• Korean Journal of Radiology
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• 제22권5호
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• pp.688-698
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• 2021

Objective: To compare the lumen parameters measured by the location-adaptive threshold method (LATM), in which the inter- and intra-scan attenuation variabilities of coronary computed tomographic angiography (CCTA) were corrected, and the scan-adaptive threshold method (SATM), in which only the inter-scan variability was corrected, with the reference standard measurement by intravascular ultrasonography (IVUS). Materials and Methods: The Hounsfield unit (HU) values of whole voxels and the centerline in each of the cross-sections of the 22 target coronary artery segments were obtained from 15 patients between March 2009 and June 2010, in addition to the corresponding voxel size. Lumen volume was calculated mathematically as the voxel volume multiplied by the number of voxels with HU within a given range, defined as the lumen for each method, and compared with the IVUS-derived reference standard. Subgroup analysis of the lumen area was performed to investigate the effect of lumen size on the studied methods. Bland-Altman plots were used to evaluate the agreement between the measurements. Results: Lumen volumes measured by SATM was significantly smaller than that measured by IVUS (mean difference, 14.6 mm³; 95% confidence interval [CI], 4.9-24.3 mm³); the lumen volumes measured by LATM and IVUS were not significantly different (mean difference, -0.7 mm³; 95% CI, -9.1-7.7 mm³). The lumen area measured by SATM was significantly smaller than that measured by LATM in the smaller lumen area group (mean of difference, 1.07 mm²; 95% CI, 0.89-1.25 mm²) but not in the larger lumen area group (mean of difference, -0.07 mm²; 95% CI, -0.22-0.08 mm²). In the smaller lumen group, the mean difference was lower in the Bland-Altman plot of IVUS and LATM (0.46 mm²; 95% CI, 0.27-0.65 mm²) than in that of IVUS and SATM (1.53 mm²; 95% CI, 1.27-1.79 mm²). Conclusion: SATM underestimated the lumen parameters for computed lumen segmentation in CCTA, and this may be overcome by using LATM.

• 정보과학회 논문지
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• 제43권4호
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• pp.419-429
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• 2016

최근 관상동맥 영역화 결과로부터 삼차원 표면 모델을 생성함으로써 혈관 구조적 정보의 렌더링 효율성의 증대뿐만 아니라 전산유체역학를 이용한 혈류 역학 시뮬레이션을 통해 혈류분획예비력과 같은 생리적 정보들을 획득하는 연구들이 활발히 진행되고 있다. 본 논문에서는 혈관 영역화 과정에서 획득한 혈관 구조 정보를 입력 데이터로 사용하여 관상동맥의 삼차원 삼각 표면 메쉬 모델을 생성하는 방법을 제안한다. 관상동맥 영역화 결과로부터 삼각형 표면 메쉬 모델을 만드는 방법으로는 Marching cube 알고리즘에 기반한 방법들이 있지만 이산적인 영상 공간에서 수행되는 알고리즘으로 가늘고 다양한 굴곡을 갖는 혈관 경계를 표현하기 힘들다. 제안된 방법은 관상동맥 영역화 과정에서 추정한 혈관 중심좌표와 법선 벡터 그리고 직경 정보를 이용하여 기존 방법들보다 정교하게 단일 혈관 가닥들에 대한 삼각 표면 메쉬들을 생성하고 분기가 일어나 중첩되는 메쉬들은 메쉬 병합 기법을 사용하여 처리함으로써 통합된 관상동맥 메쉬를 생성한다.

• Korean Journal of Radiology
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• 제22권2호
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• pp.168-178
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• 2021

Objective: To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). Materials and Methods: Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. Results: The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). Conclusion: The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

• ETRI Journal
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• 제39권5호
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• pp.662-671
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• 2017

Coronary artery disease (CAD) is a major cause of death in the world. As a non-invasive imaging modality, computed tomography angiography (CTA) is now usually used in clinical practice for CAD diagnosis. Precise quantification of coronary stenosis is of great interest for diagnosis and treatment planning. In this paper, a novel cluster method based on a Modified Student's t-Mixture Model is applied to separate the region of vessel lumen from other tissues. Then, the area of the vessel lumen in each slice is computed and the estimated value of it is fitted with a curve. Finally, the location and the level of the most stenoses are captured by comparing the calculated and fitted areas of the vessel. The proposed method has been applied to 17 clinical CTA datasets and the results have been compared with reference standard degrees of stenosis defined by an expert. The results of the experiment indicate that the proposed method can accurately quantify the stenosis of the coronary artery in CTA.

• 대한의용생체공학회:의공학회지
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• 제38권6호
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• pp.295-301
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• 2017

In this study, We subtracted the calcification blooming artifact from MDCT images of coronary atherosclerosis patients and verified their accuracy and usefulness. We performed coronary artery calcification stenosis phantom and a program to subtract calcification blooming artifact by applying 8 different image segmentation method (Otsu, Sobel, Prewitt, Canny, DoG, Region Growing, Gaussian+K-mean clustering, Otsu+DoG). As a result, In the coronary artery calcification stenosis phantom with the lumen region 5 mm the calcification blooming artifact was subtracted in the application of the mixture of Gaussian filtering and K- Clustering algorithm, and the value was close to the actual calcification region. These results may help to accurately diagnose coronary artery calcification stenosis.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권12호
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• pp.1247-1254
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• 2003

현재 경동맥 내막절제술 시행을 위한 경동맥 협착증의 정도 측정에는 디지털감산조영술(DSA), 회전조영술(rotational angiography), 컴퓨터단층조영술(CTA) 및 자기공명조영술(MRA)로부터 얻어진 경동맥의 투영 영상을 이용하여 북미, 유럽 표준 및 총경동맥 방법이 사용되고 있다. 본 논문에서는 기존의 기계적인 측경기를 이용하는 전형적인 경동맥 협착 측정 방법의 단점을 극복하고, 측정자간의 변화율을 최소화하기 위해 자기공명조영술의 단면 영상을 사용하고 컴퓨터화한 새로운 협착증 정도 측정 방법을 개발하였다. 영상 분할에 사용되는 방법중 가장 널리 사용되고 효율적인 명암값 임계치 방법을 사용하여 경동맥 및 동맥의 내강을 분할하였다. 또한, 각 증례의 측정된 총경동맥의 혈관두께를 사용하여 분할된 경동맥으로부터 혈관을 제거 하였고, 혈관이 제거된 경동맥을 혈류 영역과 플라그 영역으로 분할하였다. 각 단면 영상에서의 경동맥 협착증 정도 측정은 (분할된 플라그 영역/혈류영역 및 플라그를 합한 면적) * 100% 식으로 계산된다.