Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the ...clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality.
Abstract
Valvular regurgitation represents an important cause of cardiovascular morbidity and mortality. Imaging is pivotal in the evaluation of native valve regurgitation and echocardiography is the ...primary imaging modality for this purpose. The imaging assessment of valvular regurgitation should integrate quantification of the regurgitation, assessment of the valve anatomy and function, and the consequences of valvular disease on cardiac chambers. In clinical practice, the management of patients with valvular regurgitation largely relies on the results of imaging. It is crucial to provide standards that aim at establishing a baseline list of measurements to be performed when assessing native valve regurgitation. The present document aims to present clinical guidance for the multi-modality imaging assessment of native valvular regurgitation.
Background Visual and histogram-based assessments of coronary CT angiography have limited accuracy in the identification of advanced lesions. Radiomics-based machine learning (ML) could provide a ...more accurate tool. Purpose To compare the diagnostic performance of radiomics-based ML with that of visual and histogram-based assessment of ex vivo coronary CT angiography cross sections to identify advanced atherosclerotic lesions defined with histologic examination. Materials and Methods In this prospective study, 21 coronary arteries from seven hearts obtained from male donors (mean age, 52.3 years ± 5.3) were imaged ex vivo with coronary CT angiography between February 23, 2009, and July 31, 2010. From 95 coronary plaques, 611 histologic cross sections were coregistered with coronary CT cross sections. Lesions were considered advanced if early fibroatheroma, late fibroatheroma, or thin-cap atheroma was present. CT cross sections were classified as showing homogeneous, heterogeneous, or napkin-ring sign plaques on the basis of visual assessment. The area of low attenuation (<30 HU) and the average Hounsfield unit were quantified. Radiomic parameters were extracted and used as inputs to ML algorithms. Eight radiomics-based ML models were trained on randomly selected cross sections (training set, 75% of the cross sections) to identify advanced lesions. Visual assessment, histogram-based assessment, and the best ML model were compared on the remaining 25% of the data (validation set) by using the area under the receiver operating characteristic curve (AUC) to identify advanced lesions. Results After excluding sections with no visible plaque (
= 134) and with heavy calcium (
= 32), 445 cross sections were analyzed. Of those 445 cross sections, 134 (30.1%) were advanced lesions. Visual assessment of the 445 cross sections indicated that 207 (46.5%) were homogeneous, 200 (44.9%) were heterogeneous, and 38 (8.5%) demonstrated the napkin-ring sign. A radiomics-based ML model incorporating 13 parameters outperformed visual assessment (AUC = 0.73 with 95% confidence interval CI of 0.63, 0.84 vs 0.65 with 95% CI of 0.56, 0.73, respectively;
= .04), area of low attenuation (AUC = 0.55 with 95% CI of 0.42, 0.68;
= .01), and average Hounsfield unit (AUC = 0.53 with 95% CI of 0.42, 0.65;
= .004) in the identification of advanced atheromatous lesions. Conclusion Radiomics-based machine learning analysis improves the discriminatory power of coronary CT angiography in the identification of advanced atherosclerotic lesions. Published under a CC BY 4.0 license.
This study sought to determine whether coronary computed tomography angiography (CCTA)-based radiomic analysis of pericoronary adipose tissue (PCAT) could distinguish patients with acute myocardial ...infarction (MI) from patients with stable or no coronary artery disease (CAD).
Imaging of PCAT with CCTA enables detection of coronary inflammation. Radiomics involves extracting quantitative features from medical images to create big data and identify novel imaging biomarkers.
In a prospective case-control study, 60 patients with acute MI underwent CCTA within 48 h of admission, before invasive angiography. These subjects were matched to patients with stable CAD (n = 60) and controls with no CAD (n = 60) by age, sex, risk factors, medications, and CT tube voltage. PCAT was segmented around the proximal right coronary artery (RCA) in all patients and around culprit and nonculprit lesions in patients with MI. PCAT segmentations were analyzed using Radiomics Image Analysis software.
Of 1,103 calculated radiomic parameters, 20.3% differed significantly between MI patients and controls, and 16.5% differed between patients with MI and stable CAD (critical p < 0.0006); whereas none differed between patients with stable CAD and controls. On cluster analysis, the most significant radiomic parameters were texture or geometry based. At 6 months post-MI, there was no significant change in the PCAT radiomic profile around the proximal RCA or nonculprit lesions. Using machine learning (XGBoost), a model integrating clinical features (risk factors, serum lipids, high-sensitivity C-reactive protein), PCAT attenuation, and radiomic parameters provided superior discrimination of acute MI (area under the receiver operator characteristic curve AUC: 0.87) compared with a model with clinical features and PCAT attenuation (AUC: 0.77; p = 0.001) or clinical features alone (AUC: 0.76; p < 0.001).
Patients with acute MI have a distinct PCAT radiomic phenotype compared with patients with stable or no CAD. Using machine learning, a radiomics-based model outperforms a PCAT attenuation-based model in accurately identifying patients with MI.
This meta-analysis determined the diagnostic performance of coronary computed tomography (CT) angiography (CTA), CT myocardial perfusion (CTP), fractional flow reserve CT (FFR
), the transluminal ...attenuation gradient (TAG), and their combined use with CTA versus FFR as a reference standard for detection of hemodynamically significant coronary artery disease (CAD).
CTA provides excellent anatomic, albeit limited functional information for the evaluation of CAD. Recently, various functional CT techniques emerged to assess the hemodynamic consequences of CAD.
This meta-analysis was performed in adherence to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, EMBASE, and Web of Science were searched from inception until September 7, 2017. Bayesian random effects analysis was used to compute pooled sensitivity, specificity, and the summary receiver-operating characteristic curve of the index tests and compare them with the FFR as a reference standard. Analyses were performed on vessel and patient levels. Because CTA has excellent sensitivity, specificity was considered most relevant. Individual FFR
values were collected.
Overall, 54 articles and 5,330 patients were included. At vessel level, pooled specificity of CTP (0.86; 95% confidence interval CI: 0.76 to 0.93), FFR
(0.78; 95% CI: 0.72 to 0.83) and TAG (0.77; 95% CI: 0.61 to 0.89) were substantially higher than that of CTA (0.61; 95% CI: 0.54 to 0.68). The addition of FFR
CTP, and TAG to CTA resulted in high to excellent specificities (0.80 to 0.92). The summary receiver-operating characteristic curve at vessel level yielded superior diagnostic accuracy for CTP, FFR
, and combined CTA and CTP, compared with CTA. A subanalysis of on-site versus off-site FFR
revealed no substantial differences between the sensitivity (0.84 vs. 0.85) and specificity (0.80 vs. 0.73) of the 2 techniques. In a second subanalysis, dynamic CTP showed higher sensitivity (0.85 vs. 0.72), but had a lower specificity (0.81 vs. 0.90) than static CTP.
CTP and FFR
demonstrated a substantial improvement in the identification of hemodynamically significant CAD compared with CTA; therefore, their integration to clinical workflow before revascularization is recommended.
BACKGROUNDThe nature of input data is an essential factor when training neural networks. Research concerning magnetic resonance imaging (MRI)-based diagnosis of liver tumors using deep learning has ...been rapidly advancing. Still, evidence to support the utilization of multi-dimensional and multi-parametric image data is lacking. Due to higher information content, three-dimensional input should presumably result in higher classification precision. Also, the differentiation between focal liver lesions (FLLs) can only be plausible with simultaneous analysis of multi-sequence MRI images. AIMTo compare diagnostic efficiency of two-dimensional (2D) and three-dimensional (3D)-densely connected convolutional neural networks (DenseNet) for FLLs on multi-sequence MRI. METHODSWe retrospectively collected T2-weighted, gadoxetate disodium-enhanced arterial phase, portal venous phase, and hepatobiliary phase MRI scans from patients with focal nodular hyperplasia (FNH), hepatocellular carcinomas (HCC) or liver metastases (MET). Our search identified 71 FNH, 69 HCC and 76 MET. After volume registration, the same three most representative axial slices from all sequences were combined into four-channel images to train the 2D-DenseNet264 network. Identical bounding boxes were selected on all scans and stacked into 4D volumes to train the 3D-DenseNet264 model. The test set consisted of 10-10-10 tumors. The performance of the models was compared using area under the receiver operating characteristic curve (AUROC), specificity, sensitivity, positive predictive values (PPV), negative predictive values (NPV), and f1 scores. RESULTSThe average AUC value of the 2D model (0.98) was slightly higher than that of the 3D model (0.94). Mean PPV, sensitivity, NPV, specificity and f1 scores (0.94, 0.93, 0.97, 0.97, and 0.93) of the 2D model were also superior to metrics of the 3D model (0.84, 0.83, 0.92, 0.92, and 0.83). The classification metrics of FNH were 0.91, 1.00, 1.00, 0.95, and 0.95 using the 2D and 0.90, 0.90, 0.95, 0.95, and 0.90 using the 3D models. The 2D and 3D networks' performance in the diagnosis of HCC were 1.00, 0.80, 0.91, 1.00, and 0.89 and 0.88, 0.70, 0.86, 0.95, and 0.78, respectively; while the evaluation of MET lesions resulted in 0.91, 1.00, 1.00, 0.95, and 0.95 and 0.75, 0.90, 0.94, 0.85, and 0.82 using the 2D and 3D networks, respectively. CONCLUSIONBoth 2D and 3D-DenseNets can differentiate FNH, HCC and MET with good accuracy when trained on hepatocyte-specific contrast-enhanced multi-sequence MRI volumes.
Different visceral fat compartments have several systemic effects and may play a role in the development of both insulin resistance and cardiovascular diseases. In the last couple of years special ...attention has been paid to the epicardial adipose tissue (EAT), which can be quantified by non-invasive cardiac imaging techniques. The epicardial fat is a unique fat compartment between the myocardium and the visceral pericardium sharing a common embryologic origin with the visceral fat depot. Epicardial adipose tissue has several specific roles, and its local effects on cardiac function are incorporated in the complex pathomechanism of coronary artery disease. Importantly, EAT may produce several adipocytokines and chemokines that may influence - through paracrine and vasocrine effects - the development and progression of coronary atherosclerosis. Epicardial adipose tissue volume has a relatively strong genetic dependence, similarly to other visceral fat depots. In this article, the anatomical and physiological as well as pathophysiological characteristics of the epicardial fat compartment are reviewed.
Most acute coronary syndromes are caused by sudden luminal thrombosis due to atherosclerotic plaque rupture or erosion. Preventing such an event seems to be the only effective strategy to reduce ...mortality and morbidity of coronary heart disease. Coronary lesions prone to rupture have a distinct morphology compared with stable plaques, and provide a unique opportunity for noninvasive imaging to identify vulnerable plaques before they lead to clinical events. The submillimeter spatial resolution and excellent image quality of modern computed tomography (CT) scanners allow coronary atherosclerotic lesions to be detected, characterized, and quantified. Large plaque volume, low CT attenuation, napkin-ring sign, positive remodelling, and spotty calcification are all associated with a high risk of acute cardiovascular events in patients. Computation fluid dynamics allow the calculation of lesion-specific endothelial shear stress and fractional flow reserve, which add functional information to plaque assessment using CT. The combination of morphologic and functional characteristics of coronary plaques might enable noninvasive detection of vulnerable plaques in the future.
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