• Tunnel and Underground Space
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• v.24 no.2
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• pp.155-165
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• 2014

Multiple thermal energy storage (TES) caverns can be used for storing thermal energy on a large scale and for a high-aspect-ratio heat storage design to provide good thermal performance. It may also be necessary to consider the use of multiple caverns with a reduced length when a single, long tunnel-shaped cavern is not suitable for connection to aboveground heat production and injection equipments. When using multiple TES caverns, the separation distance between the caverns is one of the significant factors that should be considered in the design of storage space, and the optimal separation distance should be determined based on a quantitative stability criterion. In this paper, we described a numerical approach for determining the optimal separation distance between multiple caverns for large-scale TES utilization. For reliable stability evaluation of multiple caverns, we employed a probabilistic method which can quantitatively take into account the uncertainty of input parameters by probability distributions, unlike conventional deterministic approaches. The present approach was applied to the design of a conceptual TES model to store hot water for district heating. The probabilistic stability results of this application demonstrated that the approach in our work can be effectively used as a decision-making tool to determine the optimal separation distance between multiple caverns. In addition, the probabilistic results were compared to those obtained through a deterministic analysis, and the comparison results suggested that care should taken in selecting the acceptable level of stability when using deterministic approaches.

• Journal of Chest Surgery
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• v.40 no.1 s.270
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• pp.45-51
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• 2007

Background: Although echocardiography is usually used for quantitative assessment of left ventricular function, the recently developed 16-slice multidetector computed tomography (MDCT) is not only capable of evaluating the coronary arteries but also left ventricular function. Therefore, the objective of our study was to compare the values of left ventricular function quantified by MDCT to those by echocardiography for evaluation of its regards to clinical applications. Material and Method: From 49 patients who underwent MDCT in our hospital from November 1, 2003 to January 31, 2005, we enrolled 20 patients who underwent echocardiography during the same period for this study. Left ventricular end-diastolic volume index (LVEDVI), left ventricular end-systolic volume index (LVESVI), stroke volume index (SVI), left ventricular mass index (LVMI), and ejection fraction (EF) were analyzed. Result: Average LVEDVI ($80.86{\pm}34.69mL$ for MDCT vs $60.23{\pm}29.06mL$ for Echocardiography, p<0.01), average LVESVI ($37.96{\pm}24.52mL$ for MDCT vs $25.68{\pm}16.57mL$ for Echocardiography, p<0.01), average SVI ($42.90{\pm}15.86mL$ for MDCT vs $34.54{\pm}17.94mL$ for Echocardiography, p<0.01), average LVMI ($72.14{\pm}25.35mL$ for MDCT vs $130.35{\pm}53.10mL$ for Echocardiography, p<0.01), and average EF ($55.63{\pm}12.91mL$ for MOCT vs $59.95{\pm}12.75ml$ for Echocardiography, p<0.05) showed significant difference between both groups. Average LVEDVI, average LVESVI, and average SVI were higher in MDCT, and average LVMI and average EF were higher in echocardiogram. Comparing correlation for each parameters between both groups, LVEDVI $(r^2=0.74,\;p<0.0001)$, LVESVI $(r^2=0.69,\;p<0.0001)$ and SVI $(r^2=0.55,\;p<0.0001)$ showed high relevance, LVMI $(r^2=0.84,\;p<0.0001)$ showed very high relevance, and $EF (r^2=0.45,\;p=0.0002)$ showed relatively high relevance. Conclusion: Quantitative assessment of left ventricular volume and function using 16-slice MDCT showed high relevance compared with echocardiography, therefore may be a feasible assessment method. However, because the average of each parameters showed significant difference, the absolute values between both studies may not be appropriate for clinical applications. Furthermore, considering the future development of MDCT, we expect to be able to easily evaluate the assessment of coronary artery stenosis along with left ventricular function in coronary artery disease patients.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.1
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• pp.58-64
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• 2011

Objective: The 3-dimensional reconstruction method with resolution recovery modeling has advantages of high spatial resolution and contrast because of its precise modeling of spatial blurring according to the distance from detector plane. The aim of this study was to evaluate one of the resolution recovery reconstruction methods (Astonish, Philips Medical), compare it to other iterative reconstructions, and verify its clinical usefulness. Materials and Methods: NEMA IEC PET body phantom and Flanges Jaszczak ECT phantom (Data Spectrum Corp., USA) studies were performed using Skylight SPECT (Philips) system under four different conditions; short or long (2 times of short) radius, and half or full (40 kcts/frame) acquisition counts. Astonish reconstruction method was compared with two other iterative reconstructions; MLEM and 3D-OSEM which vendor supplied. For quantitative analysis, the contrast ratios obtained from IEC phantom test were compared. Reconstruction parameters were determined by optimization study using graph of contrast ratio versus background variability. The qualitative comparison was performed with Jaszczak ECT phantom and human myocardial data. Results: The overall contrast ratio was higher with Astonish than the others. For the largest hot sphere of 37 mm diameter, Astonish showed about 27.1% and 17.4% higher contrast ratio than MLEM and 3D-OSEM, in short radius study. For long radius, Astonish showed about 40.5% and 32.6% higher contrast ratio than MLEM and 3D-OSEM. The effect of acquired counts was insignificant. In the qualitative studies with Jaszczak phantom and human myocardial data, Astonish showed the best image quality. Conclusion: In this study, we have found out that Astonish can provide more reliable clinical results by better image quality compared to other iterative reconstruction methods. Although further clinical studies are required, Astonish would be used in clinics with confidence for enhancement of images.

• The Korean Journal of Nuclear Medicine Technology
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• v.20 no.2
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• pp.3-8
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• 2016

Purpose SUV is one of the parameters that assist diagnosis in origin, metastasis and staging of cancer. Specially, it is important to compare SUV before and after chemo or radiation therapy to find out effectiveness of treatment. Storing PET data which has no quantitative change is needed for SUV comparison. However, there is a possibility to loss the data in external hard drive or MINIpacs that are managed by department of nuclear medicine. The aim of this study is to evaluate SUV and metabolic tumor volume (MTV) among reconstructed data (R-D) in workstation, R-D and re-sliced data (S-D) in PACS. Materials and Methods Data of 20 patients (aged $60.5{\pm}8.3y$) underwent $^{18}F-FDG$ PET (Biograph truepoint 40, mCT 40, mCT 64, mMR, Siemens) study were analysed. $SUV_{max}$, $SUV_{peak}$ and MTV were measured in liver, aorta and tumor after sending R-D in workstation, R-D and S-D in PACS to syngo.via software. Results R-D of workstation and PACS showed the same value as mean $SUV_{max}$ in liver, aorta and tumor were $2.95{\pm}0.59$, $2.35{\pm}0.61$, $10.36{\pm}6.15$ and $SUV_{peak}$ were $2.70{\pm}0.51$, $2.07{\pm}0.43$, $7.67{\pm}3.73$(p>0.05) respectively. Mean $SUV_{max}$ of S-D in PACS were decreased by 5.18%, 7.22%, 12.11% and $SUV_{peak}$ 2.61%, 3.63%, 10.07%(p<0.05). Correlation between R-D and S-D were $SUV_{max}$ 0.99, 0.96, 0.99 and $SUV_{peak}$ 0.99, 0.99, 0.99. And 2SD in balnd-altman analysis were $SUV_{max}$ 0.125, 0.290, 1.864 and $SUV_{peak}$ 0.053, 0.103, 0.826. MTV of R-D in workstation and PACS show the same value as $14.21{\pm}12.72cm^3$(p>0.05). MTV in PACS was decreased by 0.12% compared to R-D(p>0.05). Correlation and 2SD between R-D and S-D were 0.99 and 2.243. Conclusion $SUV_{max}$, $SUV_{peak}$, MTV showed the same value in both of R-D in workstation and PACS. However, there was statistically difference in $SUV_{max}$, $SUV_{peak}$ of S-D compare to R-D despite of high correlation. It is possible to analyse reliable pre and post SUV if storing R-D in main hospital PACS system.