Rapid Brain Meninges Surface Reconstruction with Layer Topology Guarantee

The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these layers can aid in studying volume differences between patients with neurodegenerative diseases and normal aging subjects. In this work, we use convolutional neural networks (CNNs) to reconstruct surfaces representing meningeal layer boundaries from magnetic resonance (MR) images. We first use the CNNs to predict the signed distance functions (SDFs) representing these surfaces while preserving their anatomical ordering. The marching cubes algorithm is then used to generate continuous surface representations; both the subarachnoid space (SAS) and the intracranial volume (ICV) are computed from these surfaces. The proposed method is compared to a state-of-the-art deformable model-based reconstruction method, and we show that our method can reconstruct smoother and more accurate surfaces using less computation time. Finally, we conduct experiments with volumetric analysis on both subjects with multiple sclerosis and healthy controls. For healthy and MS subjects, ICVs and SAS volumes are found to be significantly correlated to sex (p<0.01) and age (p<0.03) changes, respectively.Comment: ISBI 2023 Ora

A Novel Method for High-Dimensional Anatomical Mapping of Extra-Axial Cerebrospinal Fluid: Application to the Infant Brain

Cerebrospinal fluid (CSF) plays an essential role in early postnatal brain development. Extra-axial CSF (EA-CSF) volume, which is characterized by CSF in the subarachnoid space surrounding the brain, is a promising marker in the early detection of young children at risk for neurodevelopmental disorders. Previous studies have focused on global EA-CSF volume across the entire dorsal extent of the brain, and not regionally-specific EA-CSF measurements, because no tools were previously available for extracting local EA-CSF measures suitable for localized cortical surface analysis. In this paper, we propose a novel framework for the localized, cortical surface-based analysis of EA-CSF. The proposed processing framework combines probabilistic brain tissue segmentation, cortical surface reconstruction, and streamline-based local EA-CSF quantification. The quantitative analysis of local EA-CSF was applied to a dataset of typically developing infants with longitudinal MRI scans from 6 to 24 months of age. There was a high degree of consistency in the spatial patterns of local EA-CSF across age using the proposed methods. Statistical analysis of local EA-CSF revealed several novel findings: several regions of the cerebral cortex showed reductions in EA-CSF from 6 to 24 months of age, and specific regions showed higher local EA-CSF in males compared to females. These age-, sex-, and anatomically-specific patterns of local EA-CSF would not have been observed if only a global EA-CSF measure were utilized. The proposed methods are integrated into a freely available, open-source, cross-platform, user-friendly software tool, allowing neuroimaging labs to quantify local extra-axial CSF in their neuroimaging studies to investigate its role in typical and atypical brain development

A novel method for high-dimensional anatomical mapping of extra-axial cerebrospinal fluid: Application to the infant brain

Cerebrospinal fluid (CSF) plays an essential role in early postnatal brain development. Extra-axial CSF (EA-CSF) volume, which is characterized by CSF in the subarachnoid space surrounding the brain, is a promising marker in the early detection of young children at risk for neurodevelopmental disorders. Previous studies have focused on global EA-CSF volume across the entire dorsal extent of the brain, and not regionally-specific EA-CSF measurements, because no tools were previously available for extracting local EA-CSF measures suitable for localized cortical surface analysis. In this paper, we propose a novel framework for the localized, cortical surface-based analysis of EA-CSF. The proposed processing framework combines probabilistic brain tissue segmentation, cortical surface reconstruction, and streamline-based local EA-CSF quantification. The quantitative analysis of local EA-CSF was applied to a dataset of typically developing infants with longitudinal MRI scans from 6 to 24 months of age. There was a high degree of consistency in the spatial patterns of local EA-CSF across age using the proposed methods. Statistical analysis of local EA-CSF revealed several novel findings: several regions of the cerebral cortex showed reductions in EA-CSF from 6 to 24 months of age, and specific regions showed higher local EA-CSF in males compared to females. These age-, sex-, and anatomically-specific patterns of local EA-CSF would not have been observed if only a global EA-CSF measure were utilized. The proposed methods are integrated into a freely available, open-source, cross-platform, user-friendly software tool, allowing neuroimaging labs to quantify local extra-axial CSF in their neuroimaging studies to investigate its role in typical and atypical brain development

Methodology for a global bicycle real world accidents reconstruction

The use of the bicycle on a large scale encouraged in the context to develop an eco friendly environment is facing today on a range of barriers. One of these barriers identified by researchers and governments is observed to include ‘road safety’. Hence, it is necessary to set up a protection system for bicyclists especially for the cephalic segment. Currently only few studies are available concerning the head impact loading in case of real accidents. Therefore, the objective of this work is to identify the initial condition of head impact in case of real accident. Head impact velocity and head impact area are extracted and implemented in the last generation of head injury prediction tool to simulate the head trauma by impacting directly the Strasbourg University Finite Element Head Model (SUFEHM) on the vehicle structures. The present study can be divided into three activities i.e. obtain real bicyclist accidents data issued from in depth accident investigation databases, cyclist body kinematic reconstruction to obtain the initial conditions of the head just before the impact and head impact simulation to evaluate the head loading during impact and the injury risk. A total of 26 bicyclists’ accident cases with head injuries have been collected from both a French and a German accident database. For each accident case, body kinematic has been simulated using Madymo® software. Two methodologies and human multibody models were used: 10 accident cases have been reconstructed by IFSTTAR using its owned developed human model and 16 accident cases have been reconstructed by Unistra using the human pedestrian TNO model. The results show that the head is impacted more often on top parietal zone, and the mean impact velocity is 6.8 ± 2.7 m/s with 5.5 ± 3.0 m/s and 3.4 ± 2.1 m/s for normal and tangential components respectively. Among these real accidents, 19 cases have been selected to be simulated by finite element computations by coupling the human head model and a windscreen model whose properties were extracted from literature. All reconstructed head impact gave results in accordance with the damage actually incurred to the victims. The objective of this study is to demonstrate the feasibility of numerical reconstruction as an understanding tool of the head impact conditions in bicyclist's accident cases, and hence providing knowledge for helmet optimization using biomechanical criteria

BRAIN MENINGES SURFACE RECONSTRUCTION: APPLICATION TO LONGITUDINAL STUDY OF NORMAL AGING

The cranial meninges are membranes enveloping the brain. The space between two of these membranes contains cerebrospinal fluid. Changes in the meninges have been associated with many neurodegenerative diseases. It is of interest to study how the volumes of this space change with respect to normal aging. In this work, we propose to combine convolutional neural networks (CNNs) with nested topology-preserving geometric deformable models (NTGDMs) to reconstruct meningeal surfaces from magnetic resonance (MR) images. We first use CNNs to predict implicit representations of these surfaces then refine them with NTGDMs to achieve sub-voxel accuracy while maintaining spherical topology and the correct anatomical ordering. MR contrast harmonization is used to match the contrasts between training and testing images. We applied our algorithm to a subset of healthy subjects from the Baltimore Longitudinal Study of Aging for demonstration purposes and conducted longitudinal statistical analysis of the intracranial volume (ICV) and subarachnoid space (SAS) volume. We found a statistically significant decrease in the ICV and an increase in the SAS volume with respect to normal aging. Additionally, we conducted a preliminary study on 5 subjects, in which we assigned region labels to the meninges—using a fast marching algorithm from cortical labels—and calculated the thickness of the meningeal layers. In the future, we hope to apply the algorithms to larger datasets to further study the regional thickness changes in the meninges

Imaging-Based Patient-Specific Biomechanical Evaluation of Atherosclerosis and Aneurysm: A Comparison Between Structural-Only, Fluid-Only and Fluid–Structure Interaction Analysis

Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality worldwide. Atherosclerosis is the dominating underlying cause of CVD, that occurs at susceptible locations such as coronary and carotid arteries. The progression of atherosclerosis is a gradual process and most of the time asymptomatic until a catastrophic event occurs. Similarly, an intracranial aneurysm is the bulging of the cerebral artery due to a weakened area of the vessel wall. The progression of the aneurysm could result in the rupture of the vessel wall leading to a subarachnoid haemorrhage. The formation and progression of atherosclerosis and aneurysm are closely linked to abnormal blood flow behaviour and mechanical forces acting on the vessel wall. Recent technologies in medical imaging, modeling, and computation are used to estimate critical parameters from patient-specific data. However, there is still a need to develop protocols that are reproducible and efficient. This article focuses on the methods for biomechanical analysis of the cerebral aneurysms and atherosclerotic arteries including carotid & coronary. In this study, patient-specific 3D models were reconstructed from optical coherence imaging (OCT) for coronary and magnetic resonance imaging (MRI) for the carotid and cerebral arteries. The reconstructed models were used for computational fluid dynamics (CFD), structural-only, and fluid–structure interaction (FSI) simulations. The results of the FSI were compared against structural and CFD-only simulations to identify the most suitable method for each artery. The comparison between FSI and structural only simulations for the coronary artery showed similar mechanical stress values across the cardiac cycle with a maximum difference of 1.8%. However, the results for the carotid and cerebral arteries showed a maximum difference of 5% and 20% respectively. Additionally, with relation to the hemodynamic WSS calculated from FSI and CFD-only, the coronary artery presented a significant difference of 87%. Conversely, the results for the carotid and cerebral arteries showed a maximum difference of 9 and 6.4% at systole. Based on the results it can be concluded that the shape & location of the artery will influence the selection of the model that can be used for solving the numerical problem

Understanding the role of hemodynamics in the initiation, progression, rupture, and treatment outcome of cerebral aneurysm from medical iamge-based computational studies

About a decade ago, the first image-based computational hemodynamic studies of cerebral aneurysms were presented. Their potential for clinical applications was the results of a right combination of medical image processing, vascular reconstruction, and grid generation techniques used to reconstruct personalziaed domains for computational fluid and solid dynamics solvers and data analysis and visualization techniques. A considerable number of studies have captivated the attention of clinicians, neurosurgeons, and neuroradiologists, who realized the ability of those tools to help in understanding the role played by hemodynamics in the natural history and management of intracranial aneurysms. This paper intends to summarize the most relevant results in the filed reported during the last years.Fil: Castro, Marcelo Adrian. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

A (Near) Real-Time Simulation Method of Aneurysm Coil Embolization

International audienceA (Near) Real-Time Simulation Method of Aneurysm Coil Embolizatio