• Korean Journal of Radiology
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• v.21 no.4
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• pp.450-461
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• 2020

Objective: We performed a meta-analysis to evaluate the agreement of cardiac computed tomography (CT) with cardiac magnetic resonance imaging (CMRI) in the assessment of right ventricle (RV) volume and functional parameters. Materials and Methods: PubMed, EMBASE, and Cochrane library were systematically searched for studies that compared CT with CMRI as the reference standard for measurement of the following RV parameters: end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), or ejection fraction (EF). Meta-analytic methods were utilized to determine the pooled weighted bias, limits of agreement (LOA), and correlation coefficient (r) between CT and CMRI. Heterogeneity was also assessed. Subgroup analyses were performed based on the probable factors affecting measurement of RV volume: CT contrast protocol, number of CT slices, CT reconstruction interval, CT volumetry, and segmentation methods. Results: A total of 766 patients from 20 studies were included. Pooled bias and LOA were 3.1 mL (-5.7 to 11.8 mL), 3.6 mL (-4.0 to 11.2 mL), -0.4 mL (5.7 to 5.0 mL), and -1.8% (-5.7 to 2.2%) for EDV, ESV, SV, and EF, respectively. Pooled correlation coefficients were very strong for the RV parameters (r = 0.87-0.93). Heterogeneity was observed in the studies (I² > 50%, p < 0.1). In the subgroup analysis, an RV-dedicated contrast protocol, ≥ 64 CT slices, CT volumetry with the Simpson's method, and inclusion of the papillary muscle and trabeculation had a lower pooled bias and narrower LOA. Conclusion: Cardiac CT accurately measures RV volume and function, with an acceptable range of bias and LOA and strong correlation with CMRI findings. The RV-dedicated CT contrast protocol, ≥ 64 CT slices, and use of the same CT volumetry method as CMRI can improve agreement with CMRI.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1597-1608
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• 2021

Cardiovascular computed tomography (CT) is among the most active fields with ongoing technical innovation related to image acquisition and analysis. Artificial intelligence can be incorporated into various clinical applications of cardiovascular CT, including imaging of the heart valves and coronary arteries, as well as imaging to evaluate myocardial function and congenital heart disease. This review summarizes the latest research on the application of deep learning to cardiovascular CT. The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2003.09a
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• pp.79-79
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• 2003

심실의 내부는 유두근이나 trabecular와 같은 해부학적 구조물들로 인해 복잡한 형태를 띄고 있다. 그러한 복잡한 구조는 MR 영상을 이용한 심박출량 측정시 오차를 유발시킬 수 있으며, 만약 오차를 줄이기 위해 수작업을 하게 된다면 많은 수고와 시간이 필요하게 될 것이다. 본 연구에서는 threshold 기법을 이용하여 짧은 시간동안에 정확하게 복잡한 구조를 가진 심실의 심박출량을 측정하고자 하였다. 7 명의 환자에 대해 l.5T 급 MR 장치 (INTERA, Philips, Netherlands)를 이용하여 short-axis cardiac MR 영상을 획득하였다. 한 환자에 대해서 8개에서 10개의 슬라이스 영상을 8-10 mm의 두께로, 하나의 심장주기(cardiac cycle)동안 일정한 시간간격으로 25 개의 영상을 획득하였으며, 펄스시퀀스로는 ECG-gated segmented balanced fast field echo (TR/TE = 3ms/1.56ms)를 사용하였다. 획득된 영상은 PC(threshold 기법)와 workstation (기존의 수동 및 자동 segmentation 기법)로 DICOM 형태로 전송되었다. 측정은 IDL을 이용하여 자체 제작된 소프트웨어와 상용화된 소프트웨어 (MASS 5.0, MEDIS, Netherlands)를 이용하여 분석되었다. MR 영상에서 심내벽 부위를 추출하기 위하여 자체제작된 소프트웨어로는 threshold 기법을, 상용 소프트웨어로는 기존의 수동 및 자동 기법을 이용하였다. 심박출량은 최대수축기와 이완기 사이의 용적 차이로써 계산되었으며, 좌심실 및 우심실 모두에 대해 수행되었다. 또한, 해부학적 구조의 복잡도에 따른 측정방법의 정확도를 확인하기 위해 유두근 및 trabecular의 hypertrophy의 정도를 3 단계로 구분하고 측정된 값들을 통계적으로 분석하였다. Hypertrophy가 약한 그룹에서는 기존의 수동방식과 threshold 기법간의 의미있는 차이가 없었으며 (p=0.372), 기존의 수동 및 자동방식 간에도 큰 차이가 없었다 (p=0.298). 그러나, hypertrophy가 심한 그룹에서는 수동방식 및 자동방식 측정치 사이에 통계적으로 유의한 차이를 보임을 알 수 있었다 (p=0.044). 그러나, threshold 기법과 수동방식 사이에는 유의한 차이가 없었다 (p=0.l94). 분석시간은 threshold 기법이 기존의 수동방식에 비해서 두배정도 빠르다는 것을 알 수 있었다, Threshold 기법은 심박출량 측정에 있어서 정확하면서도 빠른 결과의 도출이 가능했으며, 특히 심내벽의 구조가 복잡한 경우에 그 효과가 증대됨을 알 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.381-384
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• 2010

심장질환을 예방하기 위하여 정기적인 검진을 통한 심장 운동기능 분석과 관찰이 중요하며, 심장 기능의 분석은 좌심실의 수동윤곽분할을 통하여 혈류량과 심박구출률 계산을 통해 이루어진다. 본 논문에서는 심장단축 자기공명영상에서 좌심실을 자동분할하기 위한 연구에 대하여 설명한다. 관측자의 간섭을 최소화하고 심장기능 분석을 자동화하기 위한 자동 초기점을 추출한 후에, 그래프 탐색을 통하여 복잡한 심장 구조와 다양한 촬영환경에 적용할 수 있는 좌심실 분할 알고리즘을 제안한다. 실험 결과에 따르면 자동 초기점 추출 알고리즘의 성능은 86.8%로 나타났고, 진행 중인 그래프 탐색 알고리즘도 유용한 결과를 나타내고 있다.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.273-281
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• 2010

In this paper, we propose an automatic segmentation method of the endo- and epicardial boundary by using ray-casting profile based on intensity distribution and gradient information in CT images. First, endo-cardial boundary points are detected by using adaptive thresholding and seeded region growing. To include papillary muscles inside the boundary, the endo-cardial boundary points are refined by using ray-casting based profile. Second, epi-cardial boundary points which have both a myocardial intensity value and a maximum gradient are detected by using ray-casting based adaptive gradient profile. Finally, to preserve an elliptical or circular shape, the endo- and epi-cardial boundary points are refined by using elliptical interpolation and B-spline curve fitting. Then, curvature-based contour fitting is performed to overcome problems associated with heterogeneity of the myocardium intensity and lack of clear delineation between myocardium and adjacent anatomic structures. To evaluate our method, we performed visual inspection, accuracy and processing time. For accuracy evaluation, average distance difference and overalpping region ratio between automatic segmentation and manual segmentation are calculated. Experimental results show that the average distnace difference was $0.56{\pm}0.24mm$. The overlapping region ratio was $82{\pm}4.2%$ on average. In all experimental datasets, the whole process of our method was finished within 1 second.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.561-564
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• 2010

의학과 기술 발달로 인해 질병과 사고에 의한 사망률은 줄어들었으나, 심장 관련 질환에 의한 사망률은 증가하였다. 심장 질환을 예방하는 데는 정기적인 검진을 통해 심장기능을 분석하고 관찰하는 것이 중요하다. 심장 기능의 분석은 이완기와 수축기 사이의 혈류량 및 심박구출률 계산을 통한 심장 운동능력 평가에 의해 이루어진다. 본 연구에서는 심장 단축 자기공명영상에서 좌심실 영역을 자동 분할하여 혈류량 및 심박 구출률을 계산하는 알고리즘을 제안한다. K평균 군집화 기법을 적용하여 좌심실을 분할하고, 그래프 탐색 기법에 기반하여 분할 오류를 수정하였다. 15명의 지원자에 대해 제안하는 알고리즘을 사용하여 혈류량과 심박구출률을 계산하였고, 수동윤곽검출 및 General Electronics 사의 MASS 소프트웨어와 비교하였다. 제안한 알고리즘의 수동윤곽검출과 차이는 혈류량의 경우 평균적으로 이완기에 $4.6mL{\pm}3.9$, 수축기에 $2.1mL{\pm}2.4$로 나타났고, 심박구출률은 $1.8%{\pm}1.7$이었다. 전반적으로 MASS소프트웨어에 비해 좋은 성능을 나타내었다.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1251-1259
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• 2022

Objective: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset. Materials and Methods: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed. Results: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951-0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6-42.6 msec); for ECV, r = 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively). Conclusion: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.114-124
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• 2019

Purpose: We investigate biases in the assessments of left ventricular function (LVF), by compressed sensing (CS)-cine magnetic resonance imaging (MRI). Materials and Methods: Cardiovascular cine images with short axis view, were obtained for 8 volunteers without CS. LVFs were assessed with subsampled data, with compression factors (CF) of 2, 3, 4, and 8. A semi-automatic segmentation program was used, for the assessment. The assessments by 3 CS methods (ITSC, FOCUSS, and view sharing (VS)), were compared to those without CS. Bland-Altman analysis and paired t-test were used, for comparison. In addition, real-time CS-cine imaging was also performed, with CF of 2, 3, 4, and 8 for the same volunteers. Assessments of LVF were similarly made, for CS data. A fixed compensation technique is suggested, to reduce the bias. Results: The assessment of LVF by CS-cine, includes bias and random noise. Bias appeared much larger than random noise. Median of end-diastolic volume (EDV) with CS-cine (ITSC or FOCUSS) appeared -1.4% to -7.1% smaller, compared to that of standard cine, depending on CF from (2 to 8). End-systolic volume (ESV) appeared +1.6% to +14.3% larger, stroke volume (SV), -2.4% to -16.4% smaller, and ejection fraction (EF), -1.1% to -9.2% smaller, with P < 0.05. Bias was reduced from -5.6% to -1.8% for EF, by compensation applied to real-time CS-cine (CF = 8). Conclusion: Loss of temporal resolution by adopting missing data from nearby cardiac frames, causes an underestimation for EDV, and an overestimation for ESV, resulting in underestimations for SV and EF. The bias is not random. Thus it should be removed or reduced for better diagnosis. A fixed compensation is suggested, to reduce bias in the assessment of LVF.

• Journal of the Korea Computer Graphics Society
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• v.23 no.1
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• pp.49-55
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• 2017

Computed tomographic angiography (CTA) is widely used in the diagnosis and treatment of coronary artery disease because it shows not only the whole anatomical structure of the cardiovascular three-dimensionally but also provides information on the lesion and type of plaque. However, due to the large size of the image, there is a limitation in manually extracting coronary arteries, and related researches are performed to automatically extract coronary arteries accurately. As the coronary artery originate from the ascending aorta, the ascending aorta and ostium should be detected to extract the coronary tree accurately. In this paper, we propose an automatic segmentation for the ostium as a starting structure of coronary artery in CTA. First, the region of the ascending aorta is initially detected by using Hough circle transform based on the relative position and size of the ascending aorta. Second, the volume of interest is defined to reduce the search range based on the initial area. Third, the refined ascending aorta is segmented by using a two-dimensional geodesic active contour. Finally, the two ostia are detected within the region of the refined ascending aorta. For the evaluation of our method, we measured the Euclidean distance between the result and the ground truths annotated manually by medical experts in 20 CTA images. The experimental results showed that the ostia were accurately detected.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1042-1052
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• 2018

Objective: To determine the utility of computed tomography (CT) ventricular volumes and morphometric parameters for deciding the treatment strategy in children with a hypoplastic left ventricle (LV). Materials and Methods: Ninety-four consecutive children were included in this study and divided into small LV single ventricle repair (SVR) (n = 28), small LV biventricular repair (BVR) (n = 6), disease-matched control (n = 19), and control (n = 41) groups. The CT-based indexed LV volumes, LV-to-right-ventricular (LV/RV) volume ratio, left-to-right atrioventricular valve (AVV) area ratio, left-to-right AVV diameter ratio, and LV/RV long dimension ratio were compared between groups. Proportions of preferred SVR in the small LV SVR group suggested by the parameters were evaluated. Results: Indexed LV end-systolic (ES) and end-diastolic (ED) volumes in the small LV SVR group ($6.3{\pm}4.0mL/m^2$ and $14.4{\pm}10.2mL/m^2$, respectively) were significantly smaller than those in the disease-matched control group ($16.0{\pm}4.7mL/m^2$ and $37.7{\pm}12.0mL/m^2$, respectively; p < 0.001) and the control group ($16.0{\pm}5.5mL/m^2$ and $46.3{\pm}10.8mL/m^2$, respectively; p < 0.001). These volumes were $8.3{\pm}2.4mL/m^2$ and $21.4{\pm}5.3mL/m^2$, respectively, in the small LV BVR group. ES and ED indexed LV volumes of < $7mL/m^2$ and < $17mL/m^2$, LV/RV volume ratios of < 0.22 and < 0.25, AVV area ratios of < 0.33 and < 0.24, and AVV diameter ratios of < 0.52 and < 0.46, respectively, enabled the differentiation of a subset of patients in the small LV SVR group from those in the two control groups. One patient in the small LV biventricular group died after BVR, indicating that this patient might not have been a good candidate based on the suggested cut-off values. Conclusion: CT-based ventricular volumes and morphometric parameters can suggest cut-off values for SVR in children with a hypoplastic LV.