Vessel tractography using an intensity based tensor model

In the last decade, CAD (Coronary Artery Disease) has been the leading cause of death worldwide [1]. Extraction of arteries is a crucial step for accurate visualization, quantification, and tracking of pathologies. However, coronary artery segmentation is one of the most challenging problems in medical image analysis, since arteries are complex tubular structures with bifurcations, and have possible pathologies. Moreover, appearance of blood vessels and their geometry can be perturbed by stents, calcifications and pathologies such as stenosis. Besides, noise, contrast and resolution artifacts can make the problem more challenging. In this thesis, we present a novel tubular structure segmentation method based on an intensity-based tensor that fits to a vessel, which is inspired from diffusion tensor image (DTI) modeling. The anisotropic tensor inside the vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized with a single seed point and it is capable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrate the performance of our algorithm on 3 complex tubular structured synthetic datasets, and on 8 CTA (Computed Tomography Angiography) datasets (from Rotterdam Coronary Artery Algorithm Evaluation Framework) for quantitative validation. Additionally, extracted arteries from 10 CTA volumes are qualitatively evaluated by a cardiologist expert's visual scores

Vessel tractography using an intensity based tensor model with branch detection

In this paper, we present a tubular structure seg- mentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmen- tation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient Computed Tomography Angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert

Vessel tractography using an intensity based tensor model

In this paper, we propose a novel tubular structure segmen- tation method, which is based on an intensity-based tensor that fits to a vessel. Our model is initialized with a single seed point and it is ca- pable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrated the performance of our algorithm on 3 complex contrast varying tubular structured synthetic datasets for quantitative validation. Additionally, extracted arteries from 10 CTA (Computed Tomography An- giography) volumes are qualitatively evaluated by a cardiologist expert’s visual scores

An automatic branch and stenoses detection in computed tomography angiography

In this work, we present an automatic branch and stenoses de- tection method that is capable of detecting all types of plaques in Computed Tomography Angiography (CTA) modality. Our method is based on the vessel extraction algorithm we pro- posed in [1], and detects branches and stenoses in a very fast way. We demonstrate the performance of our branch detection method on 3 complex tubular structured synthetic datasets for quantitative validation. Additionally, we show the preliminary results of stenoses detection algorithm on 11 CTA volumes, which are qualitatively evaluated by a cardiol- ogist expert

Immune responses elicited by the recombinant Erp, HspR, LppX, MmaA4, and OmpA proteins from Mycobacterium tuberculosis in mice

Immunogenic potency of the recombinant Erp, HspR, LppX, MmaA4, and OmpA proteins from Mycobacterium tuberculosis (MTB), formulated with Montanide ISA 720 VG adjuvant, was evaluated in BALB/c mice for the first time in this study. The five vaccine formulations, adjuvant, and BCG vaccine were subcutaneously injected into mice, and the sera were collected at days 0, 15, 30, 41, and 66. The humoral and cellular immune responses against vaccine formulations were determined by measuring serum IgG and serum interferon-gamma (IFN-γ) and interleukin-12 (IL-12) levels, respectively. All formulations significantly increased IgG levels post-vaccination. The highest increase in IFN-γ level was provided by MmaA4 formulation. The Erp, HspR, and LppX formulations were as effective as BCG in enhancement of IFN-γ level. The most efficient vaccine boosting the IL-12 level was HspR formulation, especially at day 66. Erp formulation also increased the IL-12 level more than BCG at days 15 and 30. The IL-12 level boosted by MmaA4 formulation was found to be similar to that by BCG. OmpA formulation was inefficient in enhancement of cellular immune responses. This study showed that MmaA4, HspR, and Erp proteins from MTB are successful in eliciting both humoral and cellular immune responses in mice

Modeling of tubular structures and fibers in in vivo data: revealing asymmetry in human vasculature and white matter fiber tracts

In medical image analysis, asymmetric modeling of vasculatures, such as cerebro- or cardio-vessels, is a challenging task because of the n-furcated branching geometries. Detection of asymmetry in anatomical structures such as vessels is a signi cant step in the accurate modeling of vasculatures. Therefore, it is essential to present new computational methodologies to model the underlying asymmetries in anatomical structures. For the asymmetric modeling of vasculatures, which is the rst part of this thesis, we present a vasculature segmentation method that is based on a cylindrical ux-based higher order tensor (HOT). On a vessel structure, the HOT naturally models branching points as well as the tubular sections of the vessels. We demonstrate quantitative validation of the proposed algorithm on synthetic complex tubular structures, cerebral vasculature in Magnetic Resonance Angiography (MRA) datasets and coronary arteries from Computed Tomography Angiography (CTA) volumes. Capturing asymmetry in white matter (WM) bers is another open problem. Detection of asymmetry in bers is important in both the localization and the quantitative assessment of speci c neuronal pathways. More than 60% of WM ber populations make crossings in a voxel, therefore, it is natural to expect a substantial part of those to involve asymmetric crossings/junctions. However, most well-known white matter ber reconstruction methods assume symmetric signal acquisition that yield symmetric orientation distribution functions (ODFs) even when the underlying geometry is asymmetric. In the second part of this thesis, we employ inter-voxel ltering approaches through a cone model to reveal more information regarding the cytoarchitectural organization within the voxel. The cone model facilitates a sharpening of the ODFs in some directions while suppressing peaks in other directions, thus yielding an asymmetric ODF (AODF) eld. The feasibility of the technique is demonstrated on in vivo data obtained from the MGHUSC Human Connectome Project (HCP) and Parkinson's Progression Markers Initiative (PPMI) Project database. Quantitative Susceptibility Mapping (QSM) reconstruction is a recent technique for venous imaging. The reconstruction of QSM image volume is a challenging problem due to its long acquisition time, which causes several artifacts that need to be handled separately using a regularization term in the reconstruction. Prior knowledge such as smoothness and sparsity assumptions has been widely used as regularization. We hypothesize that incorporation of local orientation of vessels into regularization leads to an enhanced imaging of vasculatures. In the last part of this thesis, we present vessel orientation as a new regularization term to improve the accuracy of l1- and l2-norm regularized QSM reconstruction in cerebral veins. Using a multi-orientation QSM acquisition as gold standard, we show that the QSM reconstruction obtained with the vessel anatomy prior provides up to 40% Root-Mean-Square-Error (RMSE) reduction relative to the baseline l1 regularizer approach

A cerebral blood vessels segmentation method using a flux based second order tensor model

In this paper, we view the segmentation of cerebral blood vessels from Digital Subtraction Angiography (DSA) and Rotational Angiography (RA) problem from a tensor estimation and tractography perspective as in diffusion tensor imaging (DTI). We have developed a flux based multi-directional cylinder model that fits to a second-order tensor whose principal eigenvector represents the vessel's centerline. This anisotropic tensor inside the vessel drives the segmentation analogously to a tractography approach in DTI analysis starting from a seed point used as initialization

Asymmetric Orientation Distribution Functions (AODFs) revealing intravoxel geometry in diffusion MRI

Characterization of anisotropy via diffusion MRI reveals fiber crossings in a substantial portion of voxels within the white-matter (WM) regions of the human brain. A considerable number of such voxels could exhibit asymmetric features such as bends and junctions. However, widely employed reconstruction methods yield symmetric Orientation Distribution Functions (ODFs) even when the underlying geometry is asymmetric. In this paper, we employ inter-voxel directional filtering approaches through a cone model to reveal more information regarding the cytoarchitectural organization within the voxel. The cone model facilitates a sharpening of the ODFs in some directions while suppressing peaks in other directions, thus yielding an Asymmetric ODF (AODF) field. We also show that a scalar measure of AODF asymmetry can be employed to obtain new contrast within the human brain. The feasibility of the technique is demonstrated on in vivo data obtained from the MGH-USC Human Connectome Project (HCP) and Parkinson's Progression Markers Initiative (PPMI) Project database. Characterizing asymmetry in neural tissue cytoarchitecture could be important for localizing and quantitatively assessing specific neuronal pathways

Higher order tensor-based segmentation and n-furcation modeling of vascular structures

A new vascular structure segmentation method, which is based on a cylindrical flux-based higher order tensor (HOT), is presented. On a vessel structure, HOT naturally models branching points, which create challenges for vessel segmentation algorithms. In a general linear HOT model, embedded in 3D, one has to work with an even order tensor due to an enforced antipodal-symmetry on the unit sphere in 3D. However, in scenarios such as in a bifurcation, the antipodally-symmetric tensor models of even order will not be useful. In order to overcome that limitation, we embed the tensor in 4D and obtain a structure that can model asymmetric junction scenarios. Thus, we will demonstrate a seed-based vessel segmentation algorithm, which exploits a 3rd or 4th order tensor constructed in 4D. We validate the algorithm on both synthetic complex vascular structures as well as real coronary artery datasets of the Rotterdam Coronary Artery Algorithm Evaluation framework