396 research outputs found

    Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

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    In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted and clustered encodings. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis.Comment: This paper was submitted in April 2017 and accepted in November 2017 for publication in Medical Image Analysis. Please cite as: Zreik et al., Medical Image Analysis, 2018, vol. 44, pp. 72-8

    Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography

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    In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis that require ICA, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries using a support vector machine classifier. The detection of coronary arteries requiring invasive evaluation, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of 0.81±0.020.81 \pm 0.02 on the artery-level, and 0.87±0.020.87 \pm 0.02 on the patient-level. The results demonstrate the feasibility of automatic non-invasive detection of patients that require ICA and possibly subsequent coronary artery intervention. This could potentially reduce the number of patients that unnecessarily undergo ICA.Comment: This work has been accepted to IEEE TMI for publicatio

    A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images

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    Background and objective: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed. Methods: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included. To model long-range semantic information, which is needed to identify and classify stenosis with challenging appearance, we adopted a token-mixer architecture (ConvMixer), which can learn structural relationship over the whole coronary artery. ConvMixer consists of a patch embedding layer followed by repeated convolutional blocks to enable the algorithm to learn long-range dependences between pixels. To visually assess ConvMixer performance, Gradient-Weighted Class Activation Mapping (Grad-CAM) analysis was used. Results: Experimental results using 5-fold cross-validation showed that our ConvMixer can classify significant coronary artery stenosis (i.e., stenosis with luminal narrowing ≥50%) with accuracy and sensitivity of 87% and 90%, respectively. For CAD-RADS 0 vs. 1–2 vs. 3–4 vs. 5 classification, ConvMixer achieved accuracy and sensitivity of 72% and 75%, respectively. Additional experiments showed that ConvMixer achieved a better trade-off between performance and complexity compared to pyramid-shaped convolutional neural networks. Conclusions: Our algorithm might provide clinicians with decision support, potentially reducing the interobserver variability for coronary artery stenosis evaluation

    Application of AI in cardiovascular multimodality imaging

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    Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging

    Myocardial perfusion in heart disease

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    Heart disease: Coronary heart disease is a major cause of mortality and morbidity in the UK and globally. It is managed with medical therapy and coronary revascularisation to reduce symptoms and reduce risk of major adverse cardiovascular events. When patients present with chest pain, it is important to risk stratify those that would most benefit from invasive coronary assessment and those that can be managed with medical therapy alone. Myocardial perfusion techniques have been developed in order to do this. Cardiovascular magnetic resonance (CMR) with stress perfusion: CMR allows the non-invasive assessment of coronary artery disease (CAD). Under conditions of vasodilator stress, a gadolinium based contrast agent is injected and during the first pass through the left ventricle, perfusion defects can be observed. There is a strong evidence base for perfusion CMR but the technique is qualitative, relies on experienced operators and potentially misses globally low perfusion such as in cases of “balanced” ischaemia. Quantitative perfusion CMR: In contrast, quantitative perfusion techniques allow the calculation of myocardial blood flow (MBF). It is more objective, less reliant on the expert observer and can give additional insights into microvascular disease and cardiomyopathy. As well as being less subjective, quantitative perfusion has other advantages for example it allows full assessment of ischaemic burden and may contain prognostic information that could be used to risk stratify and improve patient care. However, quantitative perfusion has been outside the realm of routine clinical practice due to difficulties in acquiring suitable data for full quantification and the laborious nature of analysing it. Perfusion mapping: Peter Kellman, Hui Xue and colleagues at the National Institutes for Health, USA developed the “perfusion mapping” technique to address these limitations. Perfusion maps are generated automatically and inline during the CMR scan and each voxel encodes myocardial blood flow. This allows the instant quantification of MBF without complex acquisition techniques and post processing. In this thesis I have taken perfusion mapping and deployed in the real-world at a scale an order of magnitude higher than prior quantitative perfusion studies, developing the evidence base for routine clinical use across a broad range of diseases and scenarios: In coronary artery disease: I have shown that perfusion mapping is accurate to detect coronary artery stenosis as defined by 3D quantitative coronary angiography in a single centre, 50 patient study. Transmural and subendocardial perfusion are particularly sensitive to detect coronary stenoses with performances similar to expert readers. There is a high sensitivity and high negative predictive value making perfusion mapping a good “rule-out” test for coronary disease. Quantitative perfusion and prognosis: I investigated whether stress MBF and myocardial perfusion reserve (MPR) calculated by perfusion mapping would encode prognostic information in a 1049 patient multi-centre study over a mean follow up time of 605 days. Both stress MBF and MPR were independently associated with death and major adverse cardiovascular events (MACE). The hazard ratio for MACE was 2.14 for each 1ml/g/min decrease in stress MBF and 1.74 for each unit decrease in MPR. This work can now be taken forward with prospective studies in order to better risk stratify patients, including those without perfusion defects on clinical read. Reference ranges and non-obstructive coronary disease: I sought to determine the factors that contribute to perfusion in a multi-centre registry study. In patients with no obstructive coronary artery disease, stress MBF was reduced with age, diabetes, left ventricular hypertrophy (LVH) and the use of beta blockers. Rest MBF was influenced by sex (higher in females) and reduced with beta blockers. This study suggests patient factors beyond coronary artery disease (and therefore likely microvascular disease) should also be considered when interpreting quantitative perfusion studies. In cardiomyopathy: I also investigated myocardial perfusion in cardiomyopathy looking at Fabry disease as an example disease. In a prospective, observational, single centre study of 44 patients and 27 controls I found Fabry patients had reduced perfusion (and therefore likely microvascular dysfunction), particularly in the subendocardium and was associated with left ventricular hypertrophy (LVH), glycophospholipid storage and scar. Perfusion was reduced even in patients without LVH suggesting it is an early disease marker. In conclusion, in this thesis, I have developed an evidence base for quantitative perfusion CMR and demonstrated how it can be integrated into routine clinical care. Perfusion mapping is accurate for detecting coronary artery stenosis and encodes prognostic information. Further work in this area could enable patients to be risk stratified based on their myocardial perfusion in order to reduce the morbidity and mortality associated with epicardial and microvascular coronary artery disease. Following on from this work, two further British Heart Foundation Clinical Research Training Fellowships have been awarded to further investigate quantitative perfusion in patients following surgical revascularisation of coronary disease and in patients with hypertrophic cardiomyopathy

    My future and I:cardiovascular risk stratification of asymptomatic individuals

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    Deep Learning-Based Stenosis Quantification From Coronary CT Angiography

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    Background: Coronary computed tomography angiography (CTA) allows quantification of stenosis. However, such quantitative analysis is not part of clinical routine. We evaluated the feasibility of utilizing deep learning for quantifying coronary artery disease from CTA. Methods: A total of 716 diseased segments in 156 patients (66 ± 10 years) who underwent CTA were analyzed. Minimal luminal area (MLA), percent diameter stenosis (DS), and percent contrast density difference (CDD) were measured using semi-automated software (Autoplaque) by an expert reader. Using the expert annotations, deep learning was performed with convolutional neural networks using 10-fold cross-validation to segment CTA lumen and calcified plaque. MLA, DS and CDD computed using deep-learning-based approach was compared to expert reader measurements. Results: There was excellent correlation between the expert reader and deep learning for all quantitative measures (r=0.984 for MLA; r=0.957 for DS; and r=0.975 for CDD, p<0.001 for all). The expert reader and deep learning method was not significantly different for MLA (median 4.3 mm2 for both, p=0.68) and CDD (11.6 vs 11.1%, p=0.30), and was significantly different for DS (26.0 vs 26.6%, p<0.05); however, the ranges of all the quantitative measures were within inter-observer variability between 2 expert readers. Conclusions: Our deep learning-based method allows quantitative measurement of coronary artery disease segments accurately from CTA and may enhance clinical reporting.ope
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