Thematische Bibliographien / Brain microstructure imaging

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Auswahl der wissenschaftlichen Literatur zum Thema „Brain microstructure imaging“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "Brain microstructure imaging"

Nilsson, Markus, Elisabet Englund, Filip Szczepankiewicz, Danielle van Westen und Pia C. Sundgren. „Imaging brain tumour microstructure“. NeuroImage 182 (November 2018): 232–50. http://dx.doi.org/10.1016/j.neuroimage.2018.04.075.

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Alotaibi, Abdulmajeed, Christopher Tench, Rebecca Stevenson, Ghadah Felmban, Amjad Altokhis, Ali Aldhebaib, Rob A. Dineen und Cris S. Constantinescu. „Investigating Brain Microstructural Alterations in Type 1 and Type 2 Diabetes Using Diffusion Tensor Imaging: A Systematic Review“. Brain Sciences 11, Nr. 2 (22.01.2021): 140. http://dx.doi.org/10.3390/brainsci11020140.

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Type 1 and type 2 diabetes mellitus have an impact on the microstructural environment and cognitive functions of the brain due to its microvascular/macrovascular complications. Conventional Magnetic Resonance Imaging (MRI) techniques can allow detection of brain volume reduction in people with diabetes. However, conventional MRI is insufficiently sensitive to quantify microstructural changes. Diffusion Tensor Imaging (DTI) has been used as a sensitive MRI-based technique for quantifying and assessing brain microstructural abnormalities in patients with diabetes. This systematic review aims to summarise the original research literature using DTI to quantify microstructural alterations in diabetes and the relation of such changes to cognitive status and metabolic profile. A total of thirty-eight published studies that demonstrate the impact of diabetes mellitus on brain microstructure using DTI are included, and these demonstrate that both type 1 diabetes mellitus and type 2 diabetes mellitus may affect cognitive abilities due to the alterations in brain microstructures.

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Reislev, Nina Linde, Tim Bjørn Dyrby, Hartwig Roman Siebner, Ron Kupers und Maurice Ptito. „Simultaneous Assessment of White Matter Changes in Microstructure and Connectedness in the Blind Brain“. Neural Plasticity 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/6029241.

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Magnetic resonance imaging (MRI) of the human brain has provided converging evidence that visual deprivation induces regional changes in white matter (WM) microstructure. It remains unclear how these changes modify network connections between brain regions. Here we used diffusion-weighted MRI to relate differences in microstructure and structural connectedness of WM in individuals with congenital or late-onset blindness relative to normally sighted controls. Diffusion tensor imaging (DTI) provided voxel-specific microstructural features of the tissue, while anatomical connectivity mapping (ACM) assessed the connectedness of each voxel with the rest of the brain. ACM yielded reduced anatomical connectivity in the corpus callosum in individuals with congenital but not late-onset blindness. ACM did not identify any brain region where blindness resulted in increased anatomical connectivity. DTI revealed widespread microstructural differences as indexed by a reduced regional fractional anisotropy (FA). Blind individuals showed lower FA in the primary visual and the ventral visual processing stream relative to sighted controls regardless of the blindness onset. The results show that visual deprivation shapes WM microstructure and anatomical connectivity, but these changes appear to be spatially dissociated as changes emerge in different WM tracts. They also indicate that regional differences in anatomical connectivity depend on the onset of blindness.

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Dinkel, Johannes G., Godehard Lahmer, Angelika Mennecke, Stefan W. Hock, Tanja Richter-Schmidinger, Rainer Fietkau, Luitpold Distel, Florian Putz, Arnd Dörfler und Manuel A. Schmidt. „Effects of Hippocampal Sparing Radiotherapy on Brain Microstructure—A Diffusion Tensor Imaging Analysis“. Brain Sciences 12, Nr. 7 (04.07.2022): 879. http://dx.doi.org/10.3390/brainsci12070879.

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Hippocampal-sparing radiotherapy (HSR) is a promising approach to alleviate cognitive side effects following cranial radiotherapy. Microstructural brain changes after irradiation have been demonstrated using Diffusion Tensor Imaging (DTI). However, evidence is conflicting for certain parameters and anatomic structures. This study examines the effects of radiation on white matter and hippocampal microstructure using DTI and evaluates whether these may be mitigated using HSR. A total of 35 tumor patients undergoing a prospective randomized controlled trial receiving either conventional or HSR underwent DTI before as well as 6, 12, 18, 24, and 30 (±3) months after radiotherapy. Fractional Anisotropy (FA), Mean Diffusivity (MD), Axial Diffusivity (AD), and Radial Diffusivity (RD) were measured in the hippocampus (CA), temporal, and frontal lobe white matter (TL, FL), and corpus callosum (CC). Longitudinal analysis was performed using linear mixed models. Analysis of the entire patient collective demonstrated an overall FACC decrease and RDCC increase compared to baseline in all follow-ups; ADCC decreased after 6 months, and MDCC increased after 12 months (p ≤ 0.001, 0.001, 0.007, 0.018). ADTL decreased after 24 and 30 months (p ≤ 0.004, 0.009). Hippocampal FA increased after 6 and 12 months, driven by a distinct increase in ADCA and MDCA, with RDCA not increasing until 30 months after radiotherapy (p ≤ 0.011, 0.039, 0.005, 0.040, 0.019). Mean radiation dose correlated positively with hippocampal FA (p < 0.001). These findings may indicate complex pathophysiological changes in cerebral microstructures after radiation, insufficiently explained by conventional DTI models. Hippocampal microstructure differed between patients undergoing HSR and conventional cranial radiotherapy after 6 months with a higher ADCA in the HSR subgroup (p ≤ 0.034).

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Middleton, Dana M., Jonathan Y. Li, Hui J. Lee, Steven Chen, Patricia I. Dickson, N. Matthew Ellinwood, Leonard E. White und James M. Provenzale. „Diffusion tensor imaging tensor shape analysis for assessment of regional white matter differences“. Neuroradiology Journal 30, Nr. 4 (20.06.2017): 324–29. http://dx.doi.org/10.1177/1971400917709628.

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Purpose The purpose of this study was to investigate a novel tensor shape plot analysis technique of diffusion tensor imaging data as a means to assess microstructural differences in brain tissue. We hypothesized that this technique could distinguish white matter regions with different microstructural compositions. Methods Three normal canines were euthanized at seven weeks old. Their brains were imaged using identical diffusion tensor imaging protocols on a 7T small-animal magnetic resonance imaging system. We examined two white matter regions, the internal capsule and the centrum semiovale, each subdivided into an anterior and posterior region. We placed 100 regions of interest in each of the four brain regions. Eigenvalues for each region of interest triangulated onto tensor shape plots as the weighted average of three shape metrics at the plot's vertices: CS, CL, and CP. Results The distribution of data on the plots for the internal capsule differed markedly from the centrum semiovale data, thus confirming our hypothesis. Furthermore, data for the internal capsule were distributed in a relatively tight cluster, possibly reflecting the compact and parallel nature of its fibers, while data for the centrum semiovale were more widely distributed, consistent with the less compact and often crossing pattern of its fibers. This indicates that the tensor shape plot technique can depict data in similar regions as being alike. Conclusion Tensor shape plots successfully depicted differences in tissue microstructure and reflected the microstructure of individual brain regions. This proof of principle study suggests that if our findings are reproduced in larger samples, including abnormal white matter states, the technique may be useful in assessment of white matter diseases.

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Paus, Tomáš. „Imaging microstructure in the living human brain: A viewpoint“. NeuroImage 182 (November 2018): 3–7. http://dx.doi.org/10.1016/j.neuroimage.2017.10.013.

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Alexander, Daniel C., Tim B. Dyrby, Markus Nilsson und Hui Zhang. „Imaging brain microstructure with diffusion MRI: practicality and applications“. NMR in Biomedicine 32, Nr. 4 (29.11.2017): e3841. http://dx.doi.org/10.1002/nbm.3841.

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Dimitrova, Ralica, Maximilian Pietsch, Daan Christiaens, Judit Ciarrusta, Thomas Wolfers, Dafnis Batalle, Emer Hughes et al. „Heterogeneity in Brain Microstructural Development Following Preterm Birth“. Cerebral Cortex 30, Nr. 9 (18.04.2020): 4800–4810. http://dx.doi.org/10.1093/cercor/bhaa069.

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Abstract Preterm-born children are at increased risk of lifelong neurodevelopmental difficulties. Group-wise analyses of magnetic resonance imaging show many differences between preterm- and term-born infants but do not reliably predict neurocognitive prognosis for individual infants. This might be due to the unrecognized heterogeneity of cerebral injury within the preterm group. This study aimed to determine whether atypical brain microstructural development following preterm birth is significantly variable between infants. Using Gaussian process regression, a technique that allows a single-individual inference, we characterized typical variation of brain microstructure using maps of fractional anisotropy and mean diffusivity in a sample of 270 term-born neonates. Then, we compared 82 preterm infants to these normative values to identify brain regions with atypical microstructure and relate observed deviations to degree of prematurity and neurocognition at 18 months. Preterm infants showed strikingly heterogeneous deviations from typical development, with little spatial overlap between infants. Greater and more extensive deviations, captured by a whole brain atypicality index, were associated with more extreme prematurity and predicted poorer cognitive and language abilities at 18 months. Brain microstructural development after preterm birth is highly variable between individual infants. This poorly understood heterogeneity likely relates to both the etiology and prognosis of brain injury.

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Huang, Tzu-Hsin, Ming-Chi Lai, Yu-Shiue Chen und Chin-Wei Huang. „Brain Imaging in Epilepsy-Focus on Diffusion-Weighted Imaging“. Diagnostics 12, Nr. 11 (27.10.2022): 2602. http://dx.doi.org/10.3390/diagnostics12112602.

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Epilepsy is a common neurological disorder; 1% of people worldwide have epilepsy. Differentiating epileptic seizures from other acute neurological disorders in a clinical setting can be challenging. Approximately one-third of patients have drug-resistant epilepsy that is not well controlled by current antiepileptic drug therapy. Surgical treatment is potentially curative if the epileptogenic focus is accurately localized. Diffusion-weighted imaging (DWI) is an advanced magnetic resonance imaging technique that is sensitive to the diffusion of water molecules and provides additional information on the microstructure of tissue. Qualitative and quantitative analysis of peri-ictal, postictal, and interictal diffusion images can aid the differential diagnosis of seizures and seizure foci localization. This review focused on the fundamentals of DWI and its associated techniques, such as apparent diffusion coefficient, diffusion tensor imaging, and tractography, as well as their impact on epilepsy in terms of differential diagnosis, epileptic foci determination, and prognosis prediction.

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Martinot, J. l. „CS02-03 - Imaging depression“. European Psychiatry 26, S2 (März 2011): 1773. http://dx.doi.org/10.1016/s0924-9338(11)73477-0.

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ContextPET and MRI investigations performed in patient groups with major depressive disorder (MDD) by our team in Orsay searched for differences of regional brain measures during treatments.ResultsIn the patient samples investigated, thorough analysis of cortical surface and metabolism suggested marked deviations in patients with resistant depression, while abnormalities of white matter microstructure were still present in euthymic patients (1–4). Relationship with treatment response was investigated (5). Recent ALE meta-analysis of Talairach’ spatial coordinates reported in the literature on adolescent MDD confirms that imaging techniques of brain function and brain structure revealed a consistent network of frontal limbic and subcortical regions (1)ConclusionWhile the diagnosis of MDD is symptom-based by definition, brain imaging research provided a bunch of convergent information on the regions mediating the depressive syndrome, and supports a significant proportion of MDD patients have brain deviations in both regional function and regional structure measurements.

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Dissertationen zum Thema "Brain microstructure imaging"

Panagiotaki, E. „Geometric models of brain white matter for microstructure imaging with diffusion MRI“. Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1310435/.

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The research presented in this thesis models the diffusion-weighted MRI signal within brain white matter tissue. We are interested in deriving descriptive microstructure indices such as white matter axon diameter and density from the observed diffusion MRI signal. The motivation is to obtain non-invasive reliable biomarkers for early diagnosis and prognosis of brain development and disease. We use both analytic and numerical models to investigate which properties of the tissue and aspects of the diffusion process affect the diffusion signal we measure. First we develop a numerical method to approximate the tissue structure as closely as possible. We construct three-dimensional meshes, from a stack of confocal microscopy images using the marching cubes algorithm. The experiment demonstrates the technique using a biological phantom (asparagus). We devise an MRI protocol to acquire data from the sample. We use the mesh models as substrates in Monte-Carlo simulations to generate synthetic MRI measurements. To test the feasibility of the method we compare simulated measurements from the three-dimensional mesh with scanner measurements from the same sample and simulated measurements from an extruded mesh and much simpler parametric models. The results show that the three-dimensional mesh model matches the data better than the extruded mesh and the parametric models revealing the sensitivity of the diffusion signal to the microstructure. The second study constructs a taxonomy of analytic multi-compartment models of white matter by combining intra- and extra-axonal compartments from simple models. We devise an imaging protocol that allows diffusion sensitisation parallel and perpendicular to tissue fibres. We use the protocol to acquire data from two fixed rat brains, which allows us to fit, study and evaluate the models. We conclude that models which incorporate non-zero axon radius describe the measurements most accurately. The key observation is a departure of signals in the parallel direction from the two-compartment models, suggesting restriction, most likely from glial cells or binding of water molecules to the membranes. The addition of the third compartment can capture this departure and explain the data. The final study investigates the estimates using in vivo brain diffusion measurements. We adjust the imaging protocol to allow an in vivo MRI acquisition of a rat brain and compare and assess the taxonomy of models. We then select the models that best explain the in vivo data and compare the estimates with those from the ex vivo measurements to identify any discrepancies. The results support the addition of the third compartment model as per the ex vivo findings, however the ranking of the models favours the zero radius intra-axonal compartments.

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Novello, Lisa. „Towards Improving the Specificity of Human Brain Microstructure Research with Diffusion-Weighted MRI“. Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/342277.

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The possibility to perform virtual, non-invasive, quantitative, in vivo histological assessments might revolutionize entire fields, among which clinical and cognitive neurosciences. Magnetic Resonance Imaging (MRI) is an ideal non-invasive imaging technique to achieve these goals. Tremendous advancements in the last decades have favored the transition of MRI scanners from “imaging devices” to “measurement devices” (Novikov, 2021), thus capable to yield measurements in physical units, which might be further combined to provide quantities describing histological properties of substrates. A central role in this community endeavor has been played by diffusion-weighted MRI (dMRI), which by measuring the dynamics of spin diffusion, allows inferences on geometrical properties of tissues. Yet, conventional dMRI methodologies suffer from poor specificity. In this thesis, techniques aiming at improving the specificity of microstructural descriptions have been explored in dMRI datasets supporting an increasing level of complexity of the dMRI signal representations. Applications in individuals with different age range, in different populations, and for different MRI scanner fields, have been considered. Firstly, tractography has been combined with Diffusion Tensor Imaging (DTI), an along-tract framework, and morphometry, in the study of the microstructure of the optic radiations in different groups of blind individuals. Secondly, DTI has been combined with Free-Water Imaging (FWI) to monitor the effect of proton-irradiation in a pediatric brain tumor case study. Thirdly, FWI and Diffusion Kurtosis Imaging (DKI) have been combined with an advanced thalamic segmentation framework to study the associations between motor performance and thalamic microstructure in a cohort of individuals affected by Parkinson’s disease. Finally, the largest contribution of this thesis is represented by the adaptation of the Correlation Tensor Imaging - a technique increasing the specificity of DKI harnessing Double Diffusion Encoding previously applied only in preclinical settings - for a clinical 3 T scanner. The ensuing investigation revealed new important insights on the sources of diffusional kurtosis, in particular of the microscopic kurtosis (μK), a component so far neglected by contemporary neuroimaging techniques, which might carry an important clinical role (Alves et al., 2022), and can now be accessed by clinical scanners. In conclusion, strategies to increase the specificity of microstructural descriptions in the brain are presented for different datasets, and their strength and limitations are discussed.

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Lacerda, Luis Miguel Rosa Sousa Prado De. „Quantitative white matter metrics : diffusion imaging and advanced processing for detailed investigation of brain microstructure“. Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/quantitative-white-matter-metrics(9058c64a-93a0-4db0-9799-c0bba7bd55fe).html.

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Diffusion imaging is a non-invasive imaging method which has been successfully applied to study white matter. Most clinical approaches, based on Diffusion Tensor Imaging (DTI), are limited by the simple model of the underlying tissue imposed, failing to reconstruct the diffusion propagator, which fully encodes the displacement of water molecules. To do so, more comprehensive sampling schemes such as Diffusion Spectrum Imaging (DSI) have been developed. In this thesis, I have investigated the effect of different tissue configurations, sampling and processing steps in the performance of DSI. I identified specific configurations where DSI is unable to characterise diffusion without artefacts, namely aliasing caused by fast diffusion components. Furthermore, processing of the diffusion orientation distribution function (ODF) in these environments can lead to generation of spurious fibres in tractography reconstructions. To overcome this, I have applied a novel step in the processing pipeline of DSI, namely a different way of computing the ODF, which consists of restricting the range of integration to probabilities based on the physical displacement of “axonlike” diffusivities. Alternatively, it is possible to use a mathematical representation of the acquired signal, of which the Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) and Mean Apparent Propagator Magnetic Resonance Imaging (MAP-MRI) are examples. I have here used these methods and further provided optimised acquisitions based on standard propagator metrics. Finally, I have introduced new metrics that use microstructural information available at the different displacement scales, and can facilitate exploration of brain organisation even when no a-priori biophysical model is available.

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Beaujoin, Justine. „Post mortem inference of the human brain microstructure using ultra-high field magnetic resonance imaging with strong gradients“. Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS448/document.

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L’ambition des très hauts champs magnétiques (≥ 7T) à forts gradients (≥ 300mT/m) est de dépasser la résolution millimétrique imposée à plus bas champ pour atteindre l’échelle mésoscopique en neuroimagerie. Etudier le cerveau à cette échelle est essentiel pour comprendre le lien entre fonction et substrat anatomique. Malgré les progrès réalisés sur les aimants cliniques à 7T, il n’en est pas de même des gradients. Cette thèse vise à cartographier le cerveau humain à l’échelle mésoscopique via l’étude de pièces anatomiques post mortem. Une approche alternative a été choisie, reposant sur l'utilisation d'imageurs précliniques à très hauts champs (7T et 11.7T) et forts gradients (780mT/m). Après une première étape de préparation (extraction et fixation) opérée au CHU de Tours, une pièce anatomique complète a été scannée à 3T, avant découpe de l’hémisphère gauche en sept blocs. Un protocole d’acquisition IRM ciblant une résolution mésoscopique a ensuite été mis en place à 11.7T. Ce protocole, incluant des séquences anatomiques, relaxométriques, et de diffusion, a été validé à l’aide de deux structures clé: un hippocampe et un tronc cérébral. Les données anatomiques et de diffusion acquises à une résolution mésoscopique sur l’hippocampe ont permis de segmenter ses sous-champs, d’extraire le circuit polysynaptique et d’observer l’existence d’un gradient de connectivité et de densité neuritique positif dans la direction postéro-antérieure de l’hippocampe. L’utilisation de modèles avancés d’étude de la microstructure a également révélé l’apport de ces techniques pour la segmentation de l’hippocampe, les cartes de densité neuritique révélant les trois couches des champs ammoniens. Un tronc cérébral a ensuite été scanné, avec une résolution atteignant la centaine de micromètres. Une segmentation de 53 de ses 71 noyaux a été réalisée au sein du CHU de Tours, permettant d’établir la cartographie IRM du tronc cérébral humain la plus complète à ce jour. Les principaux faisceaux de la substance blanche ont été reconstruits, ainsi que les projections du locus coeruleus, structure connue pour être atteinte dans le maladie de Parkinson. Forts de ces résultats, la campagne d'acquisition de l'hémisphère gauche, d’une durée de 10 mois, a été initiée. Le protocole d’acquisition à 11.7T intègre des séquences anatomiques (100/150µm) ainsi que des séquences d'imagerie 3D pondérées en diffusion (b=1500/4500/8000 s/mm², 25/60/90 directions) à 200µm. Des acquisitions complémentaires réalisées à 7T comprenant des séquence d’écho de spin rapide avec inversion-récupération ont par ailleurs permis d’étudier la myéloarchitecture du cortex cérébral et d’identifier automatiquement sa structure laminaire. Un nouveau modèle de mélange de Gaussiennes a été développé, intégrant les informations myéloarchitecturales issues de la cartographie T1 et les informations cytoarchitecturales issues de l’imagerie de diffusion. Il a ainsi pu être démontré que l’utilisation conjointe de ces deux informations permettait de mettre en évidence des couches du cortex visuel, l’information myéloarchitecturale favorisant l’extraction des couches externes et la densité neuritique celle des couches plus profondes. Enfin, l’exploitation des données IRM acquises à 11.7T sur les différents blocs a nécessité la mise en place d’une chaîne de prétraitements pour corriger les artéfacts d’imagerie et reconstruire l’hémisphère entier à l’aide de stratégies de recalage difféomorphe avancées. L’objectif de ce projet est l’obtention d’un jeu de données IRM de très haute résolution spatio-angulaire de l’hémisphère gauche. Ce jeu de données anatomique et de diffusion unique permettra à terme de constituer un nouvel atlas IRM mésoscopique de la structure, de la connectivité et de la cytoarchitecture du cerveau humain
The aim of ultra-high field strength (≥7T) and ultra-strong gradient systems (≥300mT/m) is to go beyond the millimeter resolution imposed at lower field and to reach the mesoscopic scale in neuroimaging. This scale is essential to understand the link between brain structure and function. However, despite recent technological improvements of clinical UHF-MRI, gradient systems remain too limited to reach this resolution. This thesis aims at answering the need for mapping the human brain at a mesoscopic scale by the study of post mortem samples. An alternative approach has been developed, based on the use of preclinical systems equipped with ultra-high fields (7T/11.7T) and strong gradients (780mT). After its extraction and fixation at Bretonneau University Hospital (Tours), an entire human brain specimen was scanned on a 3T clinical system, before separating its two hemispheres and cutting each hemisphere into seven blocks that could fit into the small bore of an 11.7T preclinical system. An MRI acquisition protocol targeting a mesoscopic resolution was then set up at 11.7T. This protocol, including anatomical, quantitative, and diffusion-weighted sequences, was validated through the study of two key structures: the hippocampus and the brainstem. From the high resolution anatomical and diffusion dataset of the human hippocampus, it was possible to segment the hippocampal subfields, to extract the polysynaptic pathway, and to observe a positive gradient of connectivity and neuritic density in the posterior-anterior direction of the hippocampal formation. The use of advanced microstructural models (NODDI) also highlighted the potential of these techniques to reveal the laminar structure of the Ammon’s horn. A high resolution anatomical and diffusion MRI dataset was obtained from the human brainstem with an enhanced resolution of a hundred micrometers. The segmentation of 53 of its 71 nuclei was performed at the Bretonneau University Hospital, making it the most complete MR-based segmentation of the human brainstem to date. Major white matter bundles were reconstructed, as well as projections of the locus coeruleus, a structure known to be impaired in Parkinson’s disease. Buoyed by these results, a dedicated acquisition campaign targeting the entire left hemisphere was launched for total scan duration of 10 months. The acquisition protocol was performed at 11.7T and included high resolution anatomical sequences (100/150μm) as well as 3D diffusion-weighted sequences (b=1500/4500/8000 s/mm², 25/60/90 directions, 200μm). In addition, T1-weighted inversion recovery turbo spin echo scans were performed at 7T to further investigate the myeloarchitecture of the cortical ribbon at 300µm, revealing its laminar structure. A new method to automatically segment the cortical layers was developed relying on a Gaussian mixture model integrating both T1-based myeloarchitectural information and diffusion-based cytoarchitectural information. The results gave evidence that the combination of these two contrasts highlighted the layers of the visual cortex, the myeloarchitectural information favoring the extraction of the outer layers and the neuritic density favoring the extraction of the deeper layers. Finally, the analysis of the MRI dataset acquired at 11.7T on the seven blocks required the development of a preprocessing pipeline to correct artifacts and to reconstruct the entire hemisphere using advanced registration methods. The aim was to obtain an ultra-high spatio-angular resolution MRI dataset of the left hemisphere, in order to establish a new mesoscopic post mortem MRI atlas of the human brain, including key information about its structure, connectivity and microstructure

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Neto, Henriques Rafael. „Advanced methods for diffusion MRI data analysis and their application to the healthy ageing brain“. Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/281993.

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Diffusion of water molecules in biological tissues depends on several microstructural properties. Therefore, diffusion Magnetic Resonance Imaging (dMRI) is a useful tool to infer and study microstructural brain changes in the context of human development, ageing and neuropathology. In this thesis, the state-of-the-art of advanced dMRI techniques is explored and strategies to overcome or reduce its pitfalls are developed and validated. Firstly, it is shown that PCA denoising and Gibbs artefact suppression algorithms provide an optimal compromise between increased precision of diffusion measures and the loss of tissue's diffusion non-Gaussian information. Secondly, the spatial information provided by the diffusion kurtosis imaging (DKI) technique is explored and used to resolve crossing fibres and generalize diffusion measures to cases not limited to well-aligned white matter fibres. Thirdly, as an alternative to diffusion microstructural modelling techniques such as the neurite orientation dispersion and density imaging (NODDI), it is shown that spherical deconvolution techniques can be used to characterize fibre crossing and dispersion simultaneously. Fourthly, free water volume fraction estimates provided by the free water diffusion tensor imaging (fwDTI) are shown to be useful to detect and remove voxels corrupted by cerebrospinal fluid (CSF) partial volume effects. Finally, dMRI techniques are applied to the diffusion data from the large collaborative Cambridge Centre for Ageing and Neuroscience (CamCAN) study. From these data, the inference provided by diffusion anisotropy measures on maturation and degeneration processes is shown to be biased by age-related changes of fibre organization. Inconsistencies of previous NODDI ageing studies are also revealed to be associated with the different age ranges covered. The CamCAN data is also processed using a novel non-Gaussian diffusion characterization technique which is invariant to different fibre configurations. Results show that this technique can provide indices specific to axonal water fraction which can be linked to age-related fibre density changes.

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Fang, Chengran. „Neuron modeling, Bloch-Torrey equation, and their application to brain microstructure estimation using diffusion MRI“. Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG010.

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L'estimation non invasive de la microstructure du cerveau, qui se compose de nombreux neurites, de somas et de cellules gliales, est essentielle pour l'imagerie cérébrale. L'IRM de diffusion (IRMd) est une technique prometteuse pour sonder les propriétés microstructurelles du cerveau en dessous de la résolution spatiale des scanners IRM. En raison de la complexité structurelle du tissu cérébral et du mécanisme complexe de l'IRM de diffusion, l'estimation de la microstructure in vivo est un défi. Les méthodes existantes utilisent généralement des géométries simplifiées, notamment des sphères et des bâtons, pour modéliser les structures neuronales et obtenir des expressions analytiques des signaux intracellulaires. La validité des hypothèses faites par ces méthodes reste indéterminée. Cette thèse vise à faciliter l'estimation de la microstructure du cerveau par simulation en remplaçant les géométries simplifiées par des modèles réalistes de la géométrie des neurones et les expressions analytiques des signaux intracellulaires par des simulations d'IRM de diffusion. Combinées à des modèles précis de la géométrie des neurones, les simulations numériques d'IRMd peuvent donner des signaux intracellulaires précis et incorporer les effets dus, par exemple, à l'ondulation des neurites ou à l'échange d'eau entre le soma et les neurites.Malgré ces avantages, les simulations d'IRMd n'ont pas été largement adoptées en raison de l'inaccessibilité des fantômes numériques, de la faible efficacité de calcul des simulateurs d'IRMd et de la difficulté d'approximer les mappings implicites entre les signaux d'IRMd et les propriétés de la microstructure. Cette thèse contribue à la résolution des problèmes susmentionnés de la manière suivante : (1) en développant un générateur de maillage de neurones open-source et en rendant accessibles au public plus d'un millier de maillages cellulaires réalistes ; (2) en augmentant d'un facteur dix l'efficacité de calcul de la méthode du formalisme matriciel numérique ; (3) en mettant en œuvre une nouvelle méthode de simulation qui fournit une représentation de type Fourier des signaux IRMd ; (4) en proposant un cadre d'apprentissage supervisé basé sur la simulation pour estimer la microstructure du cerveau par IRM de diffusion
Non-invasively estimating brain microstructure that consists of a very large number of neurites, somas, and glial cells is essential for future neuroimaging. Diffusion MRI (dMRI) is a promising technique to probe brain microstructural properties below the spatial resolution of MRI scanners. Due to the structural complexity of brain tissue and the intricate diffusion MRI mechanism, in vivo microstructure estimation is challenging.Existing methods typically use simplified geometries, particularly spheres, and sticks, to model neuronal structures and to obtain analytical expressions of intracellular signals. The validity of the assumptions made by these methods remains undetermined. This thesis aims to facilitate simulationdriven brain microstructure estimation by replacing simplified geometries with realistic neuron geometry models and the analytical intracellular signal expressions with diffusion MRI simulations. Combined with accurate neuron geometry models, numerical dMRI simulations can give accurate intracellular signals and seamlessly incorporate effects arising from, for instance, neurite undulation or water exchange between soma and neurites.Despite these advantages, dMRI simulations have not been widely adopted due to the difficulties in constructing realistic numerical phantoms, the high computational cost of dMRI simulations, and the difficulty in approximating the implicit mappings between dMRI signals and microstructure properties. This thesis addresses the above problems by making four contributions. First, we develop a high-performance opensource neuron mesh generator and make publicly available over a thousand realistic cellular meshes.The neuron mesh generator, swc2mesh, can automatically and robustly convert valuable neuron tracing data into realistic neuron meshes. We have carefully designed the generator to maintain a good balance between mesh quality and size. A neuron mesh database, NeuronSet, which contains 1213 simulation-ready cell meshes and their neuroanatomical measurements, was built using the mesh generator. These meshes served as the basis for further research. Second, we increased the computational efficiency of the numerical matrix formalism method by accelerating the eigendecomposition algorithm and exploiting GPU computing. The speed was increased tenfold. With similar accuracy, the optimized numerical matrix formalism is 20 times faster than the FEM method and 65 times faster than a GPU-based Monte Carlo method. By performing simulations on realistic neuron meshes, we investigated the effect of water exchange between somas and neurites, and the relationship between soma size and signals. We then implemented a new simulation method that provides a Fourier-like representation of the dMRI signals. This method was derived theoretically and implemented numerically. We validated the convergence of the method and showed that the error behavior is consistent with our error analysis. Finally, we propose a simulation-driven supervised learning framework to estimate brain microstructure using diffusion MRI. By exploiting the powerful modeling and computational capabilities that are mentioned above, we have built a synthetic database containing the dMRI signals and microstructure parameters of 1.4 million artificial brain voxels. We have shown that this database can help approximate the underlying mappings of the dMRI signals to volume and surface fractions using artificial neural networks

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Bihan-Poudec, Yann. „IRM de diffusion cérébrale à haute résolution : développements des méthodes de reconstruction et de post-traitement“. Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1299.

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L'imagerie de diffusion (IRMd) est une méthode unique permettant d'étudier la microstructure cérébrale et la connectivité du cerveau de manière non-invasive. Cependant, la faible résolution et la qualité de cette imagerie restreint son utilisation dans certaines applications. L'objectif de cette thèse est de développer l'IRMd cérébrale à très haute résolution sur un modèle de macaque anesthésié au moyen d'une séquence d'imagerie 3D écho-planaire segmentée (3D-msEPI) à 3T. Après une étape de développement de la reconstruction et du post-traitement des données, nous avons réalisé des images de diffusion sur le cerveau de macaque à une résolution spatiale isotrope de 0.5mm. Cette résolution nous a permis de délimiter et caractériser les structures fines comme les sous-couches de l'hippocampe ou la matière blanche superficielle, qui sont indétectables avec des séquences classiques. Cependant, cette méthode se révèle vulnérable aux mouvements élastiques des tissus cérébraux induits par les pulsations cardio-vasculaires. Une stratégie de synchronisation de l'acquisition sur celle-ci nous a permis de caractériser leurs effets sur l'IRMd à très haute résolution chez le singe anesthésié. Ces effets se caractérisent par des artefacts de ghosting et des pertes de signal qui corrompent les images, le tenseur et la tractographie dans des zones spécifiques du cerveau. La synchronisation nous a ainsi permis de réaliser une imagerie de diffusion cérébrale de macaque à des résolutions spatiales et des pondérations en diffusion très élevées jamais atteintes auparavant. Ces résultats préliminaires démontrent le potentiel de notre méthode pour les applications neuroscientifiques et médicales chez l'homme
Diffusion imaging (dMRI) is a unique method for studying brain microstructure and brain connectivity in a non-invasive way. However, the low resolution and quality of this imaging restricts its use in some applications. The aim of this thesis is to develop very high resolution cerebral MRI on an anesthetized macaque model on a 3T scanner using a segmented 3D echo-planar 3D imaging sequence (3D-msEPI). After a stage of development of the reconstruction and post-processing of the data, we made diffusion images on the macaque brain at an isotropic spatial resolution of 0.5mm. This resolution allowed us to delineate and characterize fine structures such as hippocampal sublayers or superficial white matter, which are undetectable with classical sequences. However, this method is vulnerable to the elastic movements of the brain tissue induced by the cardiovascular pulsations. A strategy of synchronization of the acquisition on this one allowed us to characterize their effects on the very high resolution MRI in the anesthetized monkey. These effects are characterized by ghosting artifacts and signal losses that corrupt images, tensor, and tractography in specific areas of the brain. The synchronization allowed us to realize macaque brain diffusion imaging at spatial resolutions and very high diffusion weights never reached before. These preliminary results demonstrate the potential of our method for neuroscientific and medical applications in humans

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Horne, Nikki Renee. „Microstructural white matter changes in Alzheimer's disease a diffusion tensor imaging study /“. Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3296903.

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Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2008.
Title from first page of PDF file (viewed April 7, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 127-149).

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Gongvatana, Assawin. „Microstructural white matter integrity in HIV-infected individuals in the HAART era a diffusion tensor imaging study /“. Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3316192.

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Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2008.
Title from first page of PDF file (viewed September 4, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 81-94).

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Chappell, Michael Hastings. „Developments in the use of diffusion tensor imaging data to investigate brain structure and connectivity“. Thesis, University of Canterbury. Physics and Astronomy, 2007. http://hdl.handle.net/10092/1476.

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Diffusion tensor imaging (DTI) is a specialist MRI modality that can identify microstructural changes or abnormalities in the brain. It can also be used to show fibre tract pathways. Both of these features were used in this thesis. Firstly, standard imaging analysis techniques were used to study the effects of mild, repetitive closed head injury on a group of professional boxers. Such data is extremely rare, so the findings of regions of brain abnormalities in the boxers are important, adding to the body of knowledge about more severe traumatic brain injury. The author developed a novel multivariate analysis technique which was used on the same data. This new technique proved to be more sensitive than the standard univariate methods commonly used. An important part of diagnosing and monitoring brain damage involves the use of biomarkers. A novel investigation of whether diffusion parameters obtained from DTI data could serve as bio-markers of cognitive impairment in Parkinson's disease was conducted. This also involved developing a multivariate approach, which displayed increased sensitivity compared with any of the component parameters used singly, and suggested these diffusion measures could be robust bio-markers of cognitive impairment. Fibre tract connectivity between regions of the brain is also a potentially valuable measure for diagnosis and monitoring brain integrity. The feasibility of this was investigated in a multi-modal MRI study. Functional MRI (fMRI) identifies regions of activation associated with a particular task. DTI can then find the pathway of the fibre bundles connecting these regions. The feasibility of using regional connectivity to interrogate brain integrity was investigated using a single healthy volunteer. Fibre pathways between regions activated and deactivated by a working memory paradigm were determined. Though the results are only preliminary, they suggest that this line of research should be continued.

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Bücher zum Thema "Brain microstructure imaging"

Microstructural Parcellation Of The Human Cerebral Cortex. Springer-Verlag Berlin and Heidelberg GmbH &, 2013.

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Robert, Turner, und Stefan Geyer. Microstructural Parcellation of the Human Cerebral Cortex: From Brodmann's Post-Mortem Map to in Vivo Mapping with High-Field Magnetic Resonance Imaging. Springer, 2015.

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Passaro, Antony, Foteini Christidi, Vasiliki Tsirka und Andrew C. Papanicolaou. White Matter Connectivity. Herausgegeben von Andrew C. Papanicolaou. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.5.

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The applications of diffusion tensor imaging (DTI) have increased considerably among both normal and diverse neuropsychiatric populations in recent years. In this chapter, the authors examine the contributions of DTI in identifying profiles of trait-specific connectivity in several groups defined in terms of gender, age, handedness, and general intelligence. Additionally, the DTI literature is reviewed across a range of neurodegenerative disorders including Alzheimer’s disease, mild cognitive impairment, frontotemporal dementia, Parkinson disease, multiple sclerosis, and acquired neurological disorders resulting from neuronal injury such as traumatic brain injury, aphasia, agnosia, amnesia, and apraxia. DTI metrics sensitive to psychiatric disorders encompassing obsessive-compulsive disorder, depression, bipolar disorder, schizophrenia, and alcoholism are reviewed. Future uses of DTI as a promising means of confirming diagnoses and identifying in vivo early microstructural changes of patients’ clinical symptoms are discussed.

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Buchteile zum Thema "Brain microstructure imaging"

Jallais, Maëliss, und Demian Wassermann. „Single Encoding Diffusion MRI: A Probe to Brain Anisotropy“. In Mathematics and Visualization, 171–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_8.

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AbstractThis chapter covers anisotropy in the context of probing microstructure of the human brain using single encoded diffusion MRI. We will start by illustrating how diffusion MRI is a perfectly adapted technique to measure anisotropy in the human brain using water motion, followed by a biological presentation of human brain. The non-invasive imaging technique based on water motions known as diffusion MRI will be further presented, along with the difficulties that come with it. Within this context, we will first review and discuss methods based on signal representation that enable us to get an insight into microstructure anisotropy. We will then outline methods based on modeling, which are state-of-the-art methods to get parameter estimations of the human brain tissue.

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Roebroeck, Alard. „dMRI: Diffusion Magnetic Resonance Imaging as a Window onto Structural Brain Networks and White Matter Microstructure“. In Brain Network Dysfunction in Neuropsychiatric Illness, 105–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59797-9_6.

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Tax, Chantal M. W., Elena Kleban, Muhamed Baraković, Maxime Chamberland und Derek K. Jones. „Magnetic Resonance Imaging of $$T_2$$- and Diffusion Anisotropy Using a Tiltable Receive Coil“. In Mathematics and Visualization, 247–62. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_12.

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AbstractThe anisotropic microstructure of white matter is reflected in various MRI contrasts. Transverse relaxation rates can be probed as a function of fibre-orientation with respect to the main magnetic field, while diffusion properties are probed as a function of fibre-orientation with respect to an encoding gradient. While the latter is easy to obtain by varying the orientation of the gradient, as the magnetic field is fixed, obtaining the former requires re-orienting the head. In this work we deployed a tiltable RF-coil to study $$T_2$$ T 2 - and diffusional anisotropy of the brain white matter simultaneously in diffusion-$$T_2$$ T 2 correlation experiments.

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Zhang, Gengbiao, Yingju Lu, Hongyi Zheng, Lingmei Kong und Wenbin Zheng. „Protective Effects of Resveratrol on Brain Edema and Microstructural Changes in Human Brain After Acute Alcohol Intake: Assessment by Diffusion Weighted Kurtosis Imaging“. In Biomedical and Computational Biology, 169–79. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25191-7_13.

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Nedjati-Gilani, G., und D. C. Alexander. „Tissue Microstructure Imaging with Diffusion MRI“. In Brain Mapping, 277–85. Elsevier, 2015. http://dx.doi.org/10.1016/b978-0-12-397025-1.00296-7.

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Matuschke, Felix, Kévin Ginsburger, Cyril Poupon, Katrin Amunts und Markus Axer. „Dense Fiber Modeling for 3D-Polarized Light Imaging Simulations“. In Future Trends of HPC in a Disruptive Scenario. IOS Press, 2019. http://dx.doi.org/10.3233/apc190017.

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3D-Polarized Light Imaging (3D-PLI) is a neuroimaging technique used to study the structural connectivity of the human brain at the meso- and microscale. In 3D-PLI, the complex nerve fiber architecture of the brain is characterized by 3D orientation vector fields that are derived from birefringence measurements of unstained histological brain sections by means of an effective physical model. To optimize the physical model and to better understand the underlying microstructure, numerical simulations are essential tools to optimize the used physical model and to understand the underlying microstructure in detail. The simulations rely on predefined configurations of nerve fiber models (e.g. crossing, kissing, or complex intermingling), their physical properties, as well as the physical properties of the employed optical system to model the entire 3D-PLI measurement. By comparing the simulation and experimental results, possible misinterpretations in the fiber reconstruction process of 3D-PLI can be identified. Here, we focus on fiber modeling with a specific emphasize on the generation of dense fiber distributions as found in the human brain’s white matter. A new algorithm will be introduced that allows to control possible intersections of computationally grown fiber structures.

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Tripoliti, Evanthia E., Dimitrios I. Fotiadis und Konstantia Veliou. „Diffusion Tensor Imaging and Fiber Tractography“. In Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, 229–46. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-314-2.ch015.

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Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI) modality which can significantly improve our understanding of the brain structures and neural connectivity. DTI measures are thought to be representative of brain tissue microstructure and are particularly useful for examining organized brain regions, such as white matter tract areas. DTI measures the water diffusion tensor using diffusion weighted pulse sequences which are sensitive to microscopic random water motion. The resulting diffusion weighted images (DWI) display and allow quantification of how water diffuses along axes or diffusion encoding directions. This can help to measure and quantify the tissue’s orientation and structure, making it an ideal tool for examining cerebral white matter and neural fiber tracts. In this chapter the authors discuss the theoretical aspects of DTI, the information that can be extracted from DTI data, and the use of the extracted information for the reconstruction of fiber tracts and the diagnosis of a disease. In addition, a review of known fiber tracking algorithms is presented.

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Agartz, Ingrid, und Lynn Mørch-Johnsen. „Neural Basis of Apathy“. In Apathy, 174–90. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780198841807.003.0010.

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This chapter introduces structural neuroimaging methods and presents results from brain imaging studies of the clinical apathy syndrome in neurodegenerative diseases such as Alzheimer’s disease, mild cognitive impairment, Parkinson’s disease, Huntington’s disease, and stroke, and also in schizophrenia, today considered a neurodevelopmental disease. The main method used has been magnetic resonance imaging, which also holds many innovative possibilities for future development. Scientific studies so far have pointed to structural differences in frontal, striatal, anterior cingulate, and parietal brain regions, and of white matter microstructure and connectivity changes as being involved in the apathy syndrome. No single circuit connected to apathy has so far been identified. Brain structure and function, studied at the systems network level, and integrative multimodal imaging approaches, which combine different high-resolution magnetic resonance imaging, magnetic resonance diffusion, and positron emission tomography techniques, can be helpful in resolving future questions.

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Karampinos, Dimitrios C., Robert Dawe, Konstantinos Arfanakis und John G. Georgiadis. „Optimal Diffusion Encoding Strategies for Fiber Mapping in Diffusion MRI“. In Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, 90–107. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-314-2.ch007.

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Diffusion Magnetic Resonance Imaging (diffusion MRI) can provide important information about tissue microstructure by probing the diffusion of water molecules in a biological tissue. Although originally proposed for the characterization of cerebral white matter connectivity and pathologies, its implementation has extended to many other areas of the human body. In a parallel development, a number of diffusion models have been proposed in order to extract the underlying tissue microstructural properties from the diffusion MRI signal. The present study reviews the basic considerations that have to be taken into account in the selection of the diffusion encoding parameters in diffusion MRI acquisition. Both diffusion tensor imaging (DTI) and high-order schemes are reviewed. The selection of these parameters relies strongly on requirements of the adopted diffusion model and the diffusion characteristics of the tissue under study. The authors review several successful parameter selection strategies for the imaging of the human brain, and conclude with the basics of parameter optimization on promising applications of the technique on other tissues, such as the spinal cord, the myocardium, and the skeletal muscles.

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Chung, Sohae, Els Fieremans, Joseph F. Rath und Yvonne W. Lui. „Multi-shell diffusion MR imaging and brain microstructure after mild traumatic brain injury: A focus on working memory“. In Cellular, Molecular, Physiological, and Behavioral Aspects of Traumatic Brain Injury, 393–403. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-12-823036-7.00026-8.

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Konferenzberichte zum Thema "Brain microstructure imaging"

Brusini, Lorenza, Federica Cruciani, Ilaria Boscolo Galazzo, Marco Pitteri, Silvia F. Storti, Massimiliano Calabrese, Marco Lorenzi und Gloria Menegaz. „Multivariate Data Analysis Suggests The Link Between Brain Microstructure And Cognitive Impairment In Multiple Sclerosis“. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433799.

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Zucchelli, Mauro, Samuel Deslauriers-Gauthier und Rachid Deriche. „Investigating The Effect Of Dmri Signal Representation On Fully-Connected Neural Networks Brain Tissue Microstructure Estimation“. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9434046.

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Rolland, C., J. Lebenberg, F. Leroy, E. Moulton, P. Adibpour, D. Riviere, C. Poupon et al. „Exploring Microstructure Asymmetries in the Infant Brain Cortex: A Methodological Framework Combining Structural and Diffusion Mri“. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI). IEEE, 2019. http://dx.doi.org/10.1109/isbi.2019.8759421.

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Chiang, Ming-Chang, Marina Barysheva, Katie L. McMahon, Greig I. de Zubicaray, Kori Johnson, Nicholas G. Martin, Arthur W. Toga, Margaret J. Wright und Paul M. Thompson. „Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study“. In 2011 8th IEEE International Symposium on Biomedical Imaging (ISBI 2011). IEEE, 2011. http://dx.doi.org/10.1109/isbi.2011.5872533.

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Wright, Rika M., und K. T. Ramesh. „A Finite Element Model for Estimating Axonal Damage in Traumatic Brain Injury“. In ASME 2012 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/sbc2012-80193.

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There has been an ongoing effort to reduce the occurrence of sports-related traumatic brain injury. These injuries are caused by an impact to the head and often lead to the damage of neural axons in the brain. This type of damage is classified as diffuse axonal injury (DAI) or traumatic axonal injury (TAI) [1]. One of the difficulties in studying the progression of axonal injury is that the structural signature of DAI cannot be readily visualized with conventional medical imaging modalities since the damage occurs at the cellular level [2]. This also makes the injury difficult to diagnose. Many researchers have turned to finite element (FE) models to study the development of diffuse axonal injury. FE models provide a means for observing the mechanical process of injury development from the loads to the head at the macroscale to the damage that results at the cellular level. However, for a finite element model to be a viable tool for studying DAI, the model must be able to accurately represent the behavior of the brain tissue, and it must be able to accurately predict injury. In this work, we address both of these issues in an effort to improve the material models and injury criteria used in current FE models of TBI. We represent the white matter with an anisotropic, hyper-viscoelastic constitutive model, incorporate the microstructure of the white matter through the use of diffusion tensor imaging (DTI), and estimate injury using an axonal strain injury (ASI) criterion (Figure 1). We also develop a novel method to quantify the degree of axonal damage in the fiber tracts of the brain.

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Lefebvre, Joël, Alexia Pragassam, Julien Reynaud, Frans Irgolitsch und Frédéric Lesage. „Multiorientation mapping of white matter fiber microstructures in whole mouse brains using serial optical coherence tomography“. In Neural Imaging and Sensing 2024, herausgegeben von Qingming Luo, Jun Ding und Ling Fu. SPIE, 2024. http://dx.doi.org/10.1117/12.3002959.

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Zhuang, Jiayan, Gengbiao Zhang, Jin Wang, Yuan Xu, Qilu Gao und Wenbin Zheng. „Diffusion Kurtosis Imaging Detects Microstructural Changes in the Rat Brain with Sensorineural Hearing Loss“. In 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2023. http://dx.doi.org/10.1109/cisp-bmei60920.2023.10373279.

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Lebenberg, J., C. Poupon, B. Thirion, F. Leroy, J. F. Mangin, G. Dehaene-Lambertz und J. Dubois. „Clustering the infant brain tissues based on microstructural properties and maturation assessment using multi-parametric MRI“. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015). IEEE, 2015. http://dx.doi.org/10.1109/isbi.2015.7163837.

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Wu, Jin, Tohoru Takeda, Thet Thet Lwin, Tetsuya Yuasa, Manabu Minami und Takao Akatsuka. „Biomedical application of high sensitive synchrotron X-ray imaging techniques to assess the microstructures and function of hamster heart“. In 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging. IEEE, 2007. http://dx.doi.org/10.1109/nfsi-icfbi.2007.4387686.

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