Automatic Assessment of Cardiac Left Ventricular Function Via Magnetic Resonance Images

Automating global and segmental (regional) assessments of cardiac Left Ventricle (LV) function in Magnetic Resonance Images (MRI) has recently sparked an impressive research effort, which has resulted a number of techniques delivering promising performances. However, despite such an effort, the problem is still acknowledged to be challenging, with substantial room for improvements in regard to accuracy. Furthermore, most of the existing techniques are labour intensive, requiring delineations of the endo- and/or epi-cardial boundaries in all frames of a cardiac sequence. On the one hand, global assessments of LV function focus on estimation of the Ejection Fraction (EF), which quantifies how much blood the heart is pumping within each beat. On the other hand, regional assessments focus on comprehensive analysis of the wall motions within each of the standardized segments of the myocardium, the muscle which contracts and sends the blood out of the LV. In clinical practice, the EF is often estimated via manual segmentations of several images in a cardiac sequence. This is prohibitively time consuming, or via automatic segmentations, which is a challenging and computationally expensive task that may result in high estimation errors. Additionally, the diagnosis of the segmental dysfunction is based on visual LV assessments, which are subject to high inter-observer variability. In this thesis, we propose accurate methods to estimate both global and regional LV function with minimal user inputs in real-time from statistics estimated in MRI. From a simple user input, we build image statistics for all the images in a subject dataset. We demonstrate that these statistics are correlated with regional as well as global LV function. Different machine learning techniques have been employed to find these correlations. The regional dysfunction is investigated in terms of a binary/multi-classification problem. A comprehensive evaluation over 20 subjects demonstrated that the estimated EFs correlated very well with those obtained from independent manual segmentations. Furthermore, comparisons with estimating EF with recent segmentation algorithms show that the proposed method yielded a very competitive performance. For regional binary classification, we report a comprehensive experimental evaluation of the proposed algorithm over 928 cardiac segments obtained from 58 subjects. Compared against ground-truth evaluations by experienced radiologists, the proposed algorithm performed competitively, with an overall classification accuracy of 86.09% and a kappa measure of 0.73. We also report a comprehensive experimental evaluation of the proposed multi-classification algorithm over the same dataset. Compared against ground-truth labels assessed by experienced radiologists, the proposed algorithm yielded an overall 4-class accuracy of 74.14%

Multimodality Imaging of Anatomy and Function in Coronary Artery Disease

Various modalities are available in the diagnostic and prognostic evaluation of patients presenting with known or suspected coronary artery disease (CAD). A rapidly expanding technique is noninvasive coronary angiography with Multi-Slice Computed Tomography (MSCT), which allows accurate detection of significant stenoses. The main value of the technique lies in the noninvasive exclusion of CAD in patients with intermediate pre-test likelihood. Although imaging in populations such as patients with previous stent placement appears to be more challenging, promising results have been obtained in these populations as well. However, it remains important to realize that the presence of coronary atherosclerosis with luminal obstruction does not invariably imply the presence of ischemia. Accordingly, a noninvasive angiographic imaging technique as MSCT cannot be used to predict the hemodynamical importance of lesions. In patients with borderline stenosis, therefore, functional testing (which can be performed by nuclear imaging, stress echocardiography or MRI) will remain necessary to determine management. Nonetheless, detection of CAD at a far earlier stage than functional imaging is an important advantage of MSCT. Initial investigations suggest that MSCT may distinguish different plaque characteristics between various presentations. Potentially, this information could be useful for risk stratification. Finally, additional non-coronary information can be derived as well. LV function can be evaluated with high accuracy while also information on the cardiac venous system can be obtained.LEI Universiteit LeidenNederlandse Hartstichting, ICIN Toshiba Medical Systems BV, Vital Images BV, Biotronik BV, Stichting EMEX, Foundation Imago, J.E. Jurriaanse Stichting, Medtronic BV, Astellas Pharma BV, St Jude Medical BV, Tyco Healthcare BV, Amgen BV (Breda), Boehringer Ingelheim BV, GE Healthcare Medical Diagnostics (Eindhoven), Pfizer BV, Siemens BV, Bristol-Myers Squibb, Boston Scientific Benelux BV, Merck Sharp & Dohme BV.Vasculaire biologie en interventi

Assessment of left ventricular mass and volumes by three-dimensional echocardiography in patients with or without wall motion abnormalities: comparison against cine magnetic resonance imaging

To evaluate if three-dimensional echocardiography (3-DE) is as accurate and reproducible as cine magnetic resonance imaging (cMR) in estimating left ventricular (LV) parameters in patients with and without wall motion abnormalities (WMA)

SPM to the heart: mapping of 4D continuous velocities for motion abnormality quantification

International audienceThis paper proposes to apply parallel transport and statistical atlas techniques to quantify 4D myocardial motion abnormalities. We take advantage of our previous work on cardiac motion , which provided a continuous spatiotemporal representation of velocities, to interpolate and reorient cardiac motion fields to an unbiased reference space. Abnormal motion is quantified using SPM analysis on the velocity fields, which includes a correction based on random field theory to compensate for the spatial smoothness of the velocity fields. This paper first introduces the imaging pipeline for constructing a continuous 4D velocity atlas. This atlas is then applied to quantify abnormal motion patterns in heart failure patients

Cardiac imaging for risk stratification in asymptomatic diabetes

In this thesis, we evaluated different cardiac imaging techniques for risk stratification in asymptomatic diabetesUBL - phd migration 201