Deep Learning-based Automated Aortic Area and Distensibility Assessment: The Multi-Ethnic Study of Atherosclerosis (MESA)

This study applies convolutional neural network (CNN)-based automatic segmentation and distensibility measurement of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI) within the large MESA cohort with subsequent assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study were included. Train, validation, and internal test sets consisted of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. An external test set of TAAs consisted of 37 studies (3224 images). A U-Net based CNN was constructed, and performance was evaluated utilizing dice coefficient (for segmentation) and concordance correlation coefficients (CCC) of aortic geometric parameters by comparing to manual segmentation and parameter estimation. Dice coefficients for aorta segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the internal and external test of TAAs, respectively. CCC for comparison of manual and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. CCCs for maximum and minimum descending aortic areas were 0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93, respectively, on the external test set. We successfully developed and validated a U-Net based ascending and descending aortic segmentation and distensibility quantification model in a large multi-ethnic database and in an external cohort of TAA patients.Comment: 25 pages, 5 figure

Volumetric Quantification of Myocardial Perfusion Using Analysis of Multi-Detector Computed Tomography 3D Datasets

Abstract Multi-detector computed tomography (MDCT) assessment of myocardial perfusion is based on visualization of 2D slices. To overcome the subjective nature of this analysis, we developed a new technique for quantification of myocardial perfusion from MDCT 3D datasets and tested it against nuclear myocardial perfusion imaging (MPI Introduction While MDCT is increasingly used as an alternative to invasive coronary angiography, recent studies have demonstrated its potential to provide perfusion information, which could be a valuable addition in the diagnosis of coronary artery disease (CAD). These studies reported hypoenhanced areas corresponding to scar visualized in patients post myocardial infarction (MI), and in animals with acute MI We recently developed a new quantitative index of perfusion that was designed to take into account these differences, and tested it on 2D slices. The addition of this analysis improved the diagnostic accuracy of MDCT evaluation of CAD, especially in patients with high calcium scores and stents Methods We studied 44 patients who underwent CT coronary angiography (CTCA) for the evaluation of CAD. These patients were divided into a study group of 29 patients (age: 62±10, 23 males) who also had MPI within 57±72 days (14 patient with normal MPI both at rest and stress and 15 patients with perfusion defects on MPI), and a control group of 15 patients (age: 58±16, 8 males) who had normal MPI both at rest and stress and no significant stenosis on CTCA. Patients who underwent coronary interventions between MPI and CTCA were excluded. MDCT imaging All CTCA studies were clinically indicated and performed according to a standard protocol. Images were obtained using an MDCT scanner (64-channels, Philips) with retrospective ECG-gating. A nonionic iodinated contrast agent was used (40-80 ml iv at 5-6 ml/sec)

Consistency of aortic distensibility and pulse wave velocity estimates with respect to the Bramwell-Hill theoretical model: a cardiovascular magnetic resonance study

<p>Abstract</p> <p>Background</p> <p>Arterial stiffness is considered as an independent predictor of cardiovascular mortality, and is increasingly used in clinical practice. This study aimed at evaluating the consistency of the automated estimation of regional and local aortic stiffness indices from cardiovascular magnetic resonance (CMR) data.</p> <p>Results</p> <p>Forty-six healthy subjects underwent carotid-femoral pulse wave velocity measurements (<it>CF_PWV</it>) by applanation tonometry and CMR with steady-state free-precession and phase contrast acquisitions at the level of the aortic arch. These data were used for the automated evaluation of the aortic arch pulse wave velocity (<it>Arch_PWV</it>), and the ascending aorta distensibility (<it>AA_Distc, AA_Distb)</it>, which were estimated from ascending aorta strain (<it>AA_Strain</it>) combined with either carotid or brachial pulse pressure. The local ascending aorta pulse wave velocity <it>AA_PWVc </it>and <it>AA_PWVb </it>were estimated respectively from these carotid and brachial derived distensibility indices according to the Bramwell-Hill theoretical model, and were compared with the <it>Arch_PWV</it>. In addition, a reproducibility analysis of <it>AA_PWV </it>measurement and its comparison with the standard <it>CF_PWV </it>was performed. Characterization according to the Bramwell-Hill equation resulted in good correlations between <it>Arch_PWV </it>and both local distensibility indices <it>AA_Distc </it>(r = 0.71, p < 0.001) and <it>AA_Distb </it>(r = 0.60, p < 0.001); and between <it>Arch_PWV </it>and both theoretical local indices <it>AA_PWVc </it>(r = 0.78, p < 0.001) and <it>AA_PWVb </it>(r = 0.78, p < 0.001). Furthermore, the <it>Arch_PWV </it>was well related to <it>CF_PWV </it>(r = 0.69, p < 0.001) and its estimation was highly reproducible (inter-operator variability: 7.1%).</p> <p>Conclusions</p> <p>The present work confirmed the consistency and robustness of the regional index <it>Arch_PWV </it>and the local indices <it>AA_Distc and AA_Distb </it>according to the theoretical model, as well as to the well established measurement of <it>CF_PWV</it>, demonstrating the relevance of the regional and local CMR indices.</p

Automated left ventricular diastolic function evaluation from phase-contrast cardiovascular magnetic resonance and comparison with Doppler echocardiography

International audienceBACKGROUND: Early detection of diastolic dysfunction is crucial for patients with incipient heart failure. Although this evaluation could be performed from phase-contrast (PC) cardiovascular magnetic resonance (CMR) data, its usefulness in clinical routine is not yet established, mainly because the interpretation of such data remains mostly based on manual post-processing. Accordingly, our goal was to develop a robust process to automatically estimate velocity and flow rate-related diastolic parameters from PC-CMR data and to test the consistency of these parameters against echocardiography as well as their ability to characterize left ventricular (LV) diastolic dysfunction. RESULTS: We studied 35 controls and 18 patients with severe aortic valve stenosis and preserved LV ejection fraction who had PC-CMR and Doppler echocardiography exams on the same day. PC-CMR mitral flow and myocardial velocity data were analyzed using custom software for semi-automated extraction of diastolic parameters. Inter-operator reproducibility of flow pattern segmentation and functional parameters was assessed on a sub-group of 30 subjects. The mean percentage of overlap between the transmitral flow segmentations performed by two independent operators was 99.7 ± 1.6%, resulting in a small variability ( 0.71) and receiver operating characteristic (ROC) analysis revealed their ability to separate patients from controls, with sensitivity > 0.80, specificity > 0.80 and accuracy > 0.85. Slight superiority in terms of correlation with echocardiography (r = 0.81) and accuracy to detect LV abnormalities (sensitivity > 0.83, specificity > 0.91 and accuracy > 0.89) was found for the PC-CMR flow-rate related parameters. CONCLUSIONS: A fast and reproducible technique for flow and myocardial PC-CMR data analysis was successfully used on controls and patients to extract consistent velocity-related diastolic parameters, as well as flow rate-related parameters. This technique provides a valuable addition to established CMR tools in the evaluation and the management of patients with diastolic dysfunction

Assessment of acute myocardial infarction: current status and recommendations from the North American society for cardiovascular imaging and the European society of cardiac radiology

There are a number of imaging tests that are used in the setting of acute myocardial infarction and acute coronary syndrome. Each has their strengths and limitations. Experts from the European Society of Cardiac Radiology and the North American Society for Cardiovascular Imaging together with other prominent imagers reviewed the literature. It is clear that there is a definite role for imaging in these patients. While comparative accuracy, convenience and cost have largely guided test decisions in the past, the introduction of newer tests is being held to a higher standard which compares patient outcomes. Multicenter randomized comparative effectiveness trials with outcome measures are required

A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces

International audienceThis chapter presents an introductory overview and a tutorial of signal processing techniques that can be used to recognize mental states from electroencephalographic (EEG) signals in Brain-Computer Interfaces. More particularly, this chapter presents how to extract relevant and robust spectral, spatial and temporal information from noisy EEG signals (e.g., Band Power features, spatial filters such as Common Spatial Patterns or xDAWN, etc.), as well as a few classification algorithms (e.g., Linear Discriminant Analysis) used to classify this information into a class of mental state. It also briefly touches on alternative, but currently less used approaches. The overall objective of this chapter is to provide the reader with practical knowledge about how to analyse EEG signals as well as to stress the key points to understand when performing such an analysis

Using Intracardiac Vectorcardiographic Loop for Surface ECG Synthesis

Current cardiac implantable devices offer improved processing power and recording capabilities. Some of these devices already provide basic telemonitoring features that may help to reduce health care expenditure. A challenge is posed in particular for the telemonitoring of the patient's cardiac electrical activity. Indeed, only intracardiac electrograms (EGMs) are acquired by the implanted device and these signals are difficult to analyze directly by clinicians. In this paper, we propose a patient-specific method to synthesize the surface electrocardiogram (ECG) from a set of EGM signals, based on a 3D representation of the cardiac electrical activity and principal component analysis (PCA). The results, in the case of sinus rhythm, show a correlation coefficient between the real ECG and the synthesized ECG of about 0.85. Moreover, the application of the proposed method to the patients who present an abnormal heart rhythm exhibits promising results, especially for characterizing the bundle branch blocs. Finally, in order to evaluate the behavior of our procedure in some practical situations, the quality of the ECG reconstruction is studied as a function of the number of EGM electrodes provided by the CIDs

Independent Component Analysis Based on Non-polynomial Approximation of Negentropy Application to MRS Source Separation

International audienceIn this paper, a new ICA algorithm based on non-polynomial approximation of negentropy that captures both the asymmetry of the sources' PDF and the sub/super-Gaussianity of this latter is proposed. A gradient-ascent iteration with quasi-optimal stepsize is used to maximize the considered cost function. With this quasi-optimal computation in the case of highly non-linear objective function, the main advantages of the proposed algorithm are 1) its robustness to outliers compared to kurtosis-based ICA method especially for situations of small data size, and 2) its ability to capture sources' asymmetric probability density functions which is a property that can't be fulfilled in classic ICA algorithms like FastICA. Numerical results reported in the context of source separation of brain magnetic resonance spectroscopy show the superiority of the proposed algorithm over the FastICA algorithm in terms of both source separation accuracy and the number of iterations required for convergence. © 2018 IEEE