• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.2
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• pp.55-64
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• 2017

Purpose Recently, with the spread of SPECT/CT, various image correction methods can be applied quickly and accurately, which enabled us to expect quantitative accuracy as well as image quality improvement. Among them, the Collimator Detector Response(CDR) recovery is a correction method aiming at resolution recovery by compensating the blurring effect generated from the distance between the detector and the object. The purpose of this study is to find out quantitative change depending on the change in detection distance in SPECT/CT images with CDR recovery applied. Materials and Methods In order to find out the error of acquisition count depending on the change of detection distance, we set the detection distance according to the obit type as X, Y axis radius 30cm for circular, X, Y axis radius 21cm, 10cm for non-circular and non-circular auto(=auto body contouring, ABC_spacing limit 1cm) and applied reconstruction methods by dividing them into Astonish(3D-OSEM with CDR recovery) and OSEM(w/o CDR recovery) to find out the difference in activity recovery depending on the use of CDR recovery. At this time, attenuation correction, scatter correction, and decay correction were applied to all images. For the quantitative evaluation, calibration scan(cylindrical phantom, $^{99m}TcO_4$ 123.3 MBq, water 9293 ml) was obtained for the purpose of calculating the calibration factor(CF). For the phantom scan, a 50 cc syringe was filled with 31 ml of water and a phantom image was obtained by setting $^{99m}TcO_4$ 123.3 MBq. We set the VOI(volume of interest) in the entire volume of the syringe in the phantom image to measure total counts for each condition and obtained the error of the measured value against true value set by setting CF to check the quantitative accuracy according to the correction. Results The calculated CF was 154.28 (Bq/ml/cps/ml) and the measured values against true values in each conditional image were analyzed to be circular 87.5%, non-circular 90.1%, ABC 91.3% and circular 93.6%, non-circular 93.6%, ABC 93.9% in OSEM and Astonish, respectively. The closer the detection distance, the higher the accuracy of OSEM, and Astonish showed almost similar values regardless of distance. The error was the largest in the OSEM circular(-13.5%) and the smallest in the Astonish ABC(-6.1%). Conclusion SPECT/CT images showed that when the distance compensation is made through the application of CDR recovery, the detection distance shows almost the same quantitative accuracy as the proximity detection even under the distant condition, and accurate correction is possible without being affected by the change in detection distance.

• The Korean Journal of Nuclear Medicine Technology
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• v.28 no.1
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• pp.23-29
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• 2024

Purpose: 99mTc-mebrofenin hepatobiliary scintigraphy (HBS) is an important and clinically useful diagnostic imaging study for detecting complications after liver transplantation. CT contrast agents due to their high atomic numbers, lead to a decrease in gamma ray count rates. This study investigated the impact of CT contrast agents on the uptake of 99mTc-mebrofenin in the liver. Materials and Methods: The quantitative HBS was performed on sixty-two liver transplantation patients (male:female=36:26), with a mean age of 59.4±6.4 years. Statistical comparison of hepatic uptake reduction ratio (HURR%) before and after the injection of CT contrast agents was performed using a paired t-test. Results: Hepatic uptake of the reduction ratio was 94.47±3.65% for the pre-CT contrast agents and 92.17±4.00% for the post-CT contrast agents. HURR% after CT contrast agent injection showed a statistically significant difference compared to before the injection (t=11.09, P<0.001). Conclusion: It will be necessary to pay attention when examining the HBS of patients with liver transplantation after the injection of CT contrast medium. It is advisable to schedule the examination on a different day to prevent residual contrast medium in the body from interfering with the quantitative evaluation of the nuclear medicine examination.

• Journal of the Korean Society of Radiology
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• v.12 no.5
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• pp.593-601
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• 2018

Recently in nuclear medicine, to improve diagnostic value, SUV, semi-quantitative indicator used in PET-CT, is adopted in SPECT-CT. Therefore, this research analyzed correlation of $SUV_{max}$ of two scanners through phantom test, and evaluated possibility of clinical application. Radiopharmaceuticals ($^{18}F$, $^{99m}Tc$) were injected with the ratios of 8:1 and 4:1, considering background radioactivity, into the phantom manufactured with 6 globes of different sizes, and, based on clinical protocol, positive phases were acquired with PET-CT and SPECT-CT scanners, and interesting areas were divided into ROI and VOI, and $SUV_{max}$ of them were measured, and analyzed. Tests found out no statistically significant difference in $SUV_{max}$ measured with two scanners (P>0.05). Thus, $SUV_{max}$ of PET-CT and SPECT-CT had a certain correlation within significant levels, and were evaluated as the same. Accordingly, it seems that $SUV_{max}$ quantitative analysis using SPECT-CT can provide significant diagnostic information as the case of PET-CT.

• Imaging Science in Dentistry
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• v.44 no.3
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• pp.177-183
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• 2014

Recent research in endodontics has highlighted the need for three-dimensional imaging in the clinical arena as well as in research. Three-dimensional imaging using computed tomography (CT) has been used in endodontics over the past decade. Three types of CT scans have been studied in endodontics, namely cone-beam CT, spiral CT, and peripheral quantitative CT. Contemporary endodontics places an emphasis on the use of cone-beam CT for an accurate diagnosis of parameters that cannot be visualized on a two-dimensional image. This review discusses the role of CT in endodontics, pertaining to its importance in the diagnosis of root canal anatomy, detection of periradicular lesions, diagnosis of trauma and resorption, presurgical assessment, and evaluation of the treatment outcome.

• Journal of the Korean Society of Radiology
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• v.82 no.4
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• pp.808-816
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• 2021

Pulmonary emphysema is a cause of chronic obstructive pulmonary disease. Emphysema can be accurately diagnosed via CT. The severity of emphysema can be assessed using visual interpretation or quantitative analysis. Various studies on emphysema using deep learning have also been conducted. Although the classification of emphysema has proven clinically useful, there is a need to improve the reliability of the measurement.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.869-870
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• 2008

• Korean Journal of Radiology
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• v.21 no.9
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• pp.1095-1103
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• 2020

Objective: The present study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters offer an incremental risk stratification benefit over the CT ventricular diameter ratio in patients with acute pulmonary embolism (PE) by using propensity score analysis. Materials and Methods: This study was conducted on 480 patients with acute PE who underwent CT pulmonary angiography (CTPA) or DECT pulmonary angiography (DE CT-PA). This propensity-matched study population included 240 patients with acute PE each in the CTPA and DECT groups. Altogether, 260 (54.1%) patients were men, and the mean age was 64.9 years (64.9 ± 13.5 years). The primary endpoint was all-cause death within 30 days. The Cox proportional hazards regression model was used to identify associations between CT parameters and outcomes and to identify potential predictors. Concordance (C) statistics were used to compare the prognoses between the two groups. Results: In both CTPA and DECT groups, right to left ventricle diameter ratio ≥ 1 was associated with an increased risk of all-cause death within 30 days (hazard ratio: 3.707, p < 0.001 and 5.573, p < 0.001, respectively). However, C-statistics showed no statistically significant difference between the CTPA and DECT groups for predicting death within 30 days (C-statistics: 0.759 vs. 0.819, p = 0.117). Conclusion: Quantitative measurement of lung perfusion defect volume by DECT had no added benefit over CT ventricular diameter ratio for predicting all-cause death within 30 days.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.69-75
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• 2014

Purpose: The aim of this study was to evaluate changes of quantitative and semi-quantitative myocardial perfusion indices and image quality by image reconstruction methods in $^{13}N$-ammonia ($^{13}N-NH_3$) myocardial perfusion PET/CT. Materials and Methods: Data of 14 (8 men, 6 women) patients underwent rest and adenosine stress $^{13}N-NH_3$ PET/CT (Biograph TruePoint 40 with TrueV, Siemens) were collected. Listmode scans were acquired for 10 minutes by injecting 370MBq of $^{13}N-NH_3$. Dynamic and static reconstruction was performed by use of FBP, iterative2D (2D), iterative3D (3D) and iterative TrueX (TrueX) algorithm. Coronary flow reserve (CFR) of dynamic reconstruction data, extent(%) and total perfusion deficit (TPD) (%) measured in sum of 4-10 minutes scan were evaluated by comparing with 2D method which was recommended by vendor. The image quality of each reconstructed data was compared and evaluated by five nuclear medicine physicians through a blind test. Results: CFR were lower in TrueX 18.68% (P=0.0002), FBP 4.35% (P=0.1243) and higher in 3D 7.91% (P<0.0001). As semi-quantitative values, extent and TPD of stress were higher in 3D 3.07%p (P=0.001), 2.36%p (P=0.0002), FBP 1.93%p (P=0.4275), 1.57%p (P=0.4595), TrueX 5.43%p (P=0.0003), 3.93%p (P<0.0001). Extent and TPD of rest were lower in FBP 0.86%p (P=0.1953), 0.57%p (P=0.2053) and higher in 3D 3.21%p (P=0.0006), 2.57%p (P=0.0001) and TrueX 5.36%p (P<0.0001), 4.36%p (P<0.0001). Based on the results of the blind test for image resolution and noise from the snapshot, 3D obtained the highest score, followed by 2D, TrueX and FBP. Conclusion: We found that quantitative and semi-quantitative myocardial perfusion values could be under- or over-estimated according to the reconstruction algorithm in $^{13}N-NH_3$ PET/CT. Therefore, proper dynamic and static reconstruction method should be established to provide accurate myocardial perfusion value.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1248-1259
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• 2016

Classification methods based on dual energy X-ray absorptiometry, ultrasonic waves, and quantitative computed tomography have been proposed. Also, a classification method based on machine learning with bone mineral density and structural indicators extracted from the CT images has been proposed. We propose a method which enhances the performance of existing classification method based on bone mineral density and structural indicators by extending structural indicators and using principal component analysis. Experimental result shows that the proposed method in this paper improves the correctness of osteoporosis classification 2.8% with extended structural indicators only and 4.8% with both extended structural indicators and principal component analysis. In addition, this paper proposes a method of automatic phantom analysis needed to convert the CT values to BMD values. While existing method requires manual operation to mark the bone region within the phantom, the proposed method detects the bone region automatically by detecting circles in the CT image. The proposed method and the existing method gave the same conversion formula for converting CT value to bone mineral density.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.