Deterministic Tractography Analysis of Rat Brain Using SIGMA Atlas in 9.4T MRI.
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ABSTRACT:
SUBMITTER: Im SJ
PROVIDER: S-EPMC8699268 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PURPOSE:We propose a quantitative technique to assess solute uptake into the brain parenchyma based on dynamic contrast-enhanced MRI (DCE-MRI). With this approach, a small molecular weight paramagnetic contrast agent (Gd-DOTA) is infused in the cerebral spinal fluid (CSF) and whole brain gadolinium concentration maps are derived. METHODS:We implemented a 3D variable flip angle spoiled gradient echo (VFA-SPGR) longitudinal relaxation time (T1) technique, the accuracy of which was cross-validated by way of inversion recovery rapid acquisition with relaxation enhancement (IR-RARE) using phantoms. Normal Wistar rats underwent Gd-DOTA infusion into CSF via the cisterna magna and continuous MRI for approximately 130?min using T1-weighted imaging. Dynamic Gd-DOTA concentration maps were calculated and parenchymal uptake was estimated. RESULTS:In the phantom study, T1 discrepancies between the VFA-SPGR and IR-RARE sequences were approximately 6% with a transmit coil inhomogeneity correction. In the in vivo study, contrast transport profiles indicated maximal parenchymal retention of approximately 19% relative to the total amount delivered into the cisterna magna. CONCLUSION:Imaging strategies for accurate 3D contrast concentration mapping at 9.4T were developed and whole brain dynamic concentration maps were derived to study solute transport via the glymphatic system. The newly developed approach will enable future quantitative studies of the glymphatic system in health and disease states. Magn Reson Med 79:1568-1578, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Project description:Diffusion Tensor Imaging (DTI) and fiber tractography are important tools to map the cerebral white matter microstructure in vivo and to model the underlying axonal pathways in the brain with three-dimensional fiber tracts. As the fast and consistent extraction of anatomically correct fiber bundles for multiple datasets is still challenging, we present a novel atlas-guided clustering framework for exploratory data analysis of large tractography datasets. The framework uses an hierarchical cluster analysis approach that exploits the inherent redundancy in large datasets to time-efficiently group fiber tracts. Structural information of a white matter atlas can be incorporated into the clustering to achieve an anatomically correct and reproducible grouping of fiber tracts. This approach facilitates not only the identification of the bundles corresponding to the classes of the atlas; it also enables the extraction of bundles that are not present in the atlas. The new technique was applied to cluster datasets of 46 healthy subjects. Prospects of automatic and anatomically correct as well as reproducible clustering are explored. Reconstructed clusters were well separated and showed good correspondence to anatomical bundles. Using the atlas-guided cluster approach, we observed consistent results across subjects with high reproducibility. In order to investigate the outlier elimination performance of the clustering algorithm, scenarios with varying amounts of noise were simulated and clustered with three different outlier elimination strategies. By exploiting the multithreading capabilities of modern multiprocessor systems in combination with novel algorithms, our toolkit clusters large datasets in a couple of minutes. Experiments were conducted to investigate the achievable speedup and to demonstrate the high performance of the clustering framework in a multiprocessing environment.
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Project description:Peripheral nerve injuries result in severe loss of sensory and motor functions in the afflicted limb. There is a lack of standardised models to non-invasively study degeneration, regeneration, and normalisation of neuronal microstructure in peripheral nerves. This study aimed to develop a non-invasive evaluation of peripheral nerve injuries, using diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and tractography on a rat model of sciatic nerve injury. 10 female Sprague Dawley rats were exposed to sciatic nerve neurotmesis and studied using a 9.4?T magnet, by performing DTI and DKI of the sciatic nerve before and 4 weeks after injury. The distal nerve stump showed a decrease in fractional anisotropy (FA), mean kurtosis (MK), axonal water fraction (AWF), and radial and axonal kurtosis (RK, AK) after injury. The proximal stump showed a significant decrease in axial diffusivity (AD) and increase of MK and AK as compared with the uninjured nerve. Both mean diffusivity (MD) and radial diffusivity (RD) increased in the distal stump after injury. Tractography visualised the sciatic nerve and the site of injury, as well as local variations of the diffusion parameters following injury. In summary, the described method detects changes both proximal and distal to the nerve injury.
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Project description:Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.
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Project description:PurposeDevelopment of a freely available stroke population-specific anatomical CT/MRI atlas with a reliable normalisation pipeline for clinical CT.MethodsBy reviewing CT scans in suspected stroke patients and filtering the AIBL MRI database, respectively, we collected 50 normal-for-age CT and MRI scans to build a standard-resolution CT template and a high-resolution MRI template. The latter was manually segmented into anatomical brain regions. We then developed and validated a MRI to CT registration pipeline to align the MRI atlas onto the CT template. Finally, we developed a CT-to-CT-normalisation pipeline and tested its reliability by calculating Dice coefficient (Dice) and Average Hausdorff Distance (AHD) for predefined areas in 100 CT scans from ischaemic stroke patients.ResultsThe resulting CT/MRI templates were age and sex matched to a general stroke population (median age 71.9 years (62.1-80.2), 60% male). Specifically, this accounts for relevant structural changes related to aging, which may affect registration. Applying the validated MRI to CT alignment (Dice > 0.78, Average Hausdorff Distance < 0.59 mm) resulted in our final CT-MRI atlas. The atlas has 52 manually segmented regions and covers the whole brain. The alignment of four cortical and subcortical brain regions with our CT-normalisation pipeline was reliable for small/medium/large infarct lesions (Dice coefficient > 0.5).ConclusionThe newly created CT-MRI brain atlas has the potential to standardise stroke lesion segmentation. Together with the automated normalisation pipeline, it allows analysis of existing and new datasets to improve prediction tools for stroke patients (free download at https://forms.office.com/r/v4t3sWfbKs ).
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Project description:Integration of the neural processes in the human brain is realized through interconnections that exist between different neural centers. These interconnections take place through white matter pathways. White matter tractography is currently the only available technique for the reconstruction of the anatomical connectivity in the human brain noninvasively and in vivo. The trajectory and terminations of white matter pathways are estimated from local orientations of nerve bundles. These orientations are obtained using measurements of water diffusion in the brain. In this article, the techniques for estimating fiber directions from diffusion measurements in the human brain are reviewed. Methods of white matter tractography are described, together with the current limitations of the technique, including sensitivity to image noise and partial voluming. The applications of white matter tractography to the topographical characterization of the white matter connections and the segmentation of specific white matter pathways, and corresponding functional units of gray matter, are discussed. In this context, the potential impact of white matter tractography in mapping the functional systems and subsystems in the human brain, and their interrelations, is described. Finally, the applications of white matter tractography to the study of brain disorders, including fiber tract localization in brains affected by tumors and the identification of impaired connectivity routes in neurologic and neuropsychiatric diseases, are discussed.
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