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

SHEEP AS ANIMAL MODEL IN MINIMALLY INVASIVE NEUROSURGERY IN EDEN2020

Glioblastomas (GBMs) is a malignant type of central nervous system tumours and its presentation is almost 80% of all malignant primary brain neoplasia. This kind of tumour is highly invasive infiltrating the white matter area and is confined to the central nervous with a very poor patient outcome survival around 10 months. Of the existing treatment approaches, Convection Enhanced drug Delivery (CED) offers several advantages for the patient but still suffers from significant shortcomings. Enhanced Delivery Ecosystem for Neurosurgery in 2020 (EDEN2020) is a European project supported with a new catheter development as the key project point in an integrated technology platform for minimally invasive neurosurgery. Due to the particular anatomy and size, sheep (Ovis aries) have been selected as experimental large animal model and a new Head Frame system MRI/CT compatible has been made and validated ad hoc for the project. In order to understand experimentally the best target point for the catheter introduction a sheep brain DTI atlas has been created. Corticospinal tract (CST), corpus callosum (CC), fornix (FX), visual pathway (VP) and occipitofrontal fascicle (OF), have been identified bilaterally for all the animals. Three of these white matter tracts, the corpus callosum, the fornix and the corona radiata, have been selected to understand the drugs diffusion properties and create a computational model of diffusivity inside the white matter substance. The analysis have been conducted via Focused Ion Beam using scanning Electron Microscopy combined with focused ion beam milling and a 2D analysis and 3D reconstruction made. The results showed homogeneous myelination via detection of ~40% content of lipids in all the different fibre tracts and the fibrous organisation of the tissue described as composite material presenting elliptical tubular fibres with an average cross-sectional area of circa 0.52\u3bcm2 and an estimated mean diameter of 1.15\u3bcm. Finally, as the project is currently ongoing, we provided an overview on the future experimental steps focalised on the brain tissue damage after the rigid catheter introduction

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

Evaluation of Upper Motor Neuron Pathology in Amyotrophic Lateral Sclerosis by Mri;Towards Identifying Noninvasive Biomarkers of the Disease

Amyotrophic lateral sclerosis (ALS) is the commonest adult motor neuron disease (MND) which causes progressive muscle paralysis and death usually within 5 years of symptom onset. As a result, only ̃30,000 individuals in the United States are afflicted at any one time even though 5,000 or more individuals are diagnosed yearly. The diagnosis of ALS requires evidence of degeneration in upper motor neurons (UMNs) in the brain and in lower motor neurons (LMNs) that exit the brainstem and spinal cord to innervate skeletal muscles. Diagnosis can be incorrect or delayed when disease is early or atypical because non-invasive objective tests of UMN involvement do not exist, unlike electromyography to assess the LMN. Although magnetic resonance imaging (MRI) of brain and spinal cord is used primarily to identify conditions which mimic ALS, novel MRI sequences and post-processing techniques can identify macroscopic and even sub-macroscopic changes in ALS brain related to neuronoaxonal degeneration (e.g., in corticospinal motor tracts). MRI-based techniques like diffusion tensor imaging (DTI) and proton magnetic resonance spectroscopy (1H-MRS), as well as nuclear medicine modalities like positron emission tomography (PET) and single photon emission tomography (SPECT) are being used to study brains of patients with ALS. Many previous MRI studies of ALS brain are limited either in methodology or information obtained being primarily qualitative, i.e. changes visible to the naked eye (macroscopic). This study employed both routine and novel MRI sequences to objectively assess gray and white matter pathology of the brain in ALS patients, including T2 relaxometry, DTI, and voxel based morphometry (VBM) of 3D high resolution T1-weighted images. DTI metrics showed significant (p\u3c 0.05) changes in rostral extent of corticospinal tract (CST) in ALS patients with predominantly UMN symptoms and signs, and the ALS-dementia patients, whereas more caudal involvement was observed in ALS patients with classic findings of UMN and LMN

Group analysis of DTI fiber tract statistics with application to neurodevelopment

Diffusion tensor imaging (DTI) provides a unique source of information about the underlying tissue structure of brain white matter in vivo including both the geometry of major fiber bundles as well as quantitative information about tissue properties represented by derived tensor measures. This paper presents a method for statistical comparison of fiber bundle diffusion properties between populations of diffusion tensor images. Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics. Diffusion properties, such as fractional anisotropy (FA) and tensor norm, along fiber tracts are modeled as multivariate functions of arc length. Hypothesis testing is performed non-parametrically using permutation testing based on the Hotelling T2 statistic. The linear discriminant embedded in the T2 metric provides an intuitive, localized interpretation of detected differences. The proposed methodology was tested on two clinical studies of neurodevelopment. In a study of one and two year old subjects, a significant increase in FA and a correlated decrease in Frobenius norm was found in several tracts. Significant differences in neonates were found in the splenium tract between controls and subjects with isolated mild ventriculomegaly (MVM) demonstrating the potential of this method for clinical studies

Vessel Tree Reconstruction with Divergence Prior

Accurate structure analysis of high-resolution 3D biomedical images of vessels is a challenging issue and in demand for medical diagnosis nowadays. Previous curvature regularization based methods [10, 31] give promising results. However, their mathematical models are not designed for bifurcations and generate significant artifacts in such areas. To address the issue, we propose a new geometric regularization principle for reconstructing vector fields based on prior knowledge about their divergence. In our work, we focus on vector fields modeling blood flow pattern that should be divergent in arteries and convergent in veins. We show that this previously ignored regularization constraint can significantly improve the quality of vessel tree reconstruction particularly around bifurcations where non-zero divergence is concentrated. Our divergence prior is critical for resolving (binary) sign ambiguity in flow orientations produced by standard vessel filters, e:g: Frangi. Our vessel tree centerline reconstruction combines divergence constraints with robust curvature regularization. Our unsupervised method can reconstruct complete vessel trees with near-capillary details on both synthetic and real 3D volumes. Also, our method reduces angular reconstruction errors at bifurcations by a factor of two

Near-tubular fiber bundle segmentation for diffusion weighted imaging: Segmentation through frame reorientation

This paper proposes a methodology to segment near-tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation. Utilizing a modification of a recent segmentation approach by Bresson et al. allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares favorably with segmentation by full-brain streamline tractography

Statistical Medial Model dor Cardiac Segmentation and Morphometry

In biomedical image analysis, shape information can be utilized for many purposes. For example, irregular shape features can help identify diseases; shape features can help match different instances of anatomical structures for statistical comparison; and prior knowledge of the mean and possible variation of an anatomical structure\u27s shape can help segment a new example of this structure in noisy, low-contrast images. A good shape representation helps to improve the performance of the above techniques. The overall goal of the proposed research is to develop and evaluate methods for representing shapes of anatomical structures. The medial model is a shape representation method that models a 3D object by explicitly defining its skeleton (medial axis) and deriving the object\u27s boundary via inverse-skeletonization . This model represents shape compactly, and naturally expresses descriptive global shape features like thickening , bending , and elongation . However, its application in biomedical image analysis has been limited, and it has not yet been applied to the heart, which has a complex shape. In this thesis, I focus on developing efficient methods to construct the medial model, and apply it to solve biomedical image analysis problems. I propose a new 3D medial model which can be efficiently applied to complex shapes. The proposed medial model closely approximates the medial geometry along medial edge curves and medial branching curves by soft-penalty optimization and local correction. I further develop a scheme to perform model-based segmentation using a statistical medial model which incorporates prior shape and appearance information. The proposed medial models are applied to a series of image analysis tasks. The 2D medial model is applied to the corpus callosum which results in an improved alignment of the patterns of commissural connectivity compared to a volumetric registration method. The 3D medial model is used to describe the myocardium of the left and right ventricles, which provides detailed thickness maps characterizing different disease states. The model-based myocardium segmentation scheme is tested in a heterogeneous adult MRI dataset. Our segmentation experiments demonstrate that the statistical medial model can accurately segment the ventricular myocardium and provide useful parameters to characterize heart function

Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community