관상동맥 석회화 CT에서 측정한 대동맥 전개: 저위험 환자군에서의 정상 범위

Purpose: This study aimed to assess the factors influencing aortic unfolding (AU) defined by aortic width on coronary artery calcium (CAC) scan and determine the normal limits for AU. Materials and methods: In this retrospective study, we measured AU in 924 asymptomatic subjects who underwent CAC scanning during routine health screening from June 2015 to June 2018. Multivariate regression analysis was used to evaluate the factors influencing AU. After the exclusion of subjects with risk factors associated with AU, 283 subjects were included in the analysis of normal values of AU. Mean AU, standard deviation, and upper normal limit were calculated. Results: Sex, age, CAC score, body mass index, body surface area, hypertension, left ventricular hypertrophy, plasma creatinine, and smoking were significantly associated with AU. The mean AU was 102.2 ± 12.8 mm for men and 93.1 ± 10.7 mm for women. AU increased with advancing age (9.6 mm per decade). Conclusion: AU determined from a single measurement on CAC scans was associated with cardiovascular risk factors. The normal limits of AU were defined by age, sex, and body surface area in low-risk subjects in this study.ope

Cardiac CT

Multislice computed tomography (CT) is emerging technology that enables imaging the moving heart with high resolution. The current technology of CT is represented by 64-slice CT. CT is becoming the first-line evaluation tool for the detection of significant coronary artery stenosis and is applied for the detection of plaque composition and functional imaging. Significant coronary artery stenosis can be detected with a high accuracy over 90% and can be reliably excluded with a high negative predictive value approaching 100% by using 64-slice CT. CT coronary angiography is recommended not only to exclude significant stenosis in patients with equivocal symptoms or intermediate results on stress test but also to assess obstructive disease in symptomatic patients. Quantification of coronary artery calcium with CT is helpful to select patients for lipid-lowering therapies, who have intermediate coronary artery disease risk. With technical improvement, spatial and temporal resolution of CT will reach the level enough to establish the diagnoses of in-stent restenosis, plaque composition, and ventricular and valvular function in the foreseeable future. Myocardial imaging including myocardial perfusion and viability may be possible without increasing radiation exposure. CT is a very promising technology for cardiac imaging because, with technical improvement, clinical benefits are expected to be greater than the risk of radiation exposure. This short review is for readers ① to understand CT technology for cardiac imaging, ② to understand the limitation of current technology of CT for cardiac imaging, ③ to learn the current application of CT in cardiac diseases, ④ to get a perspective on the future directions of cardiac CT.ope

Myocardial T2* Imaging at 3T and 1.5T: A Pilot Study with Phantom and Normal Myocardium

Background: Myocardial T2* mapping at 1.5T remains the gold standard, but the use of 3T scanners is increasing. We aimed to determine the conversion equations in different scanners with clinically available, vendor-provided T2* mapping sequences using a phantom and evaluated the feasibility of the phantom-based conversion method. Methods: T2* of a phantom with FeCl3 (five samples, 3.53-20.09 mM) were measured with 1.5T (MR-A1) and 3T scanners (MR-A2, A3, B), and the site-specific equation was determined. T2* was measured in the interventricular septum of three healthy volunteers at 1.5T (T2*1.5T, MR-A1) and 3T (T2*3.0T, MR-B). T2*3.0T was converted based on the equation derived from the phantom (T2*eq). Results: R2* at 1.5T and 3T showed linear association, but a different relationship was observed according to the scanners (MR-A2, R2*1.5T = 0.76 × R2*3.0T - 2.23, R2 = 0.999; MR-A3, R2*1.5T = 0.95 × R2*3.0T - 34.28, R2 = 0.973; MR-B, R2*1.5T = 0.76 × R2*3.0T - 3.02, R2 = 0.999). In the normal myocardium, T2*eq and T2*1.5T showed no significant difference (35.5 ± 3.5 vs. 34.5 ± 1.2, p = 0.340). The mean squared error between T2*eq and T2*1.5T was 16.33, and Bland-Altman plots revealed a small bias (-0.94, 95% limits of agreement: -8.86-6.99). Conclusions: a phantom-based, site-specific equation can be utilized to estimate T2* values at 1.5T in centers where only 3T scanners are available.ope

How to Develop, Validate, and Compare Clinical Prediction Models Involving Radiological Parameters: Study Design and Statistical Methods.

Clinical prediction models are developed to calculate estimates of the probability of the presence/occurrence or future course of a particular prognostic or diagnostic outcome from multiple clinical or non-clinical parameters. Radiologic imaging techniques are being developed for accurate detection and early diagnosis of disease, which will eventually affect patient outcomes. Hence, results obtained by radiological means, especially diagnostic imaging, are frequently incorporated into a clinical prediction model as important predictive parameters, and the performance of the prediction model may improve in both diagnostic and prognostic settings. This article explains in a conceptual manner the overall process of developing and validating a clinical prediction model involving radiological parameters in relation to the study design and statistical methods. Collection of a raw dataset; selection of an appropriate statistical model; predictor selection; evaluation of model performance using a calibration plot, Hosmer-Lemeshow test and c-index; internal and external validation; comparison of different models using c-index, net reclassification improvement, and integrated discrimination improvement; and a method to create an easy-to-use prediction score system will be addressed. This article may serve as a practical methodological reference for clinical researchers.ope

Artificial Intelligence in Health Care: Current Applications and Issues

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.ope

Differentiation of Acute Myocardial Infarction from Chronic Myocardial Scar with MRI

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MDCT Application for Coronary Artery Intervention

Multidetector computed tomography (MDCT) has recently been used as a diagnostic tool for the evaluation of coronary artery morphology and stenosis. The accuracy of MDCT has improved as the number of detectors of MDCT has increased. A 64-MDCT reliably detects significant coronary artery stenosis with a sensitivity and specificity higher than 90%. With its high negative predictive value near 100%, 64-MDCT is very practical for excluding significant coronary artery disease and avoiding unnecessary invasive coronary angiography. Furthermore, preprocedural MDCT coronary angiography is useful to provide additional information and predict the procedural outcomes particularly in patients who have chronic total occlusion and those referred for percutaneous coronary intervention. Postprocedural MDCT coronary angiography usually involves evaluation of in-stent restenosis. Recently, drug-eluting stents are widely used and has notably reduced the rate of in-stent restenosis. However, the rate of in-stent restenosis of drug-eluting stents are still 5~10%. Considering the large number of patients who receive coronary artery stents, MDCT would be clinically useful as a noninvasive tool for the reliable detection of in-stent restenosis. Even with 64-MDCT, 30~40% of stents are not evaluable because the spatial and temporal resolutions are not sufficient for the detection of in-stent restenosis. With the 64-MDCT technology, the accessibility of in-stent restenosis mainly depends on stent size and severity of metal artifact of stents. Although the current MDCT does not permit reliable detection of in-stent restenosis, MDCT can be accepted as a first-line alternative to coronary angiography for the evaluation of stents, especially those with a large diameter such as left main coronary artery stents.ope

Phantom-based correction for standardization of myocardial native T1 and extracellular volume fraction in healthy subjects at 3-Tesla cardiac magnetic resonance imaging

Objectives: To investigate the effect of the phantom-based correction method for standardizing myocardial native T1 and extracellular volume fraction (ECV) in healthy subjects. Methods: Seventy-one healthy asymptomatic adult (≥ 20 years) volunteers of five different age groups (34 men and 37 women, 45.5 ± 15.5 years) were prospectively enrolled in three academic hospitals. Cardiac MRI including Modified Look - Locker Inversion recovery T1 mapping sequence was performed using a 3-Tesla system with a different type of scanner for each hospital. Native T1 and ECV were measured in the short-axis T1 map and analyzed for mean values of the 16 entire segments. The myocardial T1 value of each subject was corrected based on the site-specific equation derived from the T1 Mapping and ECV Standardization phantom. The global native T1 and ECV were compared between institutions before and after phantom-based correction, and the variation in native T1 and ECV among institutions was assessed using a coefficient of variation (CoV). Results: The global native T1 value significantly differed between the institutions (1198.7 ± 32.1 ms, institution A; 1217.7 ± 39.9 ms, institution B; 1232.7 ± 31.1 ms, institution C; p = 0.002), but the mean ECV did not (26.6-27.5%, p = 0.355). After phantom-based correction, the global native T1 and ECV were 1289.7 ± 32.4 ms and 25.0 ± 2.7%, respectively, and CoV for native T1 between the three institutions decreased from 3.0 to 2.5%. The corrected native T1 value did not significantly differ between institutions (1284.5 ± 31.5 ms, institution A; 1296.5 ± 39.1 ms, institution B; 1291.3 ± 29.3 ms, institution C; p = 0.440), and neither did the ECV (24.4-25.9%, p = 0.078). Conclusions: The phantom-based correction method can provide standardized reference T1 values in healthy subjects. Key points: • After phantom-based correction, the global native T1 of 16 entire myocardial segments on 3-T cardiac MRI is 1289.4 ± 32.4 ms, and the extracellular volume fraction was 25.0 ± 2.7% for healthy subjects. • After phantom - based correction was applied, the differences in the global native T1 among institutions became insignificant, and the CoV also decreased from 3.0 to 2.5%.ope

Korean Society of Cardiovascular Imaging Guidelines for Cardiac Computed Tomography

The Korean Society of Cardiovascular Imaging (KOCSI) has issued a guideline for the use of cardiac CT imaging in order to assist clinicians and patients in providing adequate level of medical service. In order to establish a guideline founded on evidence based medicine, it was designed based on comprehensive data such as questionnaires conducted in international and domestic hospitals, intensive journal reviews, and with experts in cardiac radiology. The recommendations of this guideline should not be used as an absolute standard and medical professionals can always refer to methods non-adherent to this guideline when it is considered more reasonable and beneficial to an individual patient's medical situation. The guideline has its limitation and should be revised appropriately with the advancement medical equipment technology and public health care system. The guideline should not be served as a measure for standard of care. KOCSI strongly disapproves the use of the guideline to be used as the standard of expected practice in medical litigation processesope

Regional Amyloid Burden Differences Evaluated Using Quantitative Cardiac MRI in Patients with Cardiac Amyloidosis

Objective: This study aimed to investigate the regional amyloid burden and myocardial deformation using T1 mapping and strain values in patients with cardiac amyloidosis (CA) according to late gadolinium enhancement (LGE) patterns. Materials and methods: Forty patients with CA were divided into 2 groups per LGE pattern, and 15 healthy subjects were enrolled. Global and regional native T1 and T2 mapping, extracellular volume (ECV), and cardiac magnetic resonance (CMR)-feature tracking strain values were compared in an intergroup and interregional manner. Results: Of the patients with CA, 32 had diffuse global LGE (group 2), and 8 had focal patchy or no LGE (group 1). Global native T1, T2, and ECV were significantly higher in groups 1 and 2 than in the control group (native T1: 1384.4 ms vs. 1466.8 ms vs. 1230.5 ms; T2: 53.8 ms vs. 54.2 ms vs. 48.9 ms; and ECV: 36.9% vs. 51.4% vs. 26.0%, respectively; all, p < 0.001). Basal ECV (53.7%) was significantly higher than the mid and apical ECVs (50.1% and 50.0%, respectively; p < 0.001) in group 2. Basal and mid peak radial strains (PRSs) and peak circumferential strains (PCSs) were significantly lower than the apical PRS and PCS, respectively (PRS, 15.6% vs. 16.7% vs. 26.9%; and PCS, -9.7% vs. -10.9% vs. -15.0%; all, p < 0.001). Basal ECV and basal strain (2-dimensional PRS) in group 2 showed a significant negative correlation (r = -0.623, p < 0.001). Group 1 showed no regional ECV differences (basal, 37.0%; mid, 35.9%; and apical, 38.3%; p = 0.184). Conclusion: Quantitative T1 mapping parameters such as native T1 and ECV may help diagnose early CA. ECV, in particular, can reflect regional differences in the amyloid deposition in patients with advanced CA, and increased basal ECV is related to decreased basal strain. Therefore, quantitative CMR parameters may help diagnose CA and determine its severity in patients with or without LGE.ope