Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System

PURPOSE: To evaluate the diagnostic accuracy of a novel on-site virtual fractional flow reserve (vFFR) derived from coronary computed tomography angiography (CTA). MATERIALS AND METHODS: We analyzed 100 vessels from 57 patients who had undergone CTA followed by invasive FFR during coronary angiography. Coronary lumen segmentation and three-dimensional reconstruction were conducted using a completely automated algorithm, and parallel computing based vFFR prediction was performed. Lesion-specific ischemia based on FFR was defined as significant at ≤0.8, as well as ≤0.75, and obstructive CTA stenosis was defined that ≥50%. The diagnostic performance of vFFR was compared to invasive FFR at both ≤0.8 and ≤0.75. RESULTS: The average computation time was 12 minutes per patient. The correlation coefficient (r) between vFFR and invasive FFR was 0.75 [95% confidence interval (CI) 0.65 to 0.83], and Bland-Altman analysis showed a mean bias of 0.005 (95% CI -0.011 to 0.021) with 95% limits of agreement of -0.16 to 0.17 between vFFR and FFR. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.0%, 87.1%, 72.5%, 58.7%, and 92.6%, respectively, using the FFR cutoff of 0.80. They were 87.0%, 95.0%, 80.0%, 54.3%, and 98.5%, respectively, with the FFR cutoff of 0.75. The area under the receiver-operating characteristics curve of vFFR versus obstructive CTA stenosis was 0.88 versus 0.61 for the FFR cutoff of 0.80, respectively; it was 0.94 versus 0.62 for the FFR cutoff of 0.75. CONCLUSION: Our novel, fully automated, on-site vFFR technology showed excellent diagnostic performance for the detection of lesion-specific ischemia.ope

Viability assessment after conventional coronary angiography using a novel cardiovascular interventional therapeutic CT system: Comparison with gross morphology in a subacute infarct swine model

BACKGROUND: Given the lack of promptness and inevitable use of additional contrast agents, the myocardial viability imaging procedures have not been used widely for determining the need to performing revascularization. OBJECTIVE: This study is aimed to evaluate the feasibility of myocardial viability assessment, consecutively with diagnostic invasive coronary angiography (ICA) without use of additional contrast agent, using a novel hybrid system comprising ICA and multislice CT (MSCT). METHODS: In all, 14 Yucatan miniature swine models (female; age, 3 months; weight, 28-30 kg) were subjected to ICA followed by balloon occlusion (90 minutes) and reperfusion of the left anterior descending coronary artery. Two weeks after induction of myocardial infarction, delayed hyperenhancement (DHE) images were obtained, using a novel combined machine comprising ICA and 320-channel MSCT scanner (Aquilion ONE, Toshiba), after 2, 5, 7, 10, 15, and 20 minutes after conventional ICA. The heart was sliced in 10-mm consecutive sections in the short-axis plane and was embedded in a solution of 1% triphenyltetrazolium chloride (TTC). Infarct size was determined as TTC-negative areas as a percentage of total left ventricular area. On MSCT images, infarct size per slice was calculated by dividing the DHE area by the total slice area (%) and compared with histochemical analyses. RESULTS: Serial MSCT scans revealed a peak CT attenuation of the infarct area (222.5 ± 36.5 Hounsfield units) with a maximum mean difference in CT attenuation between the infarct areas and normal myocardium of at 2 minutes after contrast injection (106.4; P for difference = 0.002). Furthermore, the percentage difference of infarct size by MSCT vs histopathologic specimen was significantly lower at 2 (8.5% ± 1.8%) and 5 minutes (9.5% ± 1.9%) than those after 7 minutes. Direct comparisons of slice-matched DHE area by MSCT demonstrated excellent correlation with TTC-derived infarct size (r = 0.952; P < .001). Bland-Altman plots of the differences between DHE by MSCT and TTC-derived infarct measurements plotted against their means showed good agreement between the 2 methods. CONCLUSION: The feasibility of myocardial viability assessment by DHE using MSCT after conventional ICA was proven in experimental models, and the optimal viability images were obtained after 2 to 5 minutes after the final intracoronary injection of contrast agent for conventional ICA.ope

(A)Study on rotation invariant face detection algorithms

학위논문(박사)--서울대학교 대학원 :전기·컴퓨터공학부,2004.Docto

(The)Effect of exhaustive exercise on GLUT-4 translocation and the phosphorylation of AMPK and ACC-2 in diabetes-induced rats by partial pancreatactomy and STZ

학위논문(박사)--서울대학교 대학원 :체육교육과,2006.Docto

베이즈 추론에 의한 기하학적 관계분석 기반 3차원 CT영상에서 전자동 관상동맥 추출방법

목적- 심혈관계 질환은 세계사망원인 1위로 보고되고 있으며, 심혈관계 질환을 치료하기 위하여 중재시술의 수요가 증가하고 있다. 최근에는 CT영상에서 추출된 환자맞춤형 구조물기반 시뮬레이션기술들이 많이 소개되고 있다. 이러한 기술들은 비침습적으로 환자의 생리적정보를 획득하고, 정량적으로 심혈관질환을 진단을 가능하도록 한다. 그러나, 시뮬레이션기술의 정확도는 CT영상에서 추출되는 심혈관 구조물의 정확도에 의존된다. 또한, 실제 의료현장에서 활용되기 위하여 CT영상으로 부터 사용자의 개입을 최소화하는 자동화 혈관구조 분할 프레임워크 구현이 필수적이다. 해당 학위논문은 CT영상에서 전자동으로 관상동맥을 추출하는 기법에 대해 초점을 맞추고 있다. 특히, 관상동맥 분할기법과 이를 자동화하기 위한 전처리방법도 함께 포함하고 있다. 방법- 관상동맥 분할을 위한 전자동 시스템을 구현하기 위하여 세가지 독립적인 모듈들이 필요하다. 본 학위논문에서는 대동맥의 자동 지역화, 관상동맥 시작점 자동 검출, 그리고 최종적으로 관상동맥 영역과 중심선을 자동 추출해내는 방법들이 포함되어있다. 관상동맥을 효율적이며 정확하게 추출하기 위해 심장의 랜드마크들의 기하학적 관계를 베이즈 추론에 기반한여 새로운 방법들이 제안되었다. 결과- 대동맥 자동 지역화방법은 대부분의 경우에 대해 성공적으로 검출하였고, 99%의 검출률 결과를 얻었다. 좌, 우 관상동맥 입구 (시작점) 자동 검출기법의 경우 좌, 우 각각 97%, 99%의 검출률 결과를 얻었다. 관상동맥의 영역 검출기법에서는 참 값 대비 정확도와 체적 일치도가 측정되었는데, 정확도측정의 경우 0.89의 오차 (Hausdorff distance)가 존재했으며, 체적 일치도 (Jaccard index)의 경우 0.67mm으로 측정되었다. 관상동맥의 중심선을 추적하는 방법에서는 0.92의 겹치는 정도 (overlap)를 보여주었다. 결론- 본 학위논문에서 CT영상에서 관상동맥 자동추출을 위한 세가지의 필수적인 단계에 대한 새로운 기들을 소개하였다. 실험을 통하여 관상동맥 추출 자동화에 있어서 타기법 대비 실패율은 최소화하면서 정확도는 향상 결과를 얻었다. 제안된 기법들을 적용하여 기존 관상동맥 시뮬레이션 프레임워크를 전자동화하고 동시에 정확도를 개선하여 실제임상에서 활용될 수 있다. Purpose- Cardiovascular disease is known to be the leading cause of death in the world, and the demand for interventional procedures for cardiovascular disease is increasing. In recent years, virtual fluid simulation techniques with patient-specific cardiovascular geometries extracted from computed tomography (CT) images have been introduced to diagnose cardiovascular disease before procedures. These techniques allow us to non-invasively obtain physiological information to quantitatively analyze coronary lesions. However, the accuracy of the simulation highly depends on the accuracy of the structure extracted from the CT image. It is necessary to automate the simulation framework to minimize user interactions. This dissertation focuses on the methods of automatically segmenting coronary arteries from CT images. In particular, the coronary artery segmentation method is introduced in detail including a preprocessing step. Methods- Three independent image processing methods are needed to implement a fully-automatic system for coronary artery segmentation. Localization of aorta, identification of coronary ostia and extraction of coronary arteries are the main tasks in this dissertation. To extract coronary arteries, two efficient and accurate approaches are proposed. As useful anatomic information, geometric relations of cardiac anatomies are utilized for our Bayesian formulations. Results- 99% detection rate (true positive rate) is achieved for aorta localization, and 97%, 99% detection rates (true positive rate) are achieved for identification of both right and left coronary ostiums, respectively, based on 130 CT images. 0.89 mm error (Hausdorff distance) and 0.67 0.15 mm for accuracy and similarity (Jaccard index) are achieved for identification of coronary arteries based on the ground truth labeled by a medical expert with 100 CT images. The tracking method to extract coronary artery centerline achieved 0.92 overlap in terms of accuracy. Conclusions- In this dissertation, we introduce the three necessary methods to automatically extract the coronary arteries from CT images. The experimental results show that the proposed methods minimize the failure rate and high accuracy in each step compared to the existing methods. The existing coronary artery simulation frameworks are able to be fully-automated and improved in terms of accuracy through the proposed methods.open박

최대운동부하 검사시 운동강도에 따른 글루타타이온 과산화효소 활성의 변화

학위논문(석사)--서울대학교 대학원 :체육교육과,2002.Maste

Identification of coronary arteries in CT images by Bayesian analysis of geometric relations among anatomical landmarks

We propose a robust method for the identification of coronary arteries in computed tomography angiography (CTA) images. Utilizing geometric relations among the target and reference objects, which are assumed to follow a Gaussian distribution, an anatomic and geometric model is designed by Bayesian inference, which provides robust geometric priors for the target object localization. As a prerequisite process for the identification of coronary arteries, partially broken coronary artery segments found in CTA images are grouped and reconnected by geometric analysis of higher order curves connecting the broken segments. The geometric properties such as curvature and torsion represent naturalness and consistency between the vessel segments. As a problem to identify coronary arteries from CTA images, we demonstrate the robustness and accuracy of the proposed method in comparison with existing methods including commercial workstations on a variety of CTA cases.restrictio

A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA.

We propose a fast seed detection for automatic tracking of coronary arteries in coronary computed tomographic angiography (CCTA). To detect vessel regions, Hessian-based filtering is combined with a new local geometric feature that is based on the similarity of the consecutive cross-sections perpendicular to the vessel direction. It is in turn founded on the prior knowledge that a vessel segment is shaped like a cylinder in axial slices. To improve computational efficiency, an axial slice, which contains part of three main coronary arteries, is selected and regions of interest (ROIs) are extracted in the slice. Only for the voxels belonging to the ROIs, the proposed geometric feature is calculated. With the seed points, which are the centroids of the detected vessel regions, and their vessel directions, vessel tracking method can be used for artery extraction. Here a particle filtering-based tracking algorithm is tested. Using 19 clinical CCTA datasets, it is demonstrated that the proposed method detects seed points and can be used for full automatic coronary artery extraction. ROC (receiver operating characteristic) curve analysis shows the advantages of the proposed method.ope