The Open Anatomy Browser (OABrowser) is an open source, web-based, zero-installation anatomy atlas viewer based on current web browser technologies and evolving anatomy atlas interoperability standards. OABrowser displays three-dimensional anatomical models, image cross-sections of labeled structures and source radiological imaging, and a text-based hierarchy of structures. The viewer includes novel collaborative tools: users can save bookmarks of atlas views for later access and exchange those bookmarks with other users, and dynamic shared views allow groups of users can participate in a collaborative interactive atlas viewing session. We have published several anatomy atlases (an MRI-derived brain atlas and atlases of other parts of the anatomy) to demonstrate OABrowser’s functionality. The atlas source data, processing tools, and the source for OABrowser are freely available through GitHub and are distributed under a liberal open source license.
The assessment of the free water fraction in the brain provides important information about extracellular processes such as atrophy and neuroinflammation in various clinical conditions as well as in normal development and aging. Free water estimates from diffusion MRI are assumed to account for freely diffusing water molecules in the extracellular space, but may be biased by other pools of molecules in rapid random motion, such as the intravoxel incoherent motion (IVIM) of blood, where water molecules perfuse in the randomly oriented capillary network. The goal of this work was to separate the signal contribution of the perfusing blood from that of free-water and of other brain diffusivities. The influence of the vascular compartment on the estimation of the free water fraction and other diffusivities was investigated by simulating perfusion in diffusion MRI data. The perfusion effect in the simulations was significant, especially for the estimation of the free water fraction, and was maintained as long as low b-value data were included in the analysis. Two approaches to reduce the perfusion effect were explored in this study: (i) increasing the minimal b-value used in the fitting, and (ii) using a three-compartment model that explicitly accounts for water molecules in the capillary blood. Estimation of the model parameters while excluding low b-values reduced the perfusion effect but was highly sensitive to noise. The three-compartment model fit was more stable and additionally, provided an estimation of the volume fraction of the capillary blood compartment. The three-compartment model thus disentangles the effects of free water diffusion and perfusion, which is of major clinical importance since changes in these components in the brain may indicate different pathologies, i.e., those originating from the extracellular space, such as neuroinflammation and atrophy, and those related to the vascular space, such as vasodilation, vasoconstriction and capillary density. Diffusion MRI data acquired from a healthy volunteer, using multiple b-shells, demonstrated an expected non-zero contribution from the blood fraction, and indicated that not accounting for the perfusion effect may explain the overestimation of the free water fraction evinced in previous studies. Finally, the applicability of the method was demonstrated with a dataset acquired using a clinically feasible protocol with shorter acquisition time and fewer b-shells.
Diffusion imaging is critical for detecting acute brain injury. However, normal apparent diffusion coefficient (ADC) maps change rapidly in early childhood, making abnormality detection difficult. In this article, we explored clinical PACS and electronic healthcare records (EHR) to create age-specific ADC atlases for clinical radiology reference. Using the EHR and three rounds of multiexpert reviews, we found ADC maps from 201 children 0-6 years of age scanned between 2006 and 2013 who had brain MRIs with no reported abnormalities and normal clinical evaluations 2+ years later. These images were grouped in 10 age bins, densely sampling the first 1 year of life (5 bins, including neonates and 4 quarters) and representing the 1-6 year age range (an age bin per year). Unbiased group-wise registration was used to construct ADC atlases for 10 age bins. We used the atlases to quantify (a) cross-sectional normative ADC variations; (b) spatiotemporal heterogeneous ADC changes; and (c) spatiotemporal heterogeneous volumetric changes. The quantified age-specific whole-brain and region-wise ADC values were compared to those from age-matched individual subjects in our study and in multiple existing independent studies. The significance of this study is that we have shown that clinically acquired images can be used to construct normative age-specific atlases. These first of their kind age-specific normative ADC atlases quantitatively characterize changes of myelination-related water diffusion in the first 6 years of life. The quantified voxel-wise spatiotemporal ADC variations provide standard references to assist radiologists toward more objective interpretation of abnormalities in clinical images. Our atlases are available at https://www.nitrc.org/projects/mgh_adcatlases.
The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as "pages of a book" has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of "path neighborhoods" reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure.
Diffusion MRI has been proposed as a non-invasive technique for axonal diameter mapping. However, accurate estimation of small diameters requires strong gradients, which is a challenge for the transition of the technique from preclinical to clinical MRI scanners, since these have weaker gradients. In this work, we develop a framework to estimate the lower bound for accurate diameter estimation, which we refer to as the resolution limit. We analyse only the contribution from the intra-axonal space and assume that axons can be represented by impermeable cylinders. To address the growing interest in using techniques for diffusion encoding that go beyond the conventional single diffusion encoding (SDE) sequence, we present a generalised analysis capable of predicting the resolution limit regardless of the gradient waveform. Using this framework, waveforms were optimised to minimise the resolution limit. The results show that, for parallel cylinders, the SDE experiment is optimal in terms of yielding the lowest possible resolution limit. In the presence of orientation dispersion, diffusion encoding sequences with square-wave oscillating gradients were optimal. The resolution limit for standard clinical MRI scanners (maximum gradient strength 60-80 mT/m) was found to be between 4 and 8 μm, depending on the noise levels and the level of orientation dispersion. For scanners with a maximum gradient strength of 300 mT/m, the limit was reduced to between 2 and 5 μm.
PURPOSE: To investigate the heating of EEG electrodes during magnetic resonance imaging (MRI) scans and to better understand the underlying physical mechanisms with a focus on the antenna effect. MATERIALS AND METHODS: Gold cup and conductive plastic electrodes were placed on small watermelons with fiberoptic probes used to measure electrode temperature changes during a variety of 1.5T and 3T MRI scans. A subset of these experiments was repeated on a healthy human volunteer. RESULTS: The differences between gold and plastic electrodes did not appear to be practically significant. For both electrode types, we observed heating below 4°C for straight wires whose lengths were multiples of ½ the radiofrequency (RF) wavelength and stronger heating (over 15°C) for wire lengths that were odd multiples of ¼ RF wavelength, consistent with the antenna effect. CONCLUSIONS: The antenna effect, which has received little attention so far in the context of EEG-MRI safety, can play as significant a role as the loop effect (from electromagnetic induction) in the heating of EEG electrodes, and therefore wire lengths that are odd multiples of ¼ RF wavelength should be avoided. These results have important implications for the design of EEG electrodes and MRI studies as they help to minimize the risk to patients undergoing MRI with EEG electrodes in place.
We perform a review of the literature in the field of white matter tractography for neurosurgical planning, focusing on those works where tractography was correlated with clinical information such as patient outcome, clinical functional testing, or electro-cortical stimulation. We organize the review by anatomical location in the brain and by surgical procedure, including both supratentorial and infratentorial pathologies, and excluding spinal cord applications. Where possible, we discuss implications of tractography for clinical care, as well as clinically relevant technical considerations regarding the tractography methods. We find that tractography is a valuable tool in variable situations in modern neurosurgery. Our survey of recent reports demonstrates multiple potentially successful applications of white matter tractography in neurosurgery, with progress towards overcoming clinical challenges of standardization and interpretation.
The neural correlates of spaceflight-induced sensorimotor impairments are unknown. Head down-tilt bed rest (HDBR) serves as a microgravity analog because it mimics the headward fluid shift and axial body unloading of spaceflight. We investigated focal brain white matter (WM) changes and fluid shifts during 70 days of 6° HDBR in 16 subjects who were assessed pre (2x), during (3x), and post-HDBR (2x). Changes over time were compared to those in control subjects (n = 12) assessed four times over 90 days. Diffusion MRI was used to assess WM microstructure and fluid shifts. Free-Water Imaging was used to quantify distribution of intracranial extracellular free water (FW). Additionally, we tested whether WM and FW changes correlated with changes in functional mobility and balance measures. HDBR resulted in FW increases in fronto-temporal regions and decreases in posterior-parietal regions that largely recovered by two weeks post-HDBR. WM microstructure was unaffected by HDBR. FW decreases in the post-central gyrus and precuneus correlated negatively with balance changes. We previously reported that gray matter increases in these regions were associated with less HDBR-induced balance impairment, suggesting adaptive structural neuroplasticity. Future studies are warranted to determine causality and underlying mechanisms.
We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space.