• 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.

• Journal of radiological science and technology
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• v.25 no.2
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• pp.39-45
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• 2002

Fifty patients with lung mass were studied to evaluate the effect of reconstruction algorithm on the CT number of lung mass and normal thoracic tissues. In each examination, the CT image of the lung mass was reconstructed using soft, standard, detail and bone algorithm. The results were shown as follows 1. the average maximum difference of lung mass density on the ROIs using 4 different algorithms was less than 1HU. 2. The maximum difference in the degree of lung mass enhancement was respectively $0.1{\sim}3.2HU$ (ROI $0.5\;cm^2$), $0.1{\sim}2.8HU$(ROI $3\;cm^2$) and $0.0{\sim}2.1$(ROI $6\;cm^2$). 3. The mean density of the normal thoracic tissues was highest in the bone algorithm, though there was no significant between 4 different reconstruction algorithms(p = 1.00).

• Progress in Medical Physics
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• v.28 no.1
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• pp.27-32
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• 2017

We investigated the effect of a commercial iterative reconstruction technique (iDose, Philips) on the image quality and the dose calculation for the treatment plan. Using the electron density phantom, the 3D CT images with five different protocols (50, 100, 200, 350 and 400 mAs) were obtained. Additionally, the acquired data was reconstructed using the iDose with level 5. A lung phantom was used to acquire the 4D CT with the default protocol as a reference and the low dose (one third of the default protocol) 4D CT using the iDose for the spine and lung plans. When applying the iDose at the same mAs, the mean HU value was changed up to 85 HU. Although the 1 SD was increased with reducing the CT dose, it was decreased up to 4 HU due to the use of iDose. When using the low dose 4D CT with iDose, the dose change relative to the reference was less than 0.5% for the target and OARs in the spine plan. It was also less than 1.1% in the lung plan. Therefore, our results suggests that this dose reduction technique is applicable to the 4D CT image acquisition for the radiation treatment planning.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.240-252
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• 2023

Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.658-663
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• 1999

2D sliced CT images hardly express the human disease in a space. This space expression can be reconstructed into 3D image by piling up the CT sliced image in succession. In medical image, in order to get the reconstructed 3D images, expensive system or much calculation time is needed. But by changing the method of reconstruction procedure and limit the range, the reconstruction time could be reduced. In this study, to reduce the processing time and memory, we suggested a method of interpolation and ray casting processing at the same time in a limited range. Such a limited range processing have advantages that we could reduce the unnecessary interpolation and ray casting. Through a experiment, it is founded that the reconstruction time and the memory was much reduced.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.382-384
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• 2002

Filtered-Back-Projection technique is used in X-ray CT image reconstruction. This requires X-ray transmission data from all directions. As the transverse cross-section of the body is approximately 50 cm, transmitted X-rays in this direction are strongly attenuated. If X-ray transmission data in this direction is avoided, exposure to the patients seems to be reduced one 20th of usual value. Some alternative method has to be found for clinically sufficient image quality. New methods are under development and tentative results are reported that utilizes the principle of superposition.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.159-160
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• 1998

In this study, a three-dimensional geometrical parameterized finite element modeling of the lumbar spine is compared with the 3-D reconstruction model from 2-D CT image. feasibility and accuracy of the parameterized modeling method is evaluated compared with conventional 3-D reconstruction method from 2-D CT image.

• Journal of the Korean Society of Radiology
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• v.13 no.3
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• pp.465-472
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• 2019

To investigate the effects of iterative reconstruction algorithms on fat measurements using computed tomography (CT), we comparatively and quantitatively analyzed the ratios of visceral, subcutaneous, and visceral-subcutaneous fat areas as well as the variations of HU and noise of visceral and subcutaneous fat using ADMIRE strength and attempted to identify any difference between them. Experimental results showed that no statistically significant difference existed among the visceral, subcutaneous, and visceral-subcutaneous fat area ratios HU of visceral fat area and HU of subcutaneous fat area when applying ADMIRE as compared with existing conventional filtered back projection algorithms. However, as the ADMIRE strength increases, the noise of visceral and subcutaneous fat decreases by up to 12.1% and 19.2%, respectively. In conclusion, iterative reconstruction algorithms have no effect on the visceral, subcutaneous, and visceral-subcutaneous fat area ratios, which are indicators of fat measurement using CT.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.651-662
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• 2023

In this study, we designed and fabricated an ultra-compact CT that automatically calibrates the detector's center axis and verified its performance. The three-dimensional reconstruction performance was evaluated using 3D CAD data and X-ray data acquired by manually calibrating the center axis of the CT detector. The results showed that tilting the center axis by more than 0.25° causes circle break phenomenon, which rapidly degrades the quality of the 3D reconstructed image. By applying the automatic calibration device of a detector center axis, the 3D reconstruction performance was enhanced by calibrating the detector center axis to match the specimen rotation axis.