• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.37-46
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• 2023

This study aims to evaluate the usefulness of Deep Learning Image Reconstruction (TrueFidelity, TF), the image quality of existing Filtered Back Projection (FBP) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) were compared. Noise, CNR, and SSIM were measured by obtaining images with doses fixed at 17.29 mGy and altered to 10.37 mGy, 12.10 mGy, 13.83 mGy, and 15.56 mGy in reconstruction techniques of FBP, ASIR-V 50%, and TF-H. TF-H has superior image quality compared to FBP and ASIR-V when the reconstruction technique change is given at 17.29 mGy. When dose changes were made, Noise, CNR, and SSIM were significantly different when comparing 10.37 mGy TF-H and FBP (p<0.05), and no significant difference when comparing 10.37 mGy TF-H and ASIR-V 50% (p>0.05). TF-H has a dose-reduction effect of 30%, as the highest dose of 15.56 mGy ASIR-V has the same image quality as the lowest dose of 10.37 mGy TF-H. Thus, Deep Learning Reconstruction techniques (TF) were able to reduce dose compared to Iterative Reconstruction techniques (ASIR-V) and Filtered Back Projection (FBP). Therefore, it is considered to reduce the exposure dose of patients.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.7
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• pp.1001-1007
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• 2014

This paper proposes a method to reduce operation time using discrete cosine transform and to improve image quality by the DC gain correction. Conventional filtered back projection (FBP) filtering in the frequency domain using Fourier transform, but the filtering process uses complex number operations. To simplify the filtering process, we propose a filtering process using discrete cosine transform. In addition, the image quality of reconstructed images are improved by correcting DC gain of sinograms. To correct the DC gain, we propose to find an optimum DC weight is defined as the ratio of sinogram DC and optimum DC. Experimental results show that the proposed method gets better performance than the conventional method for phantom and clinical CT images.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.258-265
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• 2021

High levels for noise and a loss of true signal make the quantitative interpretation of nuclear medicine (NM) images difficult. An application of profile optimization using a sigmoidal function in this study was used to acquire the NM images with high quality. And the images were acquired by using three kinds of reconstruction method using each same sinogram: a standard filtered back-projection (FBP), an iterative reconstruction (IR) technique, and the sigmoidal function profile optimization (SFPO). Comparison of image according to reconstruction method was performed to show a superiority of the SFPO for imaging. The images reconstructed by using the SFPO showed an average of 1.49 times and of 1.17 times better in contrast than the results obtained using the standard FBP and the IR technique, respectively. Higher signal to noise ratios were obtained as an average of 12.30 times and of 3.77 times than results obtained using the standard FBP and the IR technique, respectively. This study confirms that reconstruction with SFPO (vs FBP and vs IR) can lead to better lesion detectability and characterization with noise reduction. It can be developed for future reconstruction technique for the NM imaging.

• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.603-612
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• 2021

The effect of Advanced Modeled Iterative Reconstruction (ADMIRE) on the coronary artery calcium (CAC) score of computed tomography was evaluated. Coronary artery calcium images (348 calcium, 6 groups, total of 2088 calcium) were acquired by 128-slice dual-source CT of 89 patients.Volume score and Agatston score were measured from images reconstructed with filtered back projection (FBP) and ADMIRE (1-5). The difference between FBP and ADMIRE Strength (1-5) was confirmed through the Kruskal-Wallis test, and the post-hoc analysis was performed using the Mann-Whitney U test based on FBP. Both volume score and Agatston score showed statistically significant differences between FBP and ADMIRE (1-5) (P=0.015, P=0.0.38). As a result of post hoc analysis, the volume score decreased to 9.5% in ADMIRE 4 (Z=-2.359, P=0.018) and 13.2% in ADMIRE 5 (Z=-3.113, P=0.002) based on FBP. Agatston score decreased to 10.4% in ADMIRE 4 (Z=-2.051, P=0.040) and 14.0% in ADMIRE 5 (Z=-2.718, P=0.007) based on FBP. High ADMIRE strength affected the volume score and Agatston score due to the decrease in calcium area. In addition, the change in the Density factor due to the decrease in Maximum HU may affect the calculation of the Agatston score.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.122-127
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• 2010

Purpose: The Xpress3.$cardiac^{TM}$ which is a kind of wide beam reconstruction (WBR) method developed by UltraSPECT (Haifa, Israel) enables the acquisition of at quarter time while maintaining image quality. The purpose of this study is to investigate the usefulness of WBR method for decreasing scan times and to compare to it with filtered back projection (FBP), which is the method routinely used. Materials and Methods: Phantom and clinical studies were performed. The anthropomorphic torso phantom was made on an equality with counts from patient's body. The Tl-201 concentrations in the compartments were 74 kBq (2 ${\mu}Ci$)/cc in myocardium, 11.1 kBq (0.3 ${\mu}Ci$)/cc in soft tissue, and 2.59 kBq (0.07 ${\mu}Ci$)/cc in lung. The non-gated Tl-201 myocardial perfusion SPECT data were acquired with the phantom. The former study was scanned for 50 seconds per frame with FBP method, and the latter study was acquired for 13 seconds per frame with WBR method. Using the Xeleris ver. 2.0551, full width at half maximum (FWHM) and average image contrast were compared. In clinical studies, we analyzed the 30 patients who were examined by Tl-201 gated myocardial perfusion SPECT in department of nuclear medicine at Asan Medical Center from January to April 2010. The patients were imaged at full time (50 second per frame) with FBP algorithm and again quarter-time (13 second per frame) with the WBR algorithm. Using the 4D MSPECT (4DM), Quantitative Perfusion SPECT (QPS), and Quantitative Gated SPECT (QGS) software, the summed stress score (SSS), summed rest score (SRS), summed difference score, end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF) were analyzed for their correlations and statistical comparison by paired t-test. Results: As a result of the phantom study, the WBR method improved FWHM more than about 30% compared with FBP method (WBR data 5.47 mm, FBP data 7.07 mm). And the WBR method's average image contrast was also higher than FBP method's. However, in result of quantitative indices, SSS, SDS, SRS, EDV, ESV, EF, there were statistically significant differences from WBR and FBP(p<0.01). In the correlation of SSS, SDS, SRS, there were significant differences for WBR and FBP (0.18, 0.34, 0.08). But EDV, ESV, EF showed good correlation with WBR and FBP (0.88, 0.89, 0.71). Conclusion: From phantom study results, we confirmed that the WBR method reduces an acquisition time while improving an image quality compared with FBP method. However, we should consider significant differences in quantitative indices. And it needs to take an evaluation test to apply clinical study to find a cause of differences out between phantom and clinical results.

• Korean Journal of Radiology
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• v.23 no.11
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• pp.1044-1054
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• 2022

Objective: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods. Materials and Methods: CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%-90% edge rise slope (ERS) and 10%-90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods. Results: DLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) (p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups (p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR. Conclusion: DLR reconstruction provided better images than FBP and hybrid IR reconstruction.

• Korean Journal of Radiology
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• v.23 no.7
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• pp.752-762
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• 2022

Objective: To compare a deep learning-based reconstruction (DLR) algorithm for pediatric abdominopelvic computed tomography (CT) with filtered back projection (FBP) and iterative reconstruction (IR) algorithms. Materials and Methods: Post-contrast abdominopelvic CT scans obtained from 120 pediatric patients (mean age ± standard deviation, 8.7 ± 5.2 years; 60 males) between May 2020 and October 2020 were evaluated in this retrospective study. Images were reconstructed using FBP, a hybrid IR algorithm (ASiR-V) with blending factors of 50% and 100% (AV50 and AV100, respectively), and a DLR algorithm (TrueFidelity) with three strength levels (low, medium, and high). Noise power spectrum (NPS) and edge rise distance (ERD) were used to evaluate noise characteristics and spatial resolution, respectively. Image noise, edge definition, overall image quality, lesion detectability and conspicuity, and artifacts were qualitatively scored by two pediatric radiologists, and the scores of the two reviewers were averaged. A repeated-measures analysis of variance followed by the Bonferroni post-hoc test was used to compare NPS and ERD among the six reconstruction methods. The Friedman rank sum test followed by the Nemenyi-Wilcoxon-Wilcox all-pairs test was used to compare the results of the qualitative visual analysis among the six reconstruction methods. Results: The NPS noise magnitude of AV100 was significantly lower than that of the DLR, whereas the NPS peak of AV100 was significantly higher than that of the high- and medium-strength DLR (p < 0.001). The NPS average spatial frequencies were higher for DLR than for ASiR-V (p < 0.001). ERD was shorter with DLR than with ASiR-V and FBP (p < 0.001). Qualitative visual analysis revealed better overall image quality with high-strength DLR than with ASiR-V (p < 0.001). Conclusion: For pediatric abdominopelvic CT, the DLR algorithm may provide improved noise characteristics and better spatial resolution than the hybrid IR algorithm.

• Journal of the Korean Society of Radiology
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• v.15 no.7
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• pp.1065-1072
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• 2021

In Computed Tomography, abdominal examination showed the highest proportion of use, and effort of reducing the radiation dose is required. Recently introduced Iterative Reconstruction(IR) is repetitive reconstruction technique of Computed Tomography. SIEMENS' IR, ADMIRE and GE's IR, ASIR-V, were used in this examination. Noise, % Contrast, and High contrast resolution were measured by using ACR phantom for image quality evaluation. In addition, CTDI_vol and DLP displayed in the CT device were used for dose evaluation. When FBP and IR were compared, stage 2 to stage 5 of ADMIRE and 10, 30, 50, 70, and 90% of ASIR-V were applied, noise could be reduced from a minimum of 0.46 to a maximum of 2.38 in ADMIRE compared to FBP, and noise from a minimum of 0.51 to a maximum of 2.5 in ASIR-V compared to FBP. Also, % Contrast and High contrast resolution of FBP and IR were no statistical difference. When IR was used for abdominal CT examination, the radiation dose of ADMIRE is reduced by 25.39% compared to the radiation dose of FBP. Also, the radiation dose of ASIR-V is reduced by 16.61% compared to the radiation dose of FBP. In conclusion, it is believed that if IR is applied during abdominal CT examination, the radiation dose can be reduced without deteriorating the image quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5311-5316
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• 2012

Single Photon Emission Computed tomography(SPECT) was performed on 13 healthy adults (average age: 39) to investigate the changes of cerebral blood flow according to brain imaging analysis algorithm. The acquired images were filtered and reconstructed through Filtered Back Projection (FBP) and Ordered Subset Expectation Maximization (OSEM). The brain distribution data of radiopharmaceuticals were compared using Statistical Parametric Mapping (SPM), and the changes of blood flow was expressed in Cluster. As a result, uptake rate was increased in Sub-gyral, Sub-Lobar, Extra-Nuclear, Limbic lobe and Cingulate Gyrus, while uptake rate was decreased in Middle frontal gyrus, Inferior Frontal Gyrus and Precentral Gyrus. The discriminable SPM was shown according to cerebral blood flows in Cluster by the reconstruction algorithm.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.888-893
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• 2021

The International Atomic Energy Agency has developed a tomographic imaging system for accomplishing the total fuel rod-by-rod verification time of fuel assemblies within the order of 1-2 h, however, there are still limitations for some fuel types. The aim of this study is to develop a deep learning-based denoising process resulting in increasing the tomographic image acquisition speed of fuel assembly compared to the conventional techniques. Convolutional AutoEncoder (CAE) was employed for denoising the low-quality images reconstructed by filtered back-projection (FBP) algorithm. The image data set was constructed by the Monte Carlo method with the FBP and ground truth (GT) images for 511 patterns of missing fuel rods. The de-noising performance of the CAE model was evaluated by comparing the pixel-by-pixel subtracted images between the GT and FBP images and the GT and CAE images; the average differences of the pixel values for the sample image 1, 2, and 3 were 7.7%, 28.0% and 44.7% for the FBP images, and 0.5%, 1.4% and 1.9% for the predicted image, respectively. Even for the FBP images not discriminable the source patterns, the CAE model could successfully estimate the patterns similarly with the GT image.