• Journal of the Korean Society of Radiology
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• v.84 no.6
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• pp.1324-1336
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• 2023

Purpose This study aimed to compare the diagnostic performance of cardiac CT and transthoracic echocardiogram (TTE) depending on the degree of valvular calcification and bicuspid aortic valve (BAV) subtype. Materials and Methods This retrospective study included 266 consecutive patients (106 with BAV and 160 with tricuspid aortic valve) who underwent cardiac CT and TTE before aortic valve replacement. Cardiac CT was used to evaluate the morphology of the aortic valve, and a calcium scoring scan was used to quantify valve calcium. The aortic valves were classified into fused and two-sinus types. The diagnostic accuracy of cardiac CT and TTE was calculated using a reference standard for intraoperative inspection. Results CT demonstrated significantly higher sensitivity, negative predictive value, and accuracy than TTE in detecting BAV (p < 0.001, p < 0.001, and p = 0.003, respectively). The TTE sensitivity tended to decrease as valvular calcification increased. The error rate of TTE for CT was 10.9% for the twosinus type of BAV and 28.3% for the fused type (p = 0.044). Conclusion Cardiac CT had a higher diagnostic performance in detecting BAV than TTE and may help diagnose BAV, particularly in patients with severe valvular calcification.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.12-17
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• 2023

Aortic valve stenosis is a heart valve disease caused by the accumulation of calcium in the valve, which can divide into tricuspid aortic valve (TAV) stenosis and bicuspid aortic valve (BAV) stenosis depending on the shape of natural valve. In this study, pig heart-based TAV and BAV ex vivo models were fabricated, and the flow characteristics behind a valve were analyzed using 4D flow MRI. Flow behind normal TAV was uniformly distributed, while BAV asymmetrically opened with an eccentric strong jet. Especially, BAV ex vivo model exhibited a secondary flow in the region where the valve closed. In addition, BAV had a 26% higher peak velocity while maintaining similar stroke volume compared with normal TAV. This study would be helpful for understanding the flow characteristics for BAV AS patients.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.334-343
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• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• Journal of Chest Surgery
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• v.22 no.6
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• pp.1001-1012
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• 1989

In 1968, Carpentier and his associates introduced glutaraldehyde as a compound for preparing cardiac tissue valve, and this technique has provided a considerably more suitable and durable tissue valve substitute. To increase further durability of valve tissue, Reis and his colleagues designed a flexible stent to reduce the stress on the heterogeneous tissue valve mounted. However with the advent of more innovative mechanical valve currently, many bioprosthetic valves are being substituted by mechanical valves at our department of cardiothoracic surgery because of bioprosthetic valve failure. Main cause of bioprosthetic valves failure were calcification or/and tear of tissue valves. The purpose of this retrospective study is to clarify the relationship between the patients clinical profile during implantation of tissue valves and pathologic features of the failed bioprosthetic valve. From March, 1982 through June, 1988, 53 bioprosthetic heart valves that had been ex-planted from 45 patients at the department of cardiac surgery of Yonsei University Hospital were subjected to this study. The patients were 10 to 65 year-old [mean age: 30.3 yr] with 17 males and 28 females. Re-replacements of prosthetic valves were carried out twenty nine in mitral position, eight in aortic position and eight in both aortic and mitral position simultaneously. The grading and location for calcification of valves were verified by radiograms. The calcification of the explanted valves leaflets was graded from 0 to 4 plus according to Cipriano and associates method. The types of tear and perforation of leaflet were classified into four types as Ishihara has adopted initially in 1981. In younger age group under thirty three years, explanted tissue valves were significantly more affected in terms of grades of severity of valve calcification as compared with older age group [p < 0.035]. Valve calcification appeared more severe in male as compared to female [p< 0.002]. Ionescu-Shiley bovine pericardial bioprosthetic valves showed more severe calcification than Hancock porcine tissue valves [p< 0.035]. Calcium deposit was found very prevalent at the area of commissural attachment [86 % of all]. Type I of valve rupture was shown to be related with simultaneous calcification. However, the relation of explanted valve position, duration of implanted prosthetic valve, atrial fibrillation and anticoagulant therapy to the severity of bioprosthetic valve calcification were not significantly clear statistically [p > 0.05].

• Journal of Chest Surgery
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• v.21 no.6
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• pp.979-983
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• 1988

Calcium channel blockers may prevent myocardial injury during cardioplegia and reperfusion. This study was done to evaluate the effects of diltiazem cardioplegia on myocardial protection during ischemic arrest and recovery of myocardial function after reperfusion. Four formulations of crystalloid cardioplegic solutions, GIK solution[group I, n=12], diltiazem[lug/ml GIK] in GIK solution[group II, n=7], ],diltiazem[2ug/ml GIK] in GIK solution[group III, n=6] and diltiazem[4ug/ml GIK] in GIK solution[group IV, n=6] were compared in isolated working rat heart subjected to a long period [2 hours] of hypothermic arrest with multi-dose infusion. Diltiazem cardioplegia[group II, III and IV]was found to be superior in nearly all aspects. Diltiazem cardioplegia showed faster recovery of regular rhythm and lower incidence of ventricular fibrillation than group I did. In comparing mechanical function in all experimental hearts, the mean postischemic recoveries of aortic flow, cardiac output, peak aortic pressure, stroke volume and stroke work[expressed as a percentage of its preischemic control] were significantly greater in group II, III and IV[diltiazem cardioplegia] than in group I. The infused amount of cardioplegic solution was more increased by the addition of diltiazem to GI K solution. [p < 0.01] Creatine kinase leakage tended to be lower in hearts receiving diltiazem cardioplegia, especially in group III and IV[p<0.05] than in those receiving GIK solution only[group I]. Diltiazem cardioplegia results in the increased flow of cardioplegic solution and the decreased ischemic injury of myocardium during ischemic arrest and the improved recovery of myocardial function after reperfusion, and a dose-response relation must be established before clinical use.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1764-1776
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• 2021

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.