• Progress in Medical Physics
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• 제14권1호
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• pp.34-42
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• 2003

In medical imaging, three-dimensional (3D) display using Virtual Reality Modeling Language (VRML) as a portable file format can give intuitive information more efficiently on the World Wide Web (WWW). The web-based 3D visualization of functional images combined with anatomical images has not studied much in systematic ways. The goal of this study was to achieve a simultaneous observation of 3D anatomic and functional models with planar images on the WWW, providing their locational information in 3D space with a measuring implement using VRML. MRI and ictal-interictal SPECT images were obtained from one epileptic patient. Subtraction ictal SPECT co-registered to MRI (SISCOM) was performed to improve identification of a seizure focus. SISCOM image volumes were held by thresholds above one standard deviation (1-SD) and two standard deviations (2-SD). SISCOM foci and boundaries of gray matter, white matter, and cerebrospinal fluid (CSF) in the MRI volume were segmented and rendered to VRML polygonal surfaces by marching cube algorithm. Line profiles of x and y-axis that represent real lengths on an image were acquired and their maximum lengths were the same as 211.67 mm. The real size vs. the rendered VRML surface size was approximately the ratio of 1 to 605.9. A VRML measuring tool was made and merged with previous VRML surfaces. User interface tools were embedded with Java Script routines to display MRI planar images as cross sections of 3D surface models and to set transparencies of 3D surface models. When transparencies of 3D surface models were properly controlled, a fused display of the brain geometry with 3D distributions of focal activated regions provided intuitively spatial correlations among three 3D surface models. The epileptic seizure focus was in the right temporal lobe of the brain. The real position of the seizure focus could be verified by the VRML measuring tool and the anatomy corresponding to the seizure focus could be confirmed by MRI planar images crossing 3D surface models. The VRML application developed in this study may have several advantages. Firstly, 3D fused display and control of anatomic and functional image were achieved on the m. Secondly, the vector analysis of a 3D surface model was defined by the VRML measuring tool based on the real size. Finally, the anatomy corresponding to the seizure focus was intuitively detected by correlations with MRI images. Our web based visualization of 3-D fusion image and its localization will be a help to online research and education in diagnostic radiology, therapeutic radiology, and surgery applications.

• Pediatric Infection and Vaccine
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• 제30권2호
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• pp.73-83
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• 2023

Purpose: This study aimed to investigate the viral load dynamics in children with underlying malignancies diagnosed with symptomatic coronavirus disease 2019 (COVID-19). Methods: This was a retrospective longitudinal cohort study of patients <19 years old with underlying hemato-oncologic malignancies that were diagnosed with their first symptomatic severe acute respiratory syndrome coronavirus 2 polymerase chain reaction (PCR)-confirmed COVID-19 infection during March 1 to August 30, 2022. Review of electronic medical records and telephone surveys were undertaken to assess the clinical presentations and transmission route of the patients. Thresholds of negligible likelihood of infectious virus was defined as E gene reverse transcription (RT)-PCR cycle threshold (Ct) value ≥25. Results: During the 6-month study period, a total of 43 children with 44 episodes of COVID-19 were included. Of the 44 episodes, the median age of the patients included was 8 years old (interquartile range [IQR], 4.9-10.5), and the most common underlying disease was acute lymphoid leukemia (n=30, 68.2%), followed by patients post-hematopoietic stem cell transplantation (n=8, 18.2%). Majority of the patients had mild COVID-19 (n=32, 72.7%), and three patients (7.0%) had severe/critical COVID-19. Furthermore, 2.3% (n=1) died of COVID-19 associated acute respiratory distress syndrome. The largest percentage of the patients showed E gene RT-PCR Ct value ≥25 between 15-21 days (n=13, 39.4%), followed by 22-28 days (n=10, 30.3%). In 15.2% (n=5), E gene RT-PCR Ct value remained <25 beyond 28 days after initial positive PCR. Refractory malignancy status (β, 67.0; 95% confidence interval, 7.0-17.0; P=0.030) was significantly associated with prolonged duration of E gene RT-PCR <25. A patient with prolonged duration of E gene RT-PCR Ct value <25 was suspected to have infectivity shown by the transmission of the virus to his mother at day 86 after his initial positive test. Conclusions: Children that acquire symptomatic COVID-19 during refractory malignancy state are at a high risk for prolonged shedding warranting PCR-based transmission precautions in this cohort of patients.

• Journal of Intelligence and Information Systems
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• 제16권3호
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.