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Primary ciliary dyskinesia (PCD) implies cilia with dysmotility or total absence of motility, which may result in sinusitis, chronicbronchitis, bronchiectasis and male infertility. A large number of deficiencies detectable on the ultrastructural level give rise to PCD, but patients with normal cilia ultrastructure are common. An early diagnosis is very important since PCD can cause permanent lung...
Transient elastography can be used to measure tissue elasticity by applying a mechanical stress constraint and measuring the velocity of propagation of the induced shear wave, assumed to be proportional to tissue elasticity. In this paper we study two original maximum-likelihood (ML) approaches for shear wave velocity estimation on RF ultrasound signals acquired with a transient elastography setup...
Single-particle tracking is computationally a challenging problem, and usually solved with local methods. Local methods suffer from defects in the image data or in the detection of particles, such as temporal disappearing of particles. A particle tracking method has to provide a solution also to real disappearing and appearing of particles as a result of merging and splitting. Here, we present an...
Recent development in the inference of brain connectivity from neuroimaging data such as functional magnetic resonance images (fMRI) provides better understanding of brain activities and functions. The group analysis of fMRI data usually focuses on functional connectivity, while exploratory graphical modeling of effective connectivity is generally designed for the single-subject case. In this paper,...
The aim of this study is to develop a new method to extract input function (IF) from dynamic positron emission tomography (PET) data using the well known [18F]-2-Deoxy-2-fluoro-d-glucose (18F-FDG) for the determination of the myocardial metabolic rate of glucose (MMRG). In the case of cardiac studies, the IF can be obtained directly from the data by mean of a region of interest (ROI) drawn over the...
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...
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...
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 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....
This paper presents a new method for segmentation of ambiguously defined structures, such as the hippocampus, by exploiting prior knowledge from another perspective. An expert's experience of where to use prior knowledge and where image information, is captured as a local weighting map. This map can be used to locally guide the evolution in a level set evolution framework. Such a map is produced for...
This paper presents a method of real time 3D surgical simulation for deformable organs. It employs the technique of structured surface representation to decrease the description dimensionality, which allows for efficient deformation simulation by reducing the computational cost involved in the regular ODE solver. Majority of the deformation is estimated by surface reconstruction in the best-fit subspace...
We present a fast and robust approach to tracking whole fluorescent cells in time-lapse series. The proposed tracking scheme involves two steps. First, coherence-enhancing diffusion filtering is applied on each frame to reduce the amount of noise and enhance flow-like structures. Second, the enhanced cell boundaries are detected by minimizing the Chan-Vese model in a fast level set-like framework...
In this paper we present a dictionary-based framework for the reconstruction of a field of ensemble average propagators (EAPs), given a high angular resolution diffusion MRI data set. Existing techniques often consider voxel-wise reconstruction of the EAP field thereby leading to a noisy reconstruction across the field. We present a dictionary learning framework for achieving a smooth EAP reconstruction...
Segmenting regions of high angiogenic activity corresponding to malignant tumors from DCE-MRI is a time-consuming task requiring processing of data in 4 dimensions. Quantitative analyses developed thus far are highly sensitive to external factors and are valid only under certain operating assumptions, which need not be valid for breast carcinomas. In this paper, we have developed a novel Statistical...
Segmentation of spinal vertebrae is extremely important in the study of spinal related disease or disorders. However, limited work has been done on precise segmentation of spinal vertebrae. The complexity of vertebrae shapes, with gaps in the cortical bone, internal boundaries, as well as the noisy, incomplete or missing information from the images have undoubtedly increased the challenge for image...
Integrated analysis of tissue histology with the genome-wide array (e.g., OMIC) and clinical data have the potential for hypothesis generation and be prognostic. OMIC and clinical data are typically characterized and summarized at the patient level while whole mount histological sections are often heterogeneous in terms of nuclear morphology and organization. In this paper, we propose a multilevel...
The disruption of normal function and connectivity of neural circuits is common across many diseases and disorders of the brain. This disruptive effect can be studied and analyzed using the brain's complex functional and structural connectivity network. Complex network measures from the field of graph theory have been used for this purpose in the literature. In this paper we have introduced a new...
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...
Accurate segmentation of lymph nodes in head and neck (H&N) CT images is essential for the radiotherapy planning of the H&N cancer. Atlas-based segmentation methods are widely used for the automated segmentation of such structures. Multi-atlas approaches are proven to be more accurate and robust than using a single atlas. We have recently proposed a general Markov random field (MRF)-based...
Accurate 3D models of the human brain vessels can greatly help to diagnose serious diseases. Such models can be constructed by segmentation of 3D MRA images, especially the recently introduced high resolution 7T MRA. We propose a new two-step approach for fully automatic segmentation of 7T MRA images of the human cerebrovascular system. First, a 3D model-based approach is applied to segment thick...
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