Microstructure Imaging Core

Carl-Fredrik Westin
Carl-Fredrik Westin, PhD
Core PI

Our Publications

The goal of the Microstructure Imaging Core is to pursue technological developments that improve current understanding of white matter anatomy and pathology in the brain. Given the small dimension of the neural pathways in relation to current imaging resolution, these technologies are vital to analyzing and visualizing the neural anatomy in normal and diseased states. Our current clinical objective is to analyze abnormalities in white matter architecture that underlie schizophrenia by using techniques in diffusion magnetic resonance imaging (dMRI) and post-process imaging. The expectation is that new knowledge acquired from these studies not only will benefit the understanding and treatment of schizophrenia, but also a broad range of other white matter diseases. The work of this core is organized around the following themes:

  1. Development of Microstructural Imaging Biomarkers
  2. Development of Personalized Anatomical Analysis Methods
  3. Population Statistics and Patient-Specific Diagnosis

The objective of this research is to develop and implement quantitative measures from diffusion MRI (dMRI). Methods to be explored include multi-shell dMRI to determine free vs. multi-compartmental or restricted diffusion; compressed sensing (CS) in conjunction with MRS to allow detection of microstructural metabolic change within the tissue; and double-pulsed field gradient dMRI (double-PFG) to provide previously unavailable diffusion properties that can be mapped into families of new types of geometric and tissue-specific parameters. Software for measuring the proposed biomarkers will be developed and tested.

Featured Technologies

Standard dMRI (left) allows measurement of the angular distribution of the diffusion function; whereas the radial part of this function measured with multi-shell dMRI (right) reflects the type of diffusion observed (free vs. multicompartmental, or restricted).





Traditional diffusion MRI (single-PFG) would only provide the first pair of gradients, G1,G1. Increased microstructural detail (G2, G2) can be measured with the double-PFG diffusion sequence.


Diffusion MRI in 3D Slicer

Image legend: Diffusion MRI data for neurosurgical planning. The tractography region of interest (ROI) is depicted by the box placed around the tumor (in green) in the frontal lobe. The ROI is also visualized with rectangles in the slice views below. Tracts are then created based on the principal diffusion directions, which are color coded (bottom). Diffusion ellipsoids are shown along the tract to visualize the shape of the local diffusion.

The 3D Slicer (or simply Slicer) software was initially developed as a joint effort by the Surgical Planning Laboratory of Brigham and Women's Hospital and the Medical Vision Group of the MIT-based Computer Science and Artificial Intelligence Laboratory (CSAIL). The goal was to create an interactive, open source software platform for biomedical research. The program has evolved into a national platform supported by a variety of federal funding sources. This versatile research environment has resulted in a wide array of functionality and supports a variety of medical imaging projects.

Slicer is a "point and click" end-user application. It is used as a vehicle for delivering algorithms to computer scientists, biomedical researchers, and clinical investigators. Slicer is distributed under an open source license without a reciprocity requirement and without restrictions on use. For a sampling of the portfolio of applications, please see the Slicer Community page. 3D Slicer consists of over a million lines of code, mostly C++. This massive software development effort was enabled by the participation of several large-scale NIH-funded efforts, including the National Alliance for Medical Image Computing (NA-MIC), Neuroimage Analysis Center (NAC), Biomedical Informatics Research Network (BIRN), Center for Integration of Medicine and Innovative Technology (CIMIT), and National Center for Image Guided Therapy (NCIGT) communities. Federal funding sources include the National Center for Research Resources (NCRR), National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH Roadmap, National Cancer Institute (NCI), National Science Foundation (NSF), and the Department of Defense (DOD) among others.

DTMRI is a module in the 3D Slicer collection used for interactive visualization of diffusion tensor MRI (DT-MRI, or DTI). A cross-platform end user application used for analyzing and visualizing medical images, Slicer consists of a collection of Open Source libraries for developing and deploying new image computing technologies. It is an algorithm development platform with a powerful new Execution Model to facilitate creation of new modules. The White Matter Architecture Core has made major contributions in the diffusion MRI analysis and visualization packages in Slicer2, Slicer3, and Slicer4 (www.slicer.org).


Estimation Procedures for High Angular Resolution Diffusion MRI

The White Matter Architure Core has developed novel estimation procedures for High Angular Resolution Diffusion MRI (HARDI). Our novel method “Orientation Probability Density Transform” (OPDT) of estimating the diffusion function has received widespread attention [Tristan-Vega, 2009], [Tristan-Vega, 2010]. Compared to the popular approaches of Q-Ball imaging and the diffusion orientation transform (DOT), our formulation introduces considerably less blurring of the angular function as shown in the figure.

In our work, instead of radial projections, true angular marginalizations were computed. We showed that the Jacobian of the spherical coordinates (in this case r2) needs to be included, a crucial contribution as the transform has been calculated without the correct term since 2003. Our formulation, the OPDT, is advantageous as it has a strict probabilistic interpretation, and the correct expression introduces considerably less blurring of the angular function in comparison to other methods such as Q-Ball and DOT.

The figure shows diffusion profiles for crossing fibers with different angles reconstructed using the Q-Ball imaging (top), the diffusion orientation transform (middle), and our formulation (OPDT). Note that the OPTD method introduces considerably less blurring of the angular function, making it easier to detect the peaks of the function corresponding to the two underlying diffusion directions.
Image legend: Diffusion profiles for crossing fibers with different angles reconstructed using the Q-Ball imaging (top), the diffusion orientation transform (middle), and our formulation (OPDT).

Multiple Fiber Model Tractography

Image legend: Multiple fiber model tractography using unscented Kalman filtering. Note the additional tracts (blue) the new method finds.

The White Matter Architecture Core has developed tractography methods that can trace complex white matter regions, such as regions where there is heavy crossing and branching of the fiber tracts [Malcolm, 2009]. In this work we propose a technique to simultaneously estimate the local fiber orientations and perform multi-fiber tractography. Existing techniques estimate the local fiber orientation at each voxel independently. Consequently, there is no running knowledge of confidence in the measured signal or estimated fiber orientation. Further, to overcome noise, many algorithms use a filter as a post-processing step to obtain a smooth trajectory. Fiber tracking is formulated as a causal estimation: at each step of tracing the fiber, the current estimate of the signal is guided by the previous. The signal is modeled as a discrete mixture of Watson directional functions, and the tractography is performed within a filtering framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the signal and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides an accurate estimate of the local structure at each point along the fiber. The Watson function was chosen since it provides a compact representation of the signal parameterized by the principal diffusion direction and a scaling parameter describing anisotropy. It also allows analytic reconstruction of the oriented diffusion function from those parameters. Using a mixture of two and three components (corresponding to two-fiber and three-fiber models) we demonstrate in synthetic experiments that this approach reduces signal reconstruction error and significantly improves the angular resolution at crossings and branchings. In vivo experiments shown in the figure examine the corpus callosum and internal capsule and confirm the ability to trace through regions known to contain such crossing and branching while providing inherent path regularization.

Tract-Based Diffusion Feature Analysis

Image legend: SLICER interface for the EM fiber clustering module.

The White Matter Architecture Core has also worked on developing an automatic method that we call tract-based morphometry, or TBM, for measurement and analysis of diffusion MRI data along white matter fiber tracts [O'Donnell, 2009]. Using subject-specific tractography bundle segmentations, the method generates an arc length parameterization of the bundle with point correspondences across all fibers and all subjects, allowing tract-based measurement and analysis. In this work the team presents a quantitative comparison of fiber coordinate systems from the literature and we introduce an improved optimal match method that reduces spatial distortion and improves intra- and inter-subject variability of FA measurements.

The team proposed a method for generating arc length correspondences across hemispheres, enabling a TBM study of interhemispheric diffusion asymmetries in the arcuate fasciculus (AF) and cingulum bundle (CB). The results of this study demonstrate that TBM can detect differences that may not be found by measuring means of scalar invariants in entire tracts, such as the mean diffusivity (MD) differences found in AF. TBM results are reported of higher fractional anisotropy (FA) in the left hemisphere in AF (caused primarily by lower λ3, the smallest eigenvalue of the diffusion tensor, in the left AF), and higher left hemisphere FA in CB (related to higher λ1, the largest eigenvalue of the diffusion tensor, in the left CB). By mapping the significance levels onto the tractography trajectories for each structure, the anatomical locations of the interhemispheric differences are shown. The TBM approach brings analysis of DTI data into the clinically and neuroanatomically relevant framework of the tract anatomy. The team has also developed tools for tract-based analysis in 3D Slicer. As shown in the figure, the new command line module for EM clustering developed in collaboration with GE Research (Jim Miller) makes anatomical grouping and quantitative analysis of diffusion measures available to the research community. The team has also created an on-line tutorial for this module.

Asymmetry of White Matter Tract

Image legend: Our white matter clustering method defines homologous anatomy across subjects (left). It has been validated by comparison to interactive tract selection (middle). The clustering method has been used in several studies of schizophrenia (SZ), e.g., to define callosal subdivisions, detecting reduced FA in the anterior corpus callosum in SZ (right).

The team has proposed a new method for studying the asymmetry of white matter tracts in the entire brain. This approach has been applied to a preliminary study of normal subjects across the handedness spectrum [O’Donnell, 2010]. The quantification of brain asymmetries may provide biomarkers for presurgical localization of language function and can improve our understanding of neural structure-function relationships in health and disease. Methods for quantifying white matter asymmetry using diffusion MRI tractography have thus far been based on comparing numbers of fibers or volumes of a single fiber tract across hemispheres. The team further proposes a generalization of such methods, where the “number of fibers” laterality measurement is extended to the entire brain using a soft fiber comparison metric. The distribution of fiber laterality indices over the whole brain is summarized in a histogram, and defines properties of the distribution using its skewness, median, and inter-quartile range. The whole-brain fiber laterality histogram can be measured in an exploratory fashion without hypothesizing asymmetries only in particular structures. The team has demonstrated an overall difference in white matter asymmetry in consistent- and inconsistent-handers: the skewness of the fiber laterality histogram is significantly different across handedness groups. The figure shows fiber laterality indices and fiber laterality histograms in example subjects from each handedness group.

Research Highlights

Figure 1: Q-space trajectory imaging for multidimensional diffusion MRI of the human brain. This visualization of four proposed QTI measures demonstrates how the measures would change in eight illustrative synthetic macrodomains (voxels). Note that these measures intuitively separate size, shape, and orientation coherence, as well as provide the traditional measures of macroscopic anisotropy. The microstructure measures proposed from QTI can disambiguate complex microenvironments that are indistinguishable using today’s standard single diffusion encoding (SDE) methods. For example, the macroscopic anisotropy (FA) of all diffusion tensor distributions (DTDs) found in the bottom row is very low, while the microscopic anisotropy (μFA) reflects the anisotropy of the individual micro-environments [Westin, 2016].


Figure 2: Constrained optimization of gradient waveforms for generalized diffusion encoding. The team provided a solution to the problem of constrained optimization of gradient waveforms imposed by clinical hardware, while maximizing the diffusion encoding strength afforded by QTI, by solving a constrained optimization problem that accommodates constraints on maximum gradient amplitude, slew rate, coil heating and positioning of radio frequency pulses. The method's efficacy and flexibility is demonstrated both experimentally and by comparison with previous work on optimization of isotropic diffusion sequences. In this figure, raw diffusion weighted images are shown on an axial slice through the corpus callosum in a healthy volunteer. The encoding strength is b=2000 s/mm2 in both images. The measured signal is markedly higher in the images encoded with the optimized waveform (A, echo time 116 ms) compared to the qMAS waveform (B, echo time 170 ms). The histogram shows the distribution of voxelwise SNR from brain tissue located within the imaging slab. There is a clear tendency towards higher SNR for the optimized waveform, due to the shorter echo time [Sjölund, 2015].


Figure 3A: Sampling strategies and compressed sensing for high spatial resolution dMRI. T1 weighted MRI image with a yellow inset showing the corresponding inset of the colored-by-orientation DTI images. The low resolution images have 1.83 mm3 isotropic voxels, while the interpolated DTI and the super resolution data set have 0.93 mm3 isotropic voxels. The yellow arrows point out the fine details in the super resolution results that are missing in the low resolution and the interpolated results.

Figure 3B: Sampling strategies and compressed sensing for high spatial resolution dMRI. B. Left: T1 weighted MRI images with a yellow inset indicating the anatomical area of the DTI images with color-coded tensor glyphs visualizing the local white matter fiber orientation. Middle: the low resolution data set (with 1.83 mm3 isotropic voxels). Right: The result of the proposed compressed sensing super resolution method (with 0.93 mm3 isotropic voxels). The rectangle shows the glyphs in a gray-matter region that were missing in the low resolution results [Radmanesh, 2015].

CF5Figure 4: White matter segmentation using clustering for traumatic brain injury. Results from clustering the result of whole brain white matter tractography. Left: 650 color-coded clusters with a distinct color for each segmented tract segment. Right: Example of one selected cluster with the corresponding tract from the control group and the player group. By O'Donnell et al. (preliminary unpublished findings).]

Figure 5: Using abnormal white matter connections between medial frontal regions to predict symptoms in patients with first episode schizophrenia. Scatter plots of mean fractional anisotropy for first episode schizophrenia and the healthy control group. Abbreviations: OFC = orbitofrontal cortex; ACC = anterior cingulate cortex; SZ = schizophrenia group; HC = healthy control group. *p < .001. ]


Figure 6: New microscopic measures derived from QTI applied to schizophrenia population. Comparison of normalized measures in schizophrenia patients (SZ) and healthy controls (CTR). The CMD, CM and Cμ averaged across the white matter were all significantly reduced in the schizophrenia group. Changes in Cc were not found to be significant between the groups. Significance was tested using the Wilcoxon rank-sum U-test. ]


Dr. Westin’s team reported progress in four projects designed to further the goals of aim 1.

Q-space trajectory imaging for multidimensional diffusion MRI of the human brain (FIGURE 1). The team has developed a framework called q-space trajectory imaging (QTI), which uses gradient waveforms to probe trajectories in q-space. In contrast to single diffusion encoding (SDE) sequences that probe a single point in q-space, a single QTI measurement encodes multiple diffusion directions (Figure 1). The team has shown that measuring a family of trajectories introduces higher-order (multi-dimensional) correlations, which permit separate quantification of microstructural properties that are intrinsically entangled in traditional SDE. Within this framework the microstructural properties, mathematically expressed in terms of variability in size, shape, and orientation, are extracted from a diffusion tensor distribution model representing a mixture of distinct neuronal tissue micro-environments, such as neurites, cellular domains, and extracellular spaces [Westin, 2016].

Characterizing magnetic resonance signal decay using the path integral approach. The influence of Gaussian diffusion on the magnetic resonance signal is determined by the apparent diffusion coefficient (ADC) and tensor of the diffusing fluid as well as the gradient waveform applied to sensitize the signal to diffusion. Estimations of the ADC and the diffusion tensor from diffusion-weighted acquisitions necessitate computations of, respectively, the b-value and b-matrix associated with the employed pulse sequence. The team has established the relationship between these quantities and the gradient waveform by expressing the problem as a path integral, which is then explicitly evaluated. Further, the team has shown that these important quantities can be conveniently computed for any gradient waveform using a simple algorithm that requires a few lines of code. With this representation, the new technique complements the multiple correlation function method commonly used to compute the effects of restricted diffusion. It also provides a consistent and convenient framework for studies that aim to infer the microstructural features. To understand the meaning of MR signals, it is important, theoretically, to relate the applied waveform to an expected MR signal for a given structure or geometry. This has been studied extensively for single diffusion encoding (SDE). During the past year the team began this analysis for QTI by presenting the initial steps towards a general analytical framework with which one can derive explicit relationships for MR signal decay for a general time-dependent gradient waveform. Unlike earlier derivations, this one is based on path integrals and is more intuitive as it is based solely on probabilistic notions rather than the Bloch-Torrey equation. The team plans to extend this framework to develop efficient numerical methods for Gaussian and non-Gaussian diffusion models.

Constrained optimization of gradient waveforms for generalized diffusion encoding (FIGURE 2). The conventional diffusion encoding sequence, i.e., the single pulsed field gradient (PFG), has recently been challenged as more general gradient waveforms have been introduced, such as q-space trajectory imaging (QTI), which generalizes the scalar b-value to a tensor valued entity. To design gradient waveforms with specific features while taking full advantage of clinical scanner capabilities, it is imperative to respect the constraints imposed by the hardware while maximizing the diffusion encoding strength. The team has provided a tool that achieves this goal by solving a constrained optimization problem to accommodate constraints on maximum gradient amplitude, slew rate, coil heating, and positioning of radiofrequency pulses. The efficacy and flexibility of this method is demonstrated both experimentally and by comparison with previous work on optimization of isotropic diffusion sequences. Q-space trajectory imaging enables diffusion encoding with a general measurement tensor B. Although the “b-matrix” concept is well established and can be found in standard textbooks on diffusion NMR and MRI, the characterization of the b-matrix using double-PFG and more general gradient waveform diffusion MRI is novel and different. The team showed that it is possible to perform diffusion encoding imaging of the human brain (FIGURE 2) with arbitrary q-space trajectories while maintaining good SNR to generalize the concept that b-values can enable new types of measurements not available with single-PFG.

Sampling strategies and compressed sensing for high spatial resolution dMRI (FIGURES 3A, 3B). Compressed sensing (CS) is used in signal processing to reconstruct a signal from very few measurements or samples. CS has received significant attention in the MRI community for its ability to reconstruct an image with fewer data points, thereby reducing scan time. During the past year the team developed a novel sampling and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution images that effectively reduces acquisition time while improving signal-to-noise (SNR) ratio. The proposed method, called compressed-sensing super-resolution reconstruction, uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signals with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) using spherical ridgelets with total-variation regularization to account for signal correlation in neighboring voxels. The performance of the proposed method is quantitatively evaluated on several in vivo human data sets. The experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super-resolution dMRI data with very good data fidelity in a clinically feasible acquisition time. 


Dr. Westin’s team further refined their white matter tractography clustering method to permit automated measurement of white matter regions in subjects with traumatic brain injury.

White matter segmentation using clustering for traumatic brain injury (FIGURE 4). The team continued to develop a white matter atlas creation method for the purpose of studying subjects with traumatic brain injury (TBI). The method learns a model of the common white matter structures present in a group of subjects. Based on group spectral clustering of tractography, the method discovers structures corresponding to expected white matter anatomy such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, and corona radiata. The white matter clusters are augmented with expert anatomical labels and stored in a new type of atlas called a high-dimensional white matter atlas. The automatic tractography segmentation identifies corresponding white matter regions across hemispheres and across subjects, thus enabling group comparison of white matter anatomy. This preliminary study analyzed the white matter structures of 63 retired National Football League (NFL) players vs 26 healthy age-matched (ages 45-69) controls. The clustering method enabled automated measurement of 650 white matter regions in each subject, with 7% of the clusters showing lower fractional anisotropy (FA) in the player group. 


Dr. Westin’s team developed new QTI analysis methods and applied them to population studies in schizophrenia.

Application of analysis methods to population studies in schizophrenia (FIGURE 5). The team studied abnormal white matter connections between medial frontal regions to determine their value in predicting symptoms in patients with first episode schizophrenia. Clinically, a majority of patients with early stages of schizophrenia, a population frequently referred to as first episode schizophrenia (FESZ), demonstrate positive symptoms such as hallucinations and delusions. Negative symptoms also exist in the early stage of the illness, although they are observed less frequently in FESZ than in chronic schizophrenia populations. It has been suggested that the medial orbitofrontal cortex (mOFC) and rostral part of anterior cingulate cortex (rACC) are involved in the neural network of salience and emotional processing and associated with specific clinical symptoms of schizophrenia. Considering the schizophrenia dysconnectivity hypothesis, the connectivity abnormalities between mOFC and rACC might be associated with clinical characteristics in first episode schizophrenia patients (Figure 5). Diffusion tensor imaging (DTI) is a promising method for characterizing microstructural changes or differences with neuropathology and treatment. Diffusion MRI (dMRI) encodes information on translational displacements of water on the micrometer scale. Thus, the dMRI signal is an excellent probe for microstructural geometries in tissue such as the human brain. In dMRI, each millimeter-scale measurement contains an aggregate of information from a multitude of microscopic environments (microenvironments). The diffusion tensor characterizes the magnitude, anisotropy, and orientation of the diffusion tensor to reveal the relationships between DTI measures and white matter pathologic features (ischemia, myelination, axonal damage, inflammation, and edema). FA is highly sensitive to microstrutural changes, but not very specific as to the orientation of the change. The main findings of this study include a reduction of FA and significant increases in radial diffusivity (RD) and Trace (magnitude) without significant changes in axial diffusivity (AD) in white matter fibers connecting the mOFC and the rACC, suggesting that dismyelination might affect normal connectivity in this region in patients with FESZ. In addition, FA reduction in the fibers connecting the posterior mOFC and rACC was related to the severity of delusions and emotion-related negative symptoms in FESZ, implying an association between posterior mOFC- rACC disconnectivity and the psychopathology of early stage of schizophrenia.

The team also derived new microscopic measures based on q-trajectory imaging (QTI) and applied them to a schizophrenia population (FIGURE 6). Parameter maps derived from QTI were compared between patients with chronic schizophrenia and healthy controls. Nine of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for studying complex tissue architecture. Following a new approach, the team separated the bulk and shear variances into measures that are more intuitively meaningful, with the goal of separating size, shape, and orientation coherence. The measures were normalized and ranged between 0 and 1. Normalizing the bulk variance (VMD) yields a natural size variation parameter, CMD. Normalizing the shear variance is more challenging because it is affected by both the shape of the microenvironment and its orientation coherence (or dispersion). Using this approach, the team derived a measure of shape, Cμ, related to μFA and a separate measure of microscopic orientation coherence, Cc, related to previously presented order parameters (Figure 6). As expected, the schizophrenia patients showed elevated mean diffusivity (MD) in the white matter. Interestingly, this increase was matched by an increase in VMD. The increase cannot be explained by a homogeneous increase in the local mean diffusivity, rather it can be explained by an increase in the fraction of free water. This finding suggests that increased extracellular water, e.g., due to chronic neuroinflammatory processes or atrophy, is the primary mechanism underlying white matter diffusion changes in this cohort of patients with chronic schizophrenia. However, while reduced Cμ was also observed, Cc in the schizophrenia patients was unaltered, which could indicate axonal degradation at a microscopic level. Further studies with larger sample sizes are needed to confirm these findings. The current results, obtained by averaging throughout the entire white matter, do not lend themselves to the precise interpretation of the newly proposed measures in comparison with more traditional SDE measures. It may be more meaningful to study localized changes in both the white matter and the gray matter, and then to assess these results with analogy to simple prototype distributions.


Fan Zhang, Thomas Noh, Parikshit Juvekar, Sarah F Frisken, Laura Rigolo, Isaiah Norton, Tina Kapur, Sonia Pujol, William Wells, Alex Yarmarkovich, Gordon Kindlmann, Demian Wassermann, Raul San Jose Estepar, Yogesh Rathi, Ron Kikinis, Hans J Johnson, Carl-Fredrik Westin, Steve Pieper, Alexandra J Golby, and Lauren J O'Donnell. 3/2020. “SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization.” JCO Clin Cancer Inform, 4, Pp. 299-309.Abstract
PURPOSE: We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS: In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS: We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION: SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.
Lipeng Ning, Borjan Gagoski, Filip Szczepankiewicz, Carl-Fredrik Westin, and Yogesh Rathi. 3/2020. “Joint RElaxation-Diffusion Imaging Moments to Probe Neurite Microstructure.” IEEE Trans Med Imaging, 39, 3, Pp. 668-677.Abstract
Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting the Laplace transform to recover the joint distribution of relaxation-diffusion. However, as is well-known, this problem is notoriously ill-posed and numerically unstable. In this work, we address this issue by directly computing the joint moments of transverse relaxation rate and diffusivity, which can be robustly estimated. To zoom into different parts of the joint distribution, we further enhance our method by applying multiplicative filters to the joint probability density function of relaxation and diffusion and compute the corresponding moments. We propose an approach to use these moments to compute several novel scalar indices to characterize specific properties of the underlying tissue microstructure. Furthermore, for the first time, we propose an algorithm to estimate diffusion signals that are independent of echo time based on the moments of the marginal probability density function of diffusion. We demonstrate its utility in extracting tissue information not contaminated with multiple intra-voxel relaxation rates. We compare the performance of four types of filters that zoom into tissue components with different relaxation and diffusion properties and demonstrate it on an in-vivo human dataset. Experimental results show that these filters are able to characterize heterogeneous tissue microstructure. Moreover, the filtered diffusion signals are also able to distinguish fiber bundles with similar orientations but different relaxation rates. The proposed method thus allows to characterize the neural microstructure information in a robust and unique manner not possible using existing techniques.
Björn Lampinen, Filip Szczepankiewicz, Johan Mårtensson, Danielle van Westen, Oskar Hansson, Carl-Fredrik Westin, and Markus Nilsson. 3/2020. “Towards Unconstrained Compartment Modeling in White Matter Using Diffusion-Relaxation MRI with Tensor-Valued Diffusion Encoding.” Magn Reson Med.Abstract
PURPOSE: To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T values within a two-compartment model of white matter, and to explore the approach in vivo. METHODS: Sampling protocols featuring different b-values (b), b-tensor shapes (b ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB). Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin." RESULTS: Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/b /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm /ms) but higher T values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm /ms) and T values (150 ms). CONCLUSIONS: Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities.
Lorenz Epprecht, Ahad Qureshi, Elliott D Kozin, Nicolas Vachicouras, Alexander M Huber, Ron Kikinis, Nikos Makris, Christian M Brown, Katherine L Reinshagen, and Daniel J Lee. 1/2020. “Human Cochlear Nucleus on 7 Tesla Diffusion Tensor Imaging: Insights Into Micro-anatomy and Function for Auditory Brainstem Implant Surgery.” Otol Neurotol.Abstract
OBJECTIVE: The cochlear nucleus (CN) is the target of the auditory brainstem implant (ABI). Most ABI candidates have Neurofibromatosis Type 2 (NF2) and distorted brainstem anatomy from bilateral vestibular schwannomas. The CN is difficult to characterize as routine structural MRI does not resolve detailed anatomy. We hypothesize that diffusion tensor imaging (DTI) enables both in vivo localization and quantitative measurements of CN morphology. STUDY DESIGN: We analyzed 7 Tesla (T) DTI images of 100 subjects (200 CN) and relevant anatomic structures using an MRI brainstem atlas with submillimetric (50 μm) resolution. SETTING: Tertiary referral center. PATIENTS: Young healthy normal hearing adults. INTERVENTION: Diagnostic. MAIN OUTCOME MEASURES: Diffusion scalar measures such as fractional anisotropy (FA), mean diffusivity (MD), mode of anisotropy (Mode), principal eigenvectors of the CN, and the adjacent inferior cerebellar peduncle (ICP). RESULTS: The CN had a lamellar structure and ventral-dorsal fiber orientation and could be localized lateral to the inferior cerebellar peduncle (ICP). This fiber orientation was orthogonal to tracts of the adjacent ICP where the fibers run mainly caudal-rostrally. The CN had lower FA compared to the medial aspect of the ICP (0.44 ± 0.09 vs. 0.64 ± 0.08, p < 0.001). CONCLUSIONS: 7T DTI enables characterization of human CN morphology and neuronal substructure. An ABI array insertion vector directed more caudally would better correspond to the main fiber axis of CN. State-of-the-art DTI has implications for ABI preoperative planning and future image guidance-assisted placement of the electrode array.
Guoqiang Xie, Fan Zhang, Laura Leung, Michael A Mooney, Lorenz Epprecht, Isaiah Norton, Yogesh Rathi, Ron Kikinis, Ossama Al-Mefty, Nikos Makris, Alexandra J Golby, and Lauren J O'Donnell. 1/2020. “Anatomical Assessment of Trigeminal Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition B-Values and Single- and Multi-Fiber Tracking Strategies.” Neuroimage Clin, 25, Pp. 102160.Abstract
BACKGROUND: The trigeminal nerve (TGN) is the largest cranial nerve and can be involved in multiple inflammatory, compressive, ischemic or other pathologies. Currently, imaging-based approaches to identify the TGN mostly rely on T2-weighted magnetic resonance imaging (MRI), which provides localization of the cisternal portion of the TGN where the contrast between nerve and cerebrospinal fluid (CSF) is high enough to allow differentiation. The course of the TGN within the brainstem as well as anterior to the cisternal portion, however, is more difficult to display on traditional imaging sequences. An advanced imaging technique, diffusion MRI (dMRI), enables tracking of the trajectory of TGN fibers and has the potential to visualize anatomical regions of the TGN not seen on T2-weighted imaging. This may allow a more comprehensive assessment of the nerve in the context of pathology. To date, most work in TGN tracking has used clinical dMRI acquisitions with a b-value of 1000 s/mm and conventional diffusion tensor MRI (DTI) tractography methods. Though higher b-value acquisitions and multi-tensor tractography methods are known to be beneficial for tracking brain white matter fiber tracts, there have been no studies conducted to evaluate the performance of these advanced approaches on nerve tracking of the TGN, in particular on tracking different anatomical regions of the TGN. OBJECTIVE: We compare TGN tracking performance using dMRI data with different b-values, in combination with both single- and multi-tensor tractography methods. Our goal is to assess the advantages and limitations of these different strategies for identifying the anatomical regions of the TGN. METHODS: We proposed seven anatomical rating criteria including true and false positive structures, and we performed an expert rating study of over 1000 TGN visualizations, as follows. We tracked the TGN using high-quality dMRI data from 100 healthy adult subjects from the Human Connectome Project (HCP). TGN tracking performance was compared across dMRI acquisitions with b = 1000 s/mm, b = 2000 s/mm and b = 3000 s/mm, using single-tensor (1T) and two-tensor (2T) unscented Kalman filter (UKF) tractography. This resulted in a total of six tracking strategies. The TGN was identified using an anatomical region-of-interest (ROI) selection approach. First, in a subset of the dataset we identified ROIs that provided good TGN tracking performance across all tracking strategies. Using these ROIs, the TGN was then tracked in all subjects using the six tracking strategies. An expert rater (GX) visually assessed and scored each TGN based on seven anatomical judgment criteria. These criteria included the presence of multiple expected anatomical segments of the TGN (true positive structures), specifically branch-like structures, cisternal portion, mesencephalic trigeminal tract, and spinal cord tract of the TGN. False positive criteria included the presence of any fibers entering the temporal lobe, the inferior cerebellar peduncle, or the middle cerebellar peduncle. Expert rating scores were analyzed to compare TGN tracking performance across the six tracking strategies. Intra- and inter-rater validation was performed to assess the reliability of the expert TGN rating result. RESULTS: The TGN was selected using two anatomical ROIs (Meckel's Cave and cisternal portion of the TGN). The two-tensor tractography method had significantly better performance on identifying true positive structures, while generating more false positive streamlines in comparison to the single-tensor tractography method. TGN tracking performance was significantly different across the three b-values for almost all structures studied. Tracking performance was reported in terms of the percentage of subjects achieving each anatomical rating criterion. Tracking of the cisternal portion and branching structure of the TGN was generally successful, with the highest performance of over 98% using two-tensor tractography and b = 1000 or b = 2000. However, tracking the smaller mesencephalic and spinal cord tracts of the TGN was quite challenging (highest performance of 37.5% and 57.07%, using two-tensor tractography with b = 1000 and b = 2000, respectively). False positive connections to the temporal lobe (over 38% of subjects for all strategies) and cerebellar peduncles (100% of subjects for all strategies) were prevalent. High joint probability of agreement was obtained in the inter-rater (on average 83%) and intra-rater validation (on average 90%), showing a highly reliable expert rating result. CONCLUSIONS: Overall, the results of the study suggest that researchers and clinicians may benefit from tailoring their acquisition and tracking methodology to the specific anatomical portion of the TGN that is of the greatest interest. For example, tracking of branching structures and TGN-T2 overlap can be best achieved with a two-tensor model and an acquisition using b = 1000 or b = 2000. In general, b = 1000 and b = 2000 acquisitions provided the best-rated tracking results. Further research is needed to improve both sensitivity and specificity of the depiction of the TGN anatomy using dMRI.
Ørjan Bergmann, Rafael Henriques, Carl-Fredrik Westin, and Ofer Pasternak. 3/2020. “Fast and Accurate Initialization of the Free-water Imaging Model Parameters from Multi-shell Diffusion MRI.” NMR Biomed, 33, 3, Pp. e4219.Abstract
Cerebrospinal fluid partial volume effect is a known bias in the estimation of Diffusion Tensor Imaging (DTI) parameters from diffusion MRI data. The Free-Water Imaging model for diffusion MRI data adds a second compartment to the DTI model, which explicitly accounts for the signal contribution of extracellular free-water, such as cerebrospinal fluid. As a result the DTI parameters obtained through the free-water model are corrected for partial volume effects, and thus better represent tissue microstructure. In addition, the model estimates the fractional volume of free-water, and can be used to monitor changes in the extracellular space. Under certain assumptions, the model can be estimated from single-shell diffusion MRI data. However, by using data from multi-shell diffusion acquisitions, these assumptions can be relaxed, and the fit becomes more robust. Nevertheless, fitting the model to multi-shell data requires high computational cost, with a non-linear iterative minimization, which has to be initialized close enough to the global minimum to avoid local minima and to robustly estimate the model parameters. Here we investigate the properties of the main initialization approaches that are currently being used, and suggest new fast approaches to improve the initial estimates of the model parameters. We show that our proposed approaches provide a fast and accurate initial approximation of the model parameters, which is very close to the final solution. We demonstrate that the proposed initializations improve the final outcome of non-linear model fitting.
Samo Lasič, Filip Szczepankiewicz, Erica Dall'Armellina, Arka Das, Christopher Kelly, Sven Plein, Jürgen E Schneider, Markus Nilsson, and Irvin Teh. 2/2020. “Motion-compensated b-tensor Encoding for in vivo Cardiac Diffusion-weighted Imaging.” NMR Biomed, 33, 2, Pp. e4213.Abstract
Motion is a major confound in diffusion-weighted imaging (DWI) in the body, and it is a common cause of image artefacts. The effects are particularly severe in cardiac applications, due to the nonrigid cyclical deformation of the myocardium. Spin echo-based DWI commonly employs gradient moment-nulling techniques to desensitise the acquisition to velocity and acceleration, ie, nulling gradient moments up to the 2nd order (M2-nulled). However, current M2-nulled DWI scans are limited to encode diffusion along a single direction at a time. We propose a method for designing b-tensors of arbitrary shapes, including planar, spherical, prolate and oblate tensors, while nulling gradient moments up to the 2nd order and beyond. The design strategy comprises initialising the diffusion encoding gradients in two encoding blocks about the refocusing pulse, followed by appropriate scaling and rotation, which further enables nulling undesired effects of concomitant gradients. Proof-of-concept assessment of in vivo mean diffusivity (MD) was performed using linear and spherical tensor encoding (LTE and STE, respectively) in the hearts of five healthy volunteers. The results of the M2-nulled STE showed that (a) the sequence was robust to cardiac motion, and (b) MD was higher than that acquired using standard M2-nulled LTE, where diffusion-weighting was applied in three orthogonal directions, which may be attributed to the presence of restricted diffusion and microscopic diffusion anisotropy. Provided adequate signal-to-noise ratio, STE could significantly shorten estimation of MD compared with the conventional LTE approach. Importantly, our theoretical analysis and the proposed gradient waveform design may be useful in microstructure imaging beyond diffusion tensor imaging where the effects of motion must be suppressed.
Di Fan, Nikhil N Chaudhari, Kenneth A Rostowsky, Maria Calvillo, Sean K Lee, Nahian F Chowdhury, Fan Zhang, Lauren J O'Donnell, and Andrei Irimia. 7/2019. “Post-Traumatic Cerebral Microhemorrhages and their Effects Upon White Matter Connectivity in the Aging Human Brain.” Conf Proc IEEE Eng Med Biol Soc, 2019, Pp. 198-203.Abstract
Cerebral microbleeds (CMBs), a common manifestation of mild traumatic brain injury (mTBI), have been sporadically implicated in the neurocognitive deficits of mTBI victims but their clinical significance has not been established adequately. Here we investigate the longitudinal effects of post-mTBI CMBs upon the fractional anisotropy (FA) of white matter (WM) in 21 older mTBI patients across the first ~6 months post-injury. CMBs were segmented automatically from susceptibility-weighted imaging (SWI) by leveraging the intensity gradient properties of SWI to identify CMB-related hypointensities using gradient-based edge detection. A detailed diffusion magnetic resonance imaging (dMRI) atlas of WM was used to segment and cluster tractography streamlines whose prototypes were then identified. The correlation coefficient was calculated between (A) FA values at vertices along streamline prototypes and (B) topological (along-streamline) distances between these vertices and the nearest CMB. Across subjects, the CMB identification approach achieved a sensitivity of 97.1% ± 4.7% and a precision of 72.4% ± 11.0% across subjects. The correlation coefficient was found to be negative and, additionally, statistically significant for 12.3% ± 3.5% of WM clusters (p <; 0.05, corrected), whose FA was found to decrease, on average, by 11.8% ± 5.3% across the first 6 months post-injury. These results suggest that CMBs can be associated with deleterious effects upon peri-lesional WM and highlight the vulnerability of older mTBI patients to neurovascular injury.
Filip Szczepankiewicz, Scott Hoge, and Carl-Fredrik Westin. 7/2019. “Linear, Planar and Spherical Tensor-valued Diffusion MRI Data by Free Waveform Encoding in Healthy Brain, Water, Oil and Liquid Crystals.” Data Brief, 25, Pp. 104208.Abstract
Recently, several biophysical models and signal representations have been proposed for microstructure imaging based on tensor-valued, or multidimensional, diffusion MRI. The acquisition of the necessary data requires non-conventional pulse sequences, and data is therefore not available to the wider diffusion MRI community. To facilitate exploration and development of analysis techniques based on tensor-valued diffusion encoding, we share a comprehensive data set acquired in a healthy human brain. The data encompasses diffusion weighted images using linear, planar and spherical diffusion tensor encoding at multiple b-values and diffusion encoding directions. We also supply data acquired in several phantoms that may support validation. The data is hosted by GitHub: https://github.com/filip-szczepankiewicz/Szczepankiewicz_DIB_2019.
Filip Szczepankiewicz, Carl-Fredrik Westin, and Markus Nilsson. 10/2019. “Maxwell-compensated Design of Asymmetric Gradient Waveforms for Tensor-valued Diffusion Encoding.” Magn Reson Med, 82, 4, Pp. 1424-37.Abstract
PURPOSE: Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor-valued diffusion encoding that is not sensitive to concomitant gradients. METHODS: The "Maxwell index" was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of "Maxwell-compensated" waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain. RESULTS: Maxwell-compensated waveforms with Maxwell indices below 100 (mT/m) ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory. CONCLUSION: Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b-tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition, and large FOVs.
Fan Zhang, Lipeng Ning, Lauren J O'Donnell, and Ofer Pasternak. 8/2019. “MK-curve - Characterizing the Relation between Mean Kurtosis and Alterations in the Diffusion MRI Signal.” Neuroimage, 196, Pp. 68-80.Abstract
Diffusion kurtosis imaging (DKI) is a diffusion MRI (dMRI) technique to quantify brain microstructural properties. While DKI measures are sensitive to tissue alterations, they are also affected by signal alterations caused by imaging artifacts such as noise, motion and Gibbs ringing. Consequently, DKI often yields output parameter values (e.g. mean kurtosis; MK) that are implausible. These include implausible values that are outside of the range dictated by physics/biology, and visually apparent implausible values that form unexpected discontinuities, being too high or too low comparing with their neighborhood. These implausible values will introduce bias into any following data analyses (e.g. between-population statistical computation). Existing studies have attempted to correct implausible DKI parameter values in multiple ways; however, these approaches are not always effective. In this study, we propose a novel method for detecting and correcting voxels with implausible values to enable improved DKI parameter estimation. In particular, we focus on MK parameter estimation. We first characterize the relation between MK and alterations in the dMRI signal including diffusion weighted images (DWIs) and the baseline (b0) images. This is done by calculating MK for a range of synthetic DWI or b0 for each voxel, and generating curves (MK-curve) representing how alterations to the input dMRI signals affect the resulting output MK. We find that voxels with implausible MK values are more likely caused by artifacts in the b0 images than artifacts in DWIs with higher b-values. Accordingly, two characteristic b0 values, which define a range of synthetic b0 values that generate implausible MK values, are identified on the MK-curve. Based on this characterization, we propose an automatic approach for detection of voxels with implausible MK values by comparing a voxel's original b0 signal to the identified two characteristic b0 values, along with a correction strategy to replace the original b0 in each detected implausible voxel with a synthetic b0 value computed from the MK-curve. We evaluate the method on a DKI phantom dataset and dMRI datasets from the Human Connectome Project (HCP), and we compare the proposed correction method with other previously proposed correction methods. Results show that our proposed method is able to identify and correct most voxels with implausible DKI parameter values as well as voxels with implausible diffusion tensor parameter values.
Magnus Herberthson, Cem Yolcu, Hans Knutsson, Carl-Fredrik Westin, and Evren Özarslan. 3/2019. “Orientationally-averaged Diffusion-attenuated Magnetic Resonance Signal for Locally-anisotropic Diffusion.” Sci Rep, 9, 1, Pp. 4899.Abstract
Diffusion-attenuated MR signal for heterogeneous media has been represented as a sum of signals from anisotropic Gaussian sub-domains to the extent that this approximation is permissible. Any effect of macroscopic (global or ensemble) anisotropy in the signal can be removed by averaging the signal values obtained by differently oriented experimental schemes. The resulting average signal is identical to what one would get if the micro-domains are isotropically (e.g., randomly) distributed with respect to orientation, which is the case for "powdered" specimens. We provide exact expressions for the orientationally-averaged signal obtained via general gradient waveforms when the microdomains are characterized by a general diffusion tensor possibly featuring three distinct eigenvalues. This extends earlier results which covered only axisymmetric diffusion as well as measurement tensors. Our results are expected to be useful in not only multidimensional diffusion MR but also solid-state NMR spectroscopy due to the mathematical similarities in the two fields.
Lauren J O'Donnell, Alessandro Daducci, Demian Wassermann, and Christophe Lenglet. 4/2019. “Advances in Computational and Statistical Diffusion MRI.” NMR Biomed., 32, 4, Pp. e3805.Abstract
Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole-brain connectivity information that describes the brain's wiring diagram and population-based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high-level overview of interest to diffusion MRI researchers, with a more in-depth treatment to illustrate selected computational advances.
Ye Wu, Fan Zhang, Nikos Makris, Yuping Ning, Isaiah Norton, Shenglin She, Hongjun Peng, Yogesh Rathi, Yuanjing Feng, Huawang Wu, and Lauren J O'Donnell. 11/2018. “Investigation into Local White Matter Abnormality in Emotional Processing and Sensorimotor Areas using an Automatically Annotated Fiber Clustering in Major Depressive Disorder.” Neuroimage, 181, Pp. 16-29.Abstract
This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.
Fan Zhang, Ye Wu, Isaiah Norton, Laura Rigolo, Yogesh Rathi, Nikos Makris, and Lauren J O'Donnell. 11/2018. “An Anatomically Curated Fiber Clustering White Matter Atlas for Consistent White Matter Tract Parcellation across the Lifespan .” Neuroimage, 179, Pp. 429-47.Abstract
This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.
Jordan A Chad, Ofer Pasternak, David H Salat, and Jean J Chen. 11/2018. “Re-examining Age-related Differences in White Matter Microstructure with Free-water Corrected Diffusion Tensor Imaging.” Neurobiol Aging, 71, Pp. 161-70.Abstract
Diffusion tensor imaging (DTI) has been used extensively to investigate white matter (WM) microstructural changes during healthy adult aging. However, WM fibers are known to shrink throughout the lifespan, leading to larger interstitial spaces with age. This could allow more extracellular free water molecules to bias DTI metrics, which are relied upon to provide WM microstructural information. Using a cohort of 212 participants, we demonstrate that WM microstructural changes in aging are potentially less pronounced than previously reported once the free water compartment is eliminated. After free water elimination, DTI parameters show age-related differences that match histological evidence of myelin degradation and debris accumulation. The fraction of free water is further shown to associate better with age than any of the conventional DTI parameters. Our findings suggest that DTI analyses involving free water are likely to yield novel insight into retrospective re-analysis of data and to answer new questions in ongoing DTI studies of brain aging.
Yi Hong, Lauren J O'Donnell, Peter Savadjiev, Fan Zhang, Demian Wassermann, Ofer Pasternak, Hans Johnson, Jane Paulsen, Jean-Paul Vonsattel, Nikos Makris, Carl F Westin, and Yogesh Rathi. 10/2018. “Genetic Load Determines Atrophy in Hand Cortico-striatal Pathways in Presymptomatic Huntington's Disease.” Hum Brain Mapp, 39, 10, Pp. 3871-83.Abstract
Huntington's disease (HD) is an inherited neurodegenerative disorder that causes progressive breakdown of striatal neurons. Standard white matter integrity measures like fractional anisotropy and mean diffusivity derived from diffusion tensor imaging were analyzed in prodromal-HD subjects; however, they studied either a whole brain or specific subcortical white matter structures with connections to cortical motor areas. In this work, we propose a novel analysis of a longitudinal cohort of 243 prodromal-HD individuals and 88 healthy controls who underwent two or more diffusion MRI scans as part of the PREDICT-HD study. We separately trace specific white matter fiber tracts connecting the striatum (caudate and putamen) with four cortical regions corresponding to the hand, face, trunk, and leg motor areas. A multi-tensor tractography algorithm with an isotropic volume fraction compartment allows estimating diffusion of fast-moving extra-cellular water in regions containing crossing fibers and provides quantification of a microstructural property related to tissue atrophy. The tissue atrophy rate is separately analyzed in eight cortico-striatal pathways as a function of CAG-repeats (genetic load) by statistically regressing out age effect from our cohort. The results demonstrate a statistically significant increase in isotropic volume fraction (atrophy) bilaterally in hand fiber connections to the putamen with increasing CAG-repeats, which connects the genetic abnormality (CAG-repeats) to an imaging-based microstructural marker of tissue integrity in specific white matter pathways in HD. Isotropic volume fraction measures in eight cortico-striatal pathways are also correlated significantly with total motor scores and diagnostic confidence levels, providing evidence of their relevance to HD clinical presentation.
Shun Gong, Fan Zhang, Isaiah Norton, Walid I Essayed, Prashin Unadkat, Laura Rigolo, Ofer Pasternak, Yogesh Rathi, Lijun Hou, Alexandra J Golby, and Lauren J O'Donnell. 5/2018. “Free Water Modeling of Peritumoral Edema using Multi-fiber Tractography: Application to Tracking the Arcuate Fasciculus for Neurosurgical Planning.” PLoS One, 13, 5, Pp. e0197056.Abstract
PURPOSE: Peritumoral edema impedes the full delineation of fiber tracts due to partial volume effects in image voxels that contain a mixture of cerebral parenchyma and extracellular water. The purpose of this study is to investigate the effect of incorporating a free water (FW) model of edema for white matter tractography in the presence of edema. MATERIALS AND METHODS: We retrospectively evaluated 26 consecutive brain tumor patients with diffusion MRI and T2-weighted images acquired presurgically. Tractography of the arcuate fasciculus (AF) was performed using the two-tensor unscented Kalman filter tractography (UKFt) method, the UKFt method with a reduced fiber tracking stopping fractional anisotropy (FA) threshold (UKFt+rFA), and the UKFt method with the addition of a FW compartment (UKFt+FW). An automated white matter fiber tract identification approach was applied to delineate the AF. Quantitative measurements included tract volume, edema volume, and mean FW fraction. Visual comparisons were performed by three experts to evaluate the quality of the detected AF tracts. RESULTS: The AF volume in edematous brain hemispheres was significantly larger using the UKFt+FW method (p<0.0001) compared to UKFt, but not significantly larger (p = 0.0996) in hemispheres without edema. The AF size increase depended on the volume of edema: a significant correlation was found between AF volume affected by (intersecting) edema and AF volume change with the FW model (Pearson r = 0.806, p<0.0001). The mean FW fraction was significantly larger in tracts intersecting edema (p = 0.0271). Compared to the UKFt+rFA method, there was a significant increase of the volume of the AF tract that intersected the edema using the UKFt+FW method, while the whole AF volumes were similar. Expert judgment results, based on the five patients with the smallest AF volumes, indicated that the expert readers generally preferred the AF tract obtained by using the FW model, according to their anatomical knowledge and considering the potential influence of the final results on the surgical route. CONCLUSION: Our results indicate that incorporating biophysical models of edema can increase the sensitivity of tractography in regions of peritumoral edema, allowing better tract visualization in patients with high grade gliomas and metastases.
Evren Özarslan, Cem Yolcu, Magnus Herberthson, Hans Knutsson, and Carl-Fredrik Westin. 1/2018. “Influence of the Size and Curvedness of Neural Projections on the Orientationally Averaged Diffusion MR Signal .” Front Phys, 6.Abstract
Neuronal and glial projections can be envisioned to be tubes of infinitesimal diameter as far as diffusion magnetic resonance (MR) measurements via clinical scanners are concerned. Recent experimental studies indicate that the decay of the orientationally-averaged signal in white-matter may be characterized by the power-law, () ∝ , where is the wavenumber determined by the parameters of the pulsed field gradient measurements. One particular study by McKinnon . [1] reports a distinctively faster decay in gray-matter. Here, we assess the role of the size and curvature of the neurites and glial arborizations in these experimental findings. To this end, we studied the signal decay for diffusion along general curves at all three temporal regimes of the traditional pulsed field gradient measurements. We show that for curvy projections, employment of longer pulse durations leads to a disappearance of the decay, while such decay is robust when narrow gradient pulses are used. Thus, in clinical acquisitions, the lack of such a decay for a fibrous specimen can be seen as indicative of fibers that are curved. We note that the above discussion is valid for an intermediate range of -values as the true asymptotic behavior of the signal decay is () ∝ for narrow pulses (through Debye-Porod law) or steeper for longer pulses. This study is expected to provide insights for interpreting the diffusion-weighted images of the central nervous system and aid in the design of acquisition strategies.
Lipeng Ning, Markus Nilsson, Samo Lasič, Carl-Fredrik Westin, and Yogesh Rathi. 2/2018. “Cumulant Expansions for Measuring Water Exchange using Diffusion MRI.” J Chem Phys, 148, 7, Pp. 074109.Abstract
The rate of water exchange across cell membranes is a parameter of biological interest and can be measured by diffusion magnetic resonance imaging (dMRI). In this work, we investigate a stochastic model for the diffusion-and-exchange of water molecules. This model provides a general solution for the temporal evolution of dMRI signal using any type of gradient waveform, thereby generalizing the signal expressions for the Kärger model. Moreover, we also derive a general nth order cumulant expansion of the dMRI signal accounting for water exchange, which has not been explored in earlier studies. Based on this analytical expression, we compute the cumulant expansion for dMRI signals for the special case of single diffusion encoding (SDE) and double diffusion encoding (DDE) sequences. Our results provide a theoretical guideline on optimizing experimental parameters for SDE and DDE sequences, respectively. Moreover, we show that DDE signals are more sensitive to water exchange at short-time scale but provide less attenuation at long-time scale than SDE signals. Our theoretical analysis is also validated using Monte Carlo simulations on synthetic structures.