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:INTRODUCTION:Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies. METHODS:High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i.e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created. Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [18F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures). RESULTS:Only the SA method showed volume bias. Dice indices were significantly different between methods (PF>MP>SA). PET regional measures were more accurate with multi-atlas methods than with SA method. CONCLUSIONS:Multi-atlas methods outperform SA for automated anatomical brain segmentation and PET measure's extraction. They perform comparably to manual segmentation for FDG-PET quantification. Multi-atlas methods are suitable for rapid reproducible VOI analyses.
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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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