Quantitative-diffusion-tensor MRI consists of deriving and displaying parameters that resemble histological or physiological stains, i.e., that characterize intrinsic features of tissue ...microstructure and microdynamics. Specifically, these parameters are objective, and insensitive to the choice of laboratory coordinate system. Here, these two properties are used to derive intravoxel measures of diffusion isotropy and the degree of diffusion anisotropy, as well as intervoxel measures of structural similarity, and fiber-tract organization from the effective diffusion tensor,
D
, which is estimated in each voxel. First,
D
is decomposed into its isotropic and anisotropic parts, 〈D〉
I
and
D
– 〈D〉
I
, respectively (where 〈D〉
=
Trace(
D
)/3 is the mean diffusivity, and
I
is the identity tensor). Then, the tensor (dot) product operator is used to generate a family of new rotationally and translationally invariant quantities. Finally, maps of these quantitative parameters are produced from high-resolution diffusion tensor images (in which
D
is estimated in each voxel from a series of 2D-FT spin-echo diffusion-weighted images) in living cat brain. Due to the high inherent sensitivity of these parameters to changes in tissue architecture (i.e., macromolecular, cellular, tissue, and organ structure) and in its physiologic state, their potential applications include monitoring structural changes in development, aging, and disease.
Significance Diffusion-weighted MRI (DWI) tractography is widely used to map structural connections of the human brain in vivo and has been adopted by large-scale initiatives such as the human ...connectome project. Our results indicate that, even with high-quality data, DWI tractography alone is unlikely to provide an anatomically accurate map of the brain connectome. It is crucial to complement tractography results with a combination of histological or neurophysiological methods to map structural connectivity accurately. Our findings, however, do not diminish the importance of diffusion MRI as a noninvasive tool that offers important quantitative measures related to brain tissue microstructure and white matter architecture.
Tractography based on diffusion-weighted MRI (DWI) is widely used for mapping the structural connections of the human brain. Its accuracy is known to be limited by technical factors affecting in vivo data acquisition, such as noise, artifacts, and data undersampling resulting from scan time constraints. It generally is assumed that improvements in data quality and implementation of sophisticated tractography methods will lead to increasingly accurate maps of human anatomical connections. However, assessing the anatomical accuracy of DWI tractography is difficult because of the lack of independent knowledge of the true anatomical connections in humans. Here we investigate the future prospects of DWI-based connectional imaging by applying advanced tractography methods to an ex vivo DWI dataset of the macaque brain. The results of different tractography methods were compared with maps of known axonal projections from previous tracer studies in the macaque. Despite the exceptional quality of the DWI data, none of the methods demonstrated high anatomical accuracy. The methods that showed the highest sensitivity showed the lowest specificity, and vice versa. Additionally, anatomical accuracy was highly dependent upon parameters of the tractography algorithm, with different optimal values for mapping different pathways. These results suggest that there is an inherent limitation in determining long-range anatomical projections based on voxel-averaged estimates of local fiber orientation obtained from DWI data that is unlikely to be overcome by improvements in data acquisition and analysis alone.
Significance It is widely recognized that studying the detailed anatomy of the human brain is of great importance for neuroscience and medicine. The principal means for achieving this goal is ...presently diffusion magnetic resonance imaging (dMRI) tractography, which uses the local diffusion of water throughout the brain to estimate the course of long-range anatomical projections. Such projections connect gray matter regions through axons that travel in the deep white matter. The present study combines dMRI tractography with histological analysis to investigate where in the brain this method succeeds and fails. We conclude that certain superficial white matter systems pose challenges for measuring cortical connections that must be overcome for accurate determination of detailed neuroanatomy in humans.
In vivo tractography based on diffusion magnetic resonance imaging (dMRI) has opened new doors to study structure–function relationships in the human brain. Initially developed to map the trajectory of major white matter tracts, dMRI is used increasingly to infer long-range anatomical connections of the cortex. Because axonal projections originate and terminate in the gray matter but travel mainly through the deep white matter, the success of tractography hinges on the capacity to follow fibers across this transition. Here we demonstrate that the complex arrangement of white matter fibers residing just under the cortical sheet poses severe challenges for long-range tractography over roughly half of the brain. We investigate this issue by comparing dMRI from very-high-resolution ex vivo macaque brain specimens with histological analysis of the same tissue. Using probabilistic tracking from pure gray and white matter seeds, we found that ∼50% of the cortical surface was effectively inaccessible for long-range diffusion tracking because of dense white matter zones just beneath the infragranular layers of the cortex. Analysis of the corresponding myelin-stained sections revealed that these zones colocalized with dense and uniform sheets of axons running mostly parallel to the cortical surface, most often in sulcal regions but also in many gyral crowns. Tracer injection into the sulcal cortex demonstrated that at least some axonal fibers pass directly through these fiber systems. Current and future high-resolution dMRI studies of the human brain will need to develop methods to overcome the challenges posed by superficial white matter systems to determine long-range anatomical connections accurately.
In this work we investigate the effects of echo planar imaging (EPI) distortions on diffusion tensor imaging (DTI) based fiber tractography results. We propose a simple experimental framework that ...would enable assessing the effects of EPI distortions on the accuracy and reproducibility of fiber tractography from a pilot study on a few subjects. We compare trajectories computed from two diffusion datasets collected on each subject that are identical except for the orientation of phase encode direction, either right–left (RL) or anterior–posterior (AP). We define metrics to assess potential discrepancies between RL and AP trajectories in association, commissural, and projection pathways. Results from measurements on a 3Tesla clinical scanner indicated that the effects of EPI distortions on computed fiber trajectories are statistically significant and large in magnitude, potentially leading to erroneous inferences about brain connectivity. The correction of EPI distortion using an image-based registration approach showed a significant improvement in tract consistency and accuracy. Although obtained in the context of a DTI experiment, our findings are generally applicable to all EPI-based diffusion MRI tractography investigations, including high angular resolution (HARDI) methods. On the basis of our findings, we recommend adding an EPI distortion correction step to the diffusion MRI processing pipeline if the output is to be used for fiber tractography.
► We propose a framework to assess the effects of EPI distortions on tractography. ► We show that distortions in typical clinical 3T scans greatly affect path trajectory. ► Path trajectory artifacts lead to incorrect conclusions about brain connectivity. ► We show that simple corrections can be successfully implemented.
We propose an echo planar imaging (EPI) distortion correction method (DR-BUDDI), specialized for diffusion MRI, which uses data acquired twice with reversed phase encoding directions, often referred ...to as blip-up blip-down acquisitions. DR-BUDDI can incorporate information from an undistorted structural MRI and also use diffusion-weighted images (DWI) to guide the registration, improving the quality of the registration in the presence of large deformations and in white matter regions. DR-BUDDI does not require the transformations for correcting blip-up and blip-down images to be the exact inverse of each other. Imposing the theoretical “blip-up blip-down distortion symmetry” may not be appropriate in the presence of common clinical scanning artifacts such as motion, ghosting, Gibbs ringing, vibrations, and low signal-to-noise. The performance of DR-BUDDI is evaluated with several data sets and compared to other existing blip-up blip-down correction approaches. The proposed method is robust and generally outperforms existing approaches. The inclusion of the DWIs in the correction process proves to be important to obtain a reliable correction of distortions in the brain stem. Methods that do not use DWIs may produce a visually appealing correction of the non-diffusion weighted images, but the directionally encoded color maps computed from the tensor reveal an abnormal anatomy of the white matter pathways.
•The proposed EPI distortion correction method, DR-BUDDI, outperforms existing ones.•Blip-up blip-down symmetry principle does not always hold in practice.•Using a structural image and DWIs significantly improves correction quality.•Color and anisotropy maps should also be used to assess the quality of correction.•Artifacts from Gibbs ringing and flow voids may significantly affect correction.
This article provides a review of brain tissue alterations that may be detectable using diffusion magnetic resonance imaging MRI (dMRI) approaches and an overview and perspective on the modern dMRI ...toolkits for characterizing alterations that follow traumatic brain injury (TBI). Noninvasive imaging is a cornerstone of clinical treatment of TBI and has become increasingly used for preclinical and basic research studies. In particular, quantitative MRI methods have the potential to distinguish and evaluate the complex collection of neurobiological responses to TBI arising from pathology, neuroprotection, and recovery. dMRI provides unique information about the physical environment in tissue and can be used to probe physiological, architectural, and microstructural features. Although well‐established approaches such as diffusion tensor imaging are known to be highly sensitive to changes in the tissue environment, more advanced dMRI techniques have been developed that may offer increased specificity or new information for describing abnormalities. These tools are promising, but incompletely understood in the context of TBI. Furthermore, model dependencies and relative limitations may impact the implementation of these approaches and the interpretation of abnormalities in their metrics. The objective of this paper is to present a basic review and comparison across dMRI methods as they pertain to the detection of the most commonly observed tissue and cellular alterations following TBI.
Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, ...tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets – a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
•Organized international tractography challenge utilizing three validation datasets.•Anatomical accuracy of modern diffusion tractography techniques is limited.•Advancements are needed to overcome limited sensitivity/specificity of reconstructions.
Measures of brain morphometry derived from T1-weighted (T1W) magnetic resonance imaging (MRI) are widely used to elucidate the relation between brain structure and function. However, the computation ...of T1W morphometric measures can be confounded by subject-related factors such as head motion and level of hydration. A recent study reported subtle yet significant changes in brain volume from morning to evening in a large group of patient populations as well as in healthy elderly individuals. In addition, there is a growing recognition that factors such as circadian rhythm can impact MRI measures of brain function and structure. Here, we provide a comprehensive assessment of the impact of time-of-day (TOD) on widely used measures of brain morphometry in a group of 19 healthy young adults. Our results show that (a) even in a small group of healthy adult volunteers, a highly significant reduction in apparent brain volume, from morning to evening, could be detected; (b) the apparent volume of all three major tissue compartments – gray matter, white matter, and cerebrospinal fluid – were influenced by TOD, and the magnitude of the TOD effect varied across the tissue compartments; (c) measures of cortical thickness, cortical surface area, and gray matter density computed with widely used neuroimaging software suites (i.e., FreeSurfer, FSL-VBM) were all affected by TOD, while other measures, such as curvature indices and sulcal depth, were not; and (d) the effect of TOD appeared to have a greater impact on morphometric measures of the frontal and temporal lobe than on other major lobes of the brain. Our results suggest that the TOD effect is a physiological phenomenon and that controlling for the effect of TOD is crucial for proper interpretation of apparent structural differences measured with T1W morphometry.
•Time of day (TOD) impacts automatically derived measures of brain morphometry derived from T1W images.•The apparent volume of all major tissue compartments are influenced by TOD.•Measures of cortical thickness, cortical surface area and gray matter density are affected by TOD.•TOD has a greater impact on morphometric measures of the frontal and temporal lobe.
Diffusion-weighted magnetic resonance (MR) signals reflect information about underlying tissue microstructure and cytoarchitecture. We propose a quantitative, efficient, and robust mathematical and ...physical framework for representing diffusion-weighted MR imaging (MRI) data obtained in “q-space,” and the corresponding “mean apparent propagator (MAP)” describing molecular displacements in “r-space.” We also define and map novel quantitative descriptors of diffusion that can be computed robustly using this MAP-MRI framework.
We describe efficient analytical representation of the three-dimensional q-space MR signal in a series expansion of basis functions that accurately describes diffusion in many complex geometries. The lowest order term in this expansion contains a diffusion tensor that characterizes the Gaussian displacement distribution, equivalent to diffusion tensor MRI (DTI). Inclusion of higher order terms enables the reconstruction of the true average propagator whose projection onto the unit “displacement” sphere provides an orientational distribution function (ODF) that contains only the orientational dependence of the diffusion process. The representation characterizes novel features of diffusion anisotropy and the non-Gaussian character of the three-dimensional diffusion process. Other important measures this representation provides include the return-to-the-origin probability (RTOP), and its variants for diffusion in one- and two-dimensions—the return-to-the-plane probability (RTPP), and the return-to-the-axis probability (RTAP), respectively. These zero net displacement probabilities measure the mean compartment (pore) volume and cross-sectional area in distributions of isolated pores irrespective of the pore shape.
MAP-MRI represents a new comprehensive framework to model the three-dimensional q-space signal and transform it into diffusion propagators. Experiments on an excised marmoset brain specimen demonstrate that MAP-MRI provides several novel, quantifiable parameters that capture previously obscured intrinsic features of nervous tissue microstructure. This should prove helpful for investigating the functional organization of normal and pathologic nervous tissue.
•MAP-MRI is a comprehensive method for modeling 3D (multi-shell) q-space signal.•MAP-MRI subsumes and generalizes diffusion tensor imaging (DTI).•Several novel parameters capture previously obscured microstructural features.•MAP reconstruction is performed on an excised marmoset brain dataset.•MAP-MRI is robust, efficient, and may be adapted to in vivo and clinical imaging.
Purpose
To propose a methodology for assessment of algorithms that correct distortions due to motion, eddy‐currents, and echo planar imaging in diffusion weighted images (DWIs).
Methods
The proposed ...method evaluates correction performance by measuring variability across datasets of the same object acquired with images having distortions in different directions, thereby overcoming the unavailability of ground‐truth, undistorted DWIs. A comprehensive diffusion MRI dataset, collected using a suitable experimental design, is made available to the scientific community, consisting of three DWI shells (Bmax = 5000 s/mm2), 30 gradient directions, a replicate set of antipodal gradient directions, four phase‐encoding directions, and three different head orientations. The proposed methodology was tested using the TORTOISE diffusion MRI processing pipeline.
Results
The median variability of the original distorted data was 123% higher for DWIs, 100–168% higher for tensor‐derived metrics and 28–111% higher for MAPMRI metrics, than in the corrected versions. EPI distortions induced substantial variability, nearly comparable to the contribution of eddy‐current distortions.
Conclusions
The dataset and the evaluation strategy proposed herein enable quantitative comparison of different methods for correction of distortions due to motion, eddy‐currents, and other EPI distortions, and can be useful in benchmarking newly developed algorithms.
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