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This work introduces a new generic framework to the analysis of ridges and its application to segmentation of planar structures in bioimaging. Based on a Gaussian model, local information associated to ridges is extracted, which actually characterise them. The theoretical basis of the framework is first presented for the one-dimensional case. Its extension to higher dimensions is then shown to be...
Finite Element Analysis using geometrical 3D models obtained from medical imaging is a widely used method in the study of the mechanical properties of jawbone and dentition with important contributions in diagnosis, treatment, and outcome assessment. Numerical analysis in biomechanics seems promising yet it requires a careful reconstruction of specimens to make analysis reliable and feasible. A semi-automatic...
We propose a computational model to simulate the physical processes implied in the Magnetic Resonance Images formation in vascularised tissues. A combined model of MRI acquisition and blood flow is presented. The blood flow patterns are modeled using the Lattice Boltzmann Method, and the magnetic resonance experiments follow the Bloch equation. A new algorithm has been developed to compute the local...
Diffusion tensor imaging (DTI) has been widely used for nondestructive characterization of microstructures of myocardium or brain connectivity. It requires repeated acquisition with different diffusion gradients. The long acquisition time greatly limits the clinical application of DTI. In this paper, a novel method, named model-based method with joint sparsity constraint (MB-JSC), effectively incorporates...
The most common image representation method for biomedical image reconstruction uses pixels, and the image is assumed to be constant throughout the pixel. Other methods have also been used. In many reconstruction problems, the measured data is approximated by line integrals through the object. This fact suggests a new class of model representation methods based on classical Newton-Cotes methods of...
Medical image simulation is useful for biological modeling, image analysis, and designing new imaging devices but it is not widely available due to the complexity of simulators, the scarcity of object models, and the heaviness of the associated computations. This paper presents the Virtual Imaging Platform, an openly-accessible web platform for multi-modality image simulation. The integration of simulators...
In this paper, we explore the theory of tensor invariants as a mathematical framework for computing new biomarkers for HARDI. We present and explain the integrity basis, basic invariants and principal invariants of 2nd & 4th order tensors to expand on a recently proposed paper on 4th order tensor invariants. We present the mathematical results and compute the basic and principal invariants on...
Biological tissue characterization requires adequate models of the data. Research in ultrasound image modeling has mainly focused on statistical methods. Some authors have addressed the fractal properties of such images using fractional Brownian motion model. However, recent studies have shown that skin ultrasound signals have multifractal properties. This paper proposes a lognormal multiplicative...
Clinical translation of computational models of the heart has been hampered by the absence of complete and rigorous technical and clinical validation, as well as benchmarking of the developed tools. To address this issue, a dataset containing the cardiac anatomy and fibre orientations from magnetic resonance images (MRI), as well as epicardial transmembrane potentials from optical mapping acquired...
Cardiac electrophysiology procedures are routinely used to treat patients with rhythm disorders. The success rates of ablation procedures and cardiac resynchronization therapy are still sub-optimal. Recent advances in medical imaging, image processing and cardiac biophysical modeling have the potential to improve patient outcome. This manuscript provides an overview of how these advances have been...
Cardiac therapies aim to correct pathological blood flow. Patient-specific therapy planning is challenging due to the large variability in disease cause, location and severity. A predictive framework is therefore needed to assess the optimal treatment for a patient in terms of maximizing effectiveness (blood flow velocity, vorticity, cardiac output, etc.) and minimizing the risk of complications....
Personalization of cardiac functions and properties from subject-specific noninvasive clinical observations has been an active area with increasing clinical significance. The personalized cardiac information can quantitatively reflect the pathophysiology of an individual heart and further be used to assist heart diagnosis and treatment planning. Due to the electromechanically integrated nature of...
Mesoscopic imaging techniques, such as optical projection tomography (OPT) and single plane illumination microsopy (SPIM), are becoming invaluable tools to understand multicellular development. Our lab has worked on both of these technologies, with a focus on obtaining new data sets to be used as the basis of dynamical computer simulations. Here I will describe our work to transform a classical model...
The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophysiology inspired mathematical models were developed for simulating brain's electrical activity imaged through Electroencephalography (EEG) more than three decades ago. At the present well informative models which even describe the functional integration of cortical regions also exists. However, a very limited...
Factor Analysis (FA) is a well established method for factors separation in analysis of dynamic medical imaging. However, its assumptions are valid only in limited regions of interest (ROI) in the images which must be selected manually or using heuristics. The resulting quality of separation is sensitive to the choice of these ROI. We propose a new probabilistic model for functional analysis with...
Automated segmentation and quantification of cellular and subcellular components in multiplexed images has allowed for a combination of both spatial and protein expression information to become available for analysis. However, performing analyses across multiple patients and tissue types continues to be a challenge, as well as the greater challenge of tissue classification itself. We propose a model...
Low back pain is a current and increasing problem closely related to intervertebral disc degeneration, which is responsible for over 90% of spinal surgical procedures. In clinical routine, clinicians base their diagnosis of disc degeneration on 2D analysis of Magnetic Resonance (MR) images. In this work, an automatic 3D segmentation method, based on active shape models, is presented for both degenerated...
The most common cause of acute pulmonary hypertension is pulmonary embolism (PE). Classification of PE severity can be based on obstruction indices that are estimated from clinical imaging, however, as patients with apparently similar levels of obstruction can have quite different clinical outcomes, obstruction indices currently have limited use clinically. Embolus size and location affects patient...
We have developed techniques to automatically generate personalised biomechanical models of patients' hearts based on 3D cardiac images. We demonstrate this approach using multi-slice computed tomography images. Unsupervised segmentation was performed using non-rigid image registration with a segmented image. A finite element model was automatically fitted to the segmented data of the left ventricle...
During radiotherapy of lung tumors, the respiratory motion must be tracked to reduce radiation of healthy tissue. This is usually done by using a respiratory surrogate, but with limited accuracy. We investigate how patient-specific finite element models (FEM) of respiratory mechanics can predict the motion of the lungs. First, the anatomical models of the lungs and thorax are extracted from CT images...
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