus ATCC 49982, and spinosyn production in Saccharopolyspora spinosa NRRL 18395, respectively. Moreover, DoxR exhibited pleiotropic effects on the production of blue and red pigments in S. coelicolor when grown in different agar media, wherein the production of blue pigment was inhibited in R2YE medium and the red pigment was inhibited in YEME medium. However, the production of both blue and red pigments from S. coelicolor harboring doxR was halted in ISP2 medium, whereas S. coelicolor produced both pigmented antibiotics in the same plate. These consequences demonstrate that the on and off production of these antibiotics was not due to salt stress or media compositions, but was selectively controlled in actinomycetes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.425-433
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• 2018

In this paper, we propose a super-resolution reconstruction algorithm for plenoptic images based on the reliability of disparity. The subperture image generated by the Flanoptic camera image is used for disparity estimation and reconstruction of super-resolution image based on TV_L1 algorithm. In particular, the proposed image reconstruction method is effective in the boundary region where disparity may be relatively inaccurate. The determination of reliability of disparity vector is based on the upper, lower, left and right positional relationship of the sub-aperture image. In our method, the unreliable vectors are excluded in reconstruction. The performance of the proposed method was evaluated by comparing to a bicubic interpolation method, a conventional disparity based method and dictionary based method. The experimental results show that the proposed method provides the best performance in terms of PSNR(Peak Signal to noise ratio), SSIM(Structural Similarity).

• Journal of the Korean Physical Society
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• v.73 no.9
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• pp.1393-1398
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• 2018

In conventional single-photon emission computed tomography (SPECT), only the photoelectric events in the detectors are used for image reconstruction. However, if the $^{131}I$ isotope, which emits high-energy radiations (364, 637, and 723 keV), is used in nuclear medicine, both photoelectric and Compton scattering events can be used for image reconstruction. The purpose of our work is to perform simulations for Compton SPECT by using the Geant4 application for tomographic emission (GATE). The performance of Compton SPECT is evaluated and compared with that of conventional SPECT. The Compton SPECT unit has an area of $12cm{\times}12cm$ with four gantry heads. Each head is composed of a 2-cm tungsten collimator and a $40{\times}40$ array of CdZnTe (CZT) crystals with a $3{\times}3mm^2$ area and a 6-mm thickness. Compton SPECT can use not only the photoelectric effect but also the Compton scattering effect for image reconstruction. The correct sequential order of the interactions used for image reconstruction is determined using the angular resolution measurement (ARM) method and the energies deposited in each detector. In all the results of simulations using spherical volume sources of various diameters, the reconstructed images of Compton SPECT show higher signal-to-noise ratios (SNRs) without degradation of the image resolution when compared to those of conventional SPECT because the effective count for image reconstruction is higher. For a Derenzo-like phantom, the reconstructed images for different modalities are compared by visual inspection and by using their projected histograms in the X-direction of the reconstructed images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.343-364
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• 2021

In the 5G era, the communication networks tend to be ultra-densified, which will improve the accuracy of indoor positioning and further improve the quality of positioning service. In this study, we propose an indoor three-dimensional (3D) dynamic reconstruction fingerprint matching algorithm (DSR-FP) in a 5G ultra-dense network. The first step of the algorithm is to construct a local fingerprint matrix having low-rank characteristics using partial fingerprint data, and then reconstruct the local matrix as a complete fingerprint library using the FPCA reconstruction algorithm. In the second step of the algorithm, a dynamic base station matching strategy is used to screen out the best quality service base stations and multiple sub-optimal service base stations. Then, the fingerprints of the other base station numbers are eliminated from the fingerprint database to simplify the fingerprint database. Finally, the 3D estimated coordinates of the point to be located are obtained through the K-nearest neighbor matching algorithm. The analysis of the simulation results demonstrates that the average relative error between the reconstructed fingerprint database by the DSR-FP algorithm and the original fingerprint database is 1.21%, indicating that the accuracy of the reconstruction fingerprint database is high, and the influence of the location error can be ignored. The positioning error of the DSR-FP algorithm is less than 0.31 m. Furthermore, at the same signal-to-noise ratio, the positioning error of the DSR-FP algorithm is lesser than that of the traditional fingerprint matching algorithm, while its positioning accuracy is higher.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.402-412
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• 2022

Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images. Materials and Methods: This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM). Results: LDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; p = 0.581). Conclusion: Overall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.

• Korean Journal of Radiology
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• v.20 no.5
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• pp.729-738
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• 2019

Objective: To assess the effects of iterative model reconstruction (IMR) on image quality for demonstrating non-calcific high-risk plaque characteristics of coronary arteries. Materials and Methods: This study included 66 patients (53 men and 13 women; aged 39-76 years; mean age, 55 ± 13 years) having single-vessel disease with predominantly non-calcified plaques evaluated using prospective electrocardiogram-gated 256-slice CT angiography. Paired image sets were created using two types of reconstruction: hybrid iterative reconstruction (HIR) and IMR. Plaque characteristics were compared using the two algorithms. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images and the CNR between the plaque and adjacent adipose tissue were also compared between the two reformatted methods. Results: Seventy-seven predominantly non-calcified plaques were detected. Forty plaques showed napkin-ring sign with the IMR reformatted method, while nineteen plaques demonstrated napkin-ring sign with HIR. There was no statistically significant difference in the presentation of positive remodeling, low attenuation plaque, and spotty calcification between the HIR and IMR reconstructed methods (all p > 0.5); however, there was a statistically significant difference in the ability to discern the napkin-ring sign between the two algorithms (χ² = 12.12, p < 0.001). The image noise of IMR was lower than that of HIR (10 ± 2 HU versus 12 ± 2 HU; p < 0.01), and the SNR and CNR of the images and the CNR between plaques and surrounding adipose tissues on IMR were better than those on HIR (p < 0.01). Conclusion: IMR can significantly improve image quality compared with HIR for the demonstration of coronary artery and atherosclerotic plaques using a 256-slice CT.