Predicting Plausible Human Purkinje Network Morphology from Simulations

The Purkinje network (PN) gains more clinically importance as it becomes target for pacing in rate control and defibrillation. However, our understanding of the PN morphology arises from animal experiments, which might not transfer to humans. Therefore, we propose an automated computer simulation predicting physiological PN morphologies depending on the heart shape. It starts by generating virtual heart shapes from a statistical shape atlas and generates virtual PNs on the endocardial surface. For the combined virtual models the eikonal equation is solved to estimate the local activation times throughout the myocardium, which then feed forward to an simulation of the 12-lead surface ECG. From the simulated ECG the QRS-complex is compared against a healthy standard QRS-complex ,which allows to estimate how physiological a PN morphology is. In our model, only bundle branch bifurcation points near the base or near the apex result in physiological QRS wave forms. For the right bundle, more physiological QRS waves can be obtained when the branching point is at the apex. Only a minor dependency of the ECG on the heart shape is found. However, a strong correlation between the bundle branch bifurcation points themselves is observed

Two-dimensional PCA : a new approach to appearance-based face representation and recognition

2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Population‐specific modelling of between/within‐subject flow variability in the carotid arteries of the elderly

Computational fluid dynamics models are increasingly proposed for assisting the diagnosis and management of vascular diseases. Ideally, patient‐specific flow measurements are used to impose flow boundary conditions. When patient‐specific flow measurements are unavailable, mean values of flow measurements across small cohorts are used as normative values. In reality, both the between‐subjects and within‐subject flow variabilities are large. Consequently, neither one‐shot flow measurements nor mean values across a cohort are truly indicative of the flow regime in a given person. We develop models for both the between‐subjects and within‐subject variability of internal carotid flow. A log‐linear mixed effects model is combined with a Gaussian process to model the between‐subjects flow variability, while a lumped parameter model of cerebral autoregulation is used to model the within‐subject flow variability in response to heart rate and blood pressure changes. The model parameters are identified from carotid ultrasound measurements in a cohort of 103 elderly volunteers. We use the models to study intracranial aneurysm flow in 54 subjects under rest and exercise and conclude that OSI, a common wall shear‐stress derived quantity in vascular CFD studies, may be too sensitive to flow fluctuations to be a reliable biomarker

KPCA Plus LDA : a complete kernel Fisher discriminant framework for feature extraction and recognition

2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Intracranial Aneurysm Detection from 3D Vascular Mesh Models with Ensemble Deep Learning

Intracranial aneurysm rupture can cause a serious stroke, which is related to the decline of daily life ability of the elderly. Although deep learning is now the most successful solution for organ detection, it requires myriads of training data, consistent of the image format, and a balanced sample distribution. This work presents an innovative representation of intracranial aneurysm detection as a shape analysis problem rather than a computer vision problem. We detected intracranial aneurysms in 3D cerebrovascular mesh models after segmentation of the brain vessels from the medical images, which can overcome the barriers of data format and data distribution, serving both clinical and screening purposes. Additionally, we propose a transferable multi-model ensemble (MMEN) architecture to detect intracranial aneurysms from cerebrovascular mesh models with limited data. To obtain a well-defined convolution operator, we use a global seamless parameterization converting a 3D cerebrovascular mesh model to a planar flat-torus. In the architecture, we transfer the planar flat-torus presentation abilities of three GoogleNet Inception V3 models, which were pre-trained on the ImageNet database, to characterize the intracranial aneurysms with local and global geometric features such as Gaussian curvature (GC), shape diameter function (SDF) and wave kernel signature (WKS), respectively. We jointly utilize all three models to detect aneurysms with adaptive weights learning based on back propagation. The experimental results on the 121 models show that our proposed method can achieve detection accuracy of 95.1% with 94.7% F1-score and 94.8% sensitivity, which is as good as the state-of-art work but is applicable to inhomogeneous image modalities and smaller datasets

Information theoretic measurement of blood flow complexity in vessels and aneurysms: Interlacing complexity index

Haemodynamics is believed to be a crucial factor in the aneurysm formation, evolution and eventual rupture. The 3D blood flow is typically derived by computational fluid dynamics (CFD) from patient-specific models obtained from angiographic images. Typical quantitative haemodynamic indices are local. Some qualitative classifications of global haemodynamic features have been proposed. However these classifications are subjective, depending on the operator visual inspection. In this work we introduce an information theoretic measurement of the blood flow complexity, based on Shannon’s Mutual Information, named Interlacing Complexity Index (ICI). ICI is an objective quantification of the flow complexity from aneurysm inlet to aneurysm outlets. It measures how unpredictable is the location of the streamlines at the outlets from knowing the location at the inlet, relative to the scale of observation. We selected from the @neurIST database a set of 49 cerebral vasculatures with aneurysms in the middle cerebral artery. Surface models of patient-specific vascular geometries were obtained by geodesic active region segmentation and manual correction, and unsteady flow simulations were performed imposing physiological flow boundary conditions. The obtained ICI has been compared to several qualitative classifications performed by an expert, revealing high correlations

Guest Editorial: IJCARS-MICCAI 2018 special issue.

status: publishe

Nonparametric Quality Assessment of Natural Images

In this article, the authors explore an alternative way to perform no-reference image quality assessment (NR-IQA). Following a feature extraction stage in which spatial domain statistics are utilized as features, a two-stage nonparametric NR-IQA framework is proposed. This approach requires no training phase, and it enables prediction of the image distortion type as well as local regions' quality, which is not available in most current algorithms. Experimental results on IQA databases show that the proposed framework achieves high correlation to human perception of image quality and delivers competitive performance to state-of-the-art NR-IQA algorithms

Special issue on MICCAI 2018

status: publishe

Semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets

Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Ensuring full coverage of the Left Ventricle (LV) is a basic criteria of CMR image quality. Complete LV coverage, from base to apex, precedes accurate cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in large imaging cohorts. In this paper, we propose a novel semi-supervised method to check the coverage of LV from CMR images by using generative adversarial networks (GAN), we call it Semi-Coupled-GANs (SCGANs). To identify missing basal and apical slices in a CMR volume, a two-stage framework is proposed. First, the SCGANs generate adversarial examples and extract high-level features from the CMR images; then these image attributes are used to detect missing basal and apical slices. We constructed extensive experiments to validate the proposed method on UK Biobank with more than 6000 independent volumetric MR scans, which achieved high accuracy and robust results for missing slice detection, comparable with those of state of the art deep learning methods. The proposed method, in principle, can be adapted to other CMR image data for LV coverage assessment