• The Korean Journal of Physiology and Pharmacology
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• 제16권6호
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• pp.387-392
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• 2012

In this study, we examined the antinociceptive effect of Cyperi rhizoma (CR) and Corydalis tuber (CT) extracts using a chronic constriction injury-induced neuropathic pain rat model. After the ligation of sciatic nerve, neuropathic pain behavior such as mechanical allodynia and thermal hyperalgesia were rapidly induced and maintained for 1 month. Repeated treatment of CR or CT (per oral, 10 or 30 mg/kg, twice a day) was performed either in induction (day 0~5) or maintenance (day 14~19) period of neuropathic pain state. Treatment of CR or CT at doses of 30 mg/kg in the induction and maintenance periods significantly decreased the nerve injury-induced mechanical allodynia. In addition, CR and CT at doses of 10 or 30 mg/kg alleviated thermal heat hyperalgesia when they were treated in the maintenance period. Finally, CR or CT (30 mg/kg) treated during the induction period remarkably reduced the nerve injury-induced phosphorylation of NMDA receptor NR1 subunit (pNR1) in the spinal dorsal horn. Results of this study suggest that extracts from CR and CT may be useful to alleviate neuropathic pain.

• Asian Pacific Journal of Cancer Prevention
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• 제13권4호
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• pp.1575-1578
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• 2012

Objectives: To obtain permeability surface (PS) values using multi-slice helical CT perfusion imaging and to evaluate the spatial distribution correlation between PS values and vascular endothelial growth factor (VEGF) expression in solitary brain metastases. Methods: Imaging was performed on 21 patients, PS values being calculated from the central, border and peripheral parts of tumours. VEGF expression was determined by immunohistochemical staining. Results: Rim enhancement was found in 16 cases, the border of the tumour featuring PS elevation with high VEGF expression in 13 cases. In the 5 cases with nodular enhancement, the border and the central part had high permeability and VEGF expression was high in all cases, the correlation being significant (P<0.01). Conclusion: VEGF expression in brain metastases positively correlates with PS values from CT perfusion imaging, so that the latter can be used in the surveillance of angiogenic activity in brain metastases.

• 한국응급구조학회지
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• 제24권2호
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• pp.99-109
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• 2020

Purpose: Alcohol intoxication is frequently observed in patients with brain hemorrhage. The purpose of this study was to determine whether intoxication affects the Korean Triage and Acuity Stage (KTAS) level and the emergency medical process in emergency departments. Methods: This study was a retrospective observational study enrolled 253 brain hemorrhage patients (47 of those intoxicated) who visited the emergency medical center on public EMS ambulance from January. 1, 2017 to April, 30, 2019. Data were collected through the electronic medical record (EMR). KTAS level and time to computerized tomography (CT) were compared to evaluate whether inebriation affects care and examination processes. All data were analyzed using SPSS program. Results: Of the 47 patients intoxicated patients, 85.1% were male, and 74.5% accompanied by trauma. Initial KTAS level showed significant differences (77.2%; p=.000) when the level 3,4 was not drunk. The average time taken from triage to CT scans showed a significant difference of 24.81±23.72 (min) when the drunken state was not 58.38±56.54 (min)(p=.000). Conclusion: In patients with brain hemorrhage admitted to ED from public EMS, undertriage and delay after initial assessment were detected in inebriated patients. Careful initial evaluation and prompt medical response should be considered for patients transported by EMS.

• 대한핵의학회지
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• 제28권1호
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• pp.17-21
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• 1994

경련을 주소로 내원하여 뇌혈류 SPECT와 MRI 또는 CT, 뇌파도가 시행되었던 115명을 대상으로 경련의 원인과 뇌혈류 SPECT의 유형을 비교하고자 하였다. 대상환자 115명중 28명이 기질적인 원인에 의한 경련이었으며 42명이 전신성 경련, 45명이 부분적 발작이었다. 전신성 경련환자의 뇌혈류 SPECT 유형은 22명이 정상이었고 8명이 미만성의 혈류감소, 4명이 전두엽, 5명이 측두엽, 3명이 전두엽과 측두엽에 걸친 혈류감소를 보였다. 부분적 발작환자 54명중 19명이 뇌파도와 일치하는 부위에 혈류감소를 보였다. 결론적으로 간질 발작간의 뇌혈류 SPECT는 기질적인 원인을 비교적 예민하게 찾을 수 있었고, 전신성 경련환자에서는 정상이거나 육안적으로 경미한 정도의 전두엽 또는 측두엽의 혈류감소를 보였다. 이러한 혈류 감소가 전신성 경련환자와 부분적 발작 환자에서 인지기능이나 항경련제 등과의 연관성에 대해서는 더욱 연구가 되어야 할 것이다.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2014년도 추계 종합학술대회 논문집
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• pp.191-192
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• 2014

두부 CT 검사 시 FBP와 ASIR적용에 따른 화질과 선량을 평가해 보고, Scan type(Axial, Helical)별 영상을 비교해 보아 두부 CT 영상획득 시 최적의 방법을 알아보고자 하였다.

• Tuberculosis and Respiratory Diseases
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• 제80권4호
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• pp.368-376
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• 2017

Background: A ground-glass nodule (GGN) represents early-stage lung adenocarcinoma. However, there is still no consensus for preoperative staging of GGNs. Therefore, we evaluated the need for the routine use of positron emission tomography/computed tomography (PET)/computed tomography (CT) scans and brain magnetic resonance imaging (MRI) during staging. Methods: A retrospective analysis was undertaken in 72 patients with 74 GGNs of less than 3 cm in diameter, which were confirmed via surgery as malignancy, at the Samsung Medical Center between May 2010 and December 2011. Results: The median age of the patients was 59 years. The median GGN diameter was 18 mm. Pure and part-solid GGNs were identified in 35 (47.3%) and 39 (52.7%) cases, respectively. No mediastinal or distant metastasis was observed in these patients. In preoperative staging, all of the 74 GGNs were categorized as stage IA via chest CT scans. Additional PET/CT scans and brain MRIs classified 71 GGNs as stage IA, one as stage IIIA, and two as stage IV. However, surgery and additional diagnostic work-ups for abnormal findings from PET/CT scans classified 70 GGNs as stage IA, three as stage IB, and one as stage IIA. The chest CT scans did not differ from the combined modality of PET/CT scans and brain MRIs for the determination of the overall stage (94.6% vs. 90.5%; kappa value, 0.712). Conclusion: PET/CT scans in combination with brain MRIs have no additional benefit for the staging of patients with GGN lung adenocarcinoma before surgery.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• 대한한의학회지
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• 제23권4호
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• pp.91-97
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• 2002

Objective: This study was conducted to know the survival probability of the patients with cerebrovascular disease. Method: 1,341 patients who were suspected of having cerebrovascular disease clinically were investigated by telephone and NHIC (National Health Insurance Corporation) data. Conclusion: 1. The study population was grouped as 'Negative Brain CT findings' (11.8%), 'Hemorrhage' (12.4%) and 'Infarction' (75.7%). 2. The survival probabilities calculated by the Life Table method were statistically significant among brain CT finding groups (P<0.01). 3. The mean survival time calculated by the Kaplan-Meier method were also statistically significant among brain CT finding groups (P<0.01). 4. The result of Cox regression model was that sex (OR=0.7), age (OR=1.07), diabetes mellitus (OR=1.38), and heart disease (OR=1.69) affected the survival of the patients with cerebrovascular disease.

• 한국방사선학회논문지
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• 제13권5호
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• pp.705-711
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• 2019

현재 최신 장비의 두부 CT검사에서 파라메타의 변화와 선량변화에 대한 연구가 부족하고 특히 노이즈, 균일도 해석 및 선량변화에 대한 연구가 부족하다고 생각된다. 따라서 높은 사양 두부 CT 사용 시 노출 파라메타 중 관전압, 슬라이스 두께, 피치변화에 대해 분석하여 이때 발생하는 현상에 대해서 연구하고자 하였다. 실험을 통해서 균일도는 고관전압과 두꺼운 슬라이스 선택 및 최저 피치를 사용할 때 균일도가 좋음을 알 수 있었다. 모두 조합한 결과 균일도가 가장 조건은 140 kVp, 10 mm, pitch 0.5로 나타났다. 노이즈는 관전압과 슬라이스 두께를 높이면 피치에 관계없이 개선되는 것을 알 수 있었고, 선량은 관전압과 피치의 증가에 따라 선형적으로 증가하는 것으로 나타났다. 따라서 본 연구결과는 고 사양의 두부 CT 사용에서 참고자료가 될 것이다.

• 한국방사선학회논문지
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• 제16권1호
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• pp.25-34
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• 2022

뇌 전산화단층촬영은 비침습성, 3차원 영상 제공, 저방사선량 등의 장점 때문에 뇌출혈과 같은 질병 진단을 위해 시행된다. 하지만 뇌 전산화단층영상 판독을 위한 전문의의 인력 공급 부족 및 막대한 업무량으로 인해 수많은 판독 오류 및 오진이 발생하고 있다. 이와 같은 문제를 해결하기 위해 객체 검출을 위한 다양한 인공지능 기술이 개발되고 있다. 본 연구에서는 뇌 전산화단층영상으로부터 뇌출혈 검출을 위한 딥러닝 기반 YOLOv5s 모델의 적용 가능성을 확인하였다. 또한 YOLOv5s 모델 학습 시 초매개변수를 변화시켜 학습된 모델의 성능을 평가하였다. YOLOv5s 모델은 backbone, neck 및 output 모듈로 구성하였고, 입력 CT 영상 내 뇌출혈로 의심되는 부위를 검출하여 출력할 수 있도록 하였다. YOLOv5s 모델 학습 시 활성화함수, 최적화함수, 손실함수 및 학습 횟수를 변화시켰고, 학습된 모델의 뇌출혈 검출 정확도 및 학습 시간을 측정하였다. 연구결과 학습된 YOLOv5s 모델은 뇌출혈로 의심되는 부위에 대한 경계 박스 및 해당 경계박스에 대한 정확도를 출력할 수 있음을 확인하였다. Mish 활성화함수, stochastic gradient descent 최적화함수 및 completed intersection over union 손실함수 적용 시 YOLOv5s 모델의 뇌출혈 검출 정확도 향상 및 학습 시간이 단축되는 결과를 확인하였다. 또한 YOLOv5s 모델의 뇌출혈 검출 정확도 및 학습 시간은 학습 횟수에 비례하여 증가하는 결과를 확인하였다. 따라서 YOLOv5s 모델은 뇌 전산화단층영상을 이용한 뇌출혈 검출을 위해 활용할 수 있으며, 최적의 초매개변수 적용을 통해 성능을 향상 시킬 수 있다.