Lauren J. O’Donnell

An Introduction to Diffusion Tensor Image Analysis

Diffusion tensor magnetic resonance imaging (DTI) is a relatively new technology that is popular for imaging the white matter of the brain. This article provides a basic and broad overview of DTI to enable the reader to develop an intuitive understanding of these types of data, and an awareness of their strengths and weaknesses.

A combined fMRI and DTI examination of functional language lateralization and arcuate fasciculus structure: Effects of degree versus direction of hand preference

The present study examined the relationship between hand preference degree and direction, functional language lateralization in Brocas and Wernickes areas, and structural measures of the arcuate fasciculus. Results revealed an effect of degree of hand preference on arcuate fasciculus structure, such that consistently-handed individuals, regardless of the direction of hand preference, demonstrated the most asymmetric arcuate fasciculus, with larger left versus right arcuate, as measured by DTI. Functional language lateralization in Wernickes area, measured via fMRI, was related to arcuate fasciculus volume in consistent-left-handers only, and only in people who were not right hemisphere lateralized for language; given the small sample size for this finding, future investigation is warranted. Results suggest handedness degree may be an important variable to investigate in the context of neuroanatomical asymmetries.

White matter tract clustering and correspondence in populations

We present a novel method for finding white matter fiber correspondences and clusters across a population of brains. Our input is a collection of paths from tractography in every brain. Using spectral methods we embed each path as a vector in a high dimensional space. We create the embedding space so that it is common across all brains, consequently similar paths in all brains will map to points near each other in the space. By performing clustering in this space we are able to find matching fiber tract clusters in all brains. In addition, we automatically obtain correspondence of tractographic paths across brains: by selecting one or several paths of interest in one brain, the most similar paths in all brains are obtained as the nearest points in the high-dimensional space.

Diffusion Tensor and Functional MRI Fusion with Anatomical MRI for Image-Guided Neurosurgery

In order to achieve its main goal of maximal tumor removal while avoiding postoperative neurologic deficits, neuro-oncological surgery is strongly dependent on image guidance. Among all currently available imaging modalities, MRI provides the best anatomic detail and is highly sensitive for intracranial pathology. However, conventional MRI does not detect the exact location of white matter tracts or areas of cortical activation. This essential information can be obtained non-invasively by means of diffusion tensor MRI (DT-MRI) and functional MRI (fMRI) respectively. Here we present our initial experience with fMRI and DT-MRI for surgical planning and guidance in ten brain tumor cases.

Unbiased Groupwise Registration of White Matter Tractography

We present what we believe to be the first investigation into unbiased multi-subject registration of whole brain diffusion tractography of the white matter. To our knowledge, this is also the first entropy-based objective function applied to fiber tract registration. To define the probability of fiber trajectories for the computation of entropy, we take advantage of a pairwise fiber distance used as the basis for a Gaussian-like kernel. By employing several values of the kernels scale parameter, the method is inherently multi-scale. Results of experiments using synthetic and real datasets demonstrate the potential of the method for simultaneous joint registration of tractography.

High-Dimensional white matter atlas generation and group analysis

We present a two-step process including white matter atlas generation and automatic segmentation. Our atlas generation method is based on population fiber clustering. We produce an atlas which contains high-dimensional descriptors of fiber bundles as well as anatomical label information. We use the atlas to automatically segment tractography in the white matter of novel subjects and we present quantitative results (FA measurements) in segmented white matter regions from a small population. We demonstrate reproducibility of these measurements across scans. In addition, we introduce the idea of using clustering for automatic matching of anatomical structures across hemispheres.

Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?

Diffusion magnetic resonance imaging (dMRI) is the modality of choice for investigating in-vivo white matter connectivity and neural tissue architecture of the brain. The diffusion-weighted signal in dMRI reflects the diffusivity of water molecules in brain tissue and can be utilized to produce image-based biomarkers for clinical research. Due to the constraints on scanning time, a limited number of measurements can be acquired within a clinically feasible scan time. In order to reconstruct the dMRI signal from a discrete set of measurements, a large number of algorithms have been proposed in recent years in conjunction with varying sampling schemes, i.e., with varying b-values and gradient directions. Thus, it is imperative to compare the performance of these reconstruction methods on a single data set to provide appropriate guidelines to neuroscientists on making an informed decision while designing their acquisition protocols. For this purpose, the SPArse Reconstruction Challenge (SPARC) was held along with the workshop on Computational Diffusion MRI (at MICCAI 2014) to validate the performance of multiple reconstruction methods using data acquired from a physical phantom. A total of 16 reconstruction algorithms (9 teams) participated in this community challenge. The goal was to reconstruct single b-value and/or multiple b-value data from a sparse set of measurements. In particular, the aim was to determine an appropriate acquisition protocol (in terms of the number of measurements, b-values) and the analysis method to use for a neuroimaging study. The challenge did not delve on the accuracy of these methods in estimating model specific measures such as fractional anisotropy (FA) or mean diffusivity, but on the accuracy of these methods to fit the data. This paper presents several quantitative results pertaining to each reconstruction algorithm. The conclusions in this paper provide a valuable guideline for choosing a suitable algorithm and the corresponding data-sampling scheme for clinical neuroscience applications.

Automated white matter fiber tract identification in patients with brain tumors

We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.

Interface Detection in Diffusion Tensor MRI

We present a new method for detecting the interface, or edge, structure present in diffusion MRI. Interface detection is an important first step for applications including segmentation and registration. Additionally, due to the higher dimensionality of tensor data, humans are visually unable to detect edges as easily as in scalar data, so edge detection has potential applications in diffusion tensor visualization. Our method employs the computer vision techniques of local structure filtering and normalized convolution. We detect the edges in the tensor field by calculating a generalized local structure tensor, based on the sum of the outer products of the gradients of the tensor components. The local structure tensor provides a rotationally invariant description of edge orientation, and its shape after local averaging describes the type of edge. We demonstrate the ability to detect not only edges caused by differences in tensor magnitude, but also edges between regions of different tensor shape. We demonstrate the method’s performance on synthetic data, on major fiber tract boundaries, and in one gray matter region.

Corticospinal tract modeling for neurosurgical planning by tracking through regions of peritumoral edema and crossing fibers using two-tensor unscented Kalman filter tractography

PurposeThe aim of this study was to present a tractography algorithm using a two-tensor unscented Kalman filter (UKF) to improve the modeling of the corticospinal tract (CST) by tracking through regions of peritumoral edema and crossing fibers.MethodsTen patients with brain tumors in the vicinity of motor cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-T magnetic resonance imaging (MRI) including functional MRI (fMRI) and a diffusion-weighted data set with 31 directions. Fiber tracking was performed using both single-tensor streamline and two-tensor UKF tractography methods. A two-region-of-interest approach was used to delineate the CST. Results from the two tractography methods were compared visually and quantitatively. fMRI was applied to identify the functional fiber tracts.ResultsSingle-tensor streamline tractography underestimated the extent of tracts running through the edematous areas and could only track the medial projections of the CST. In contrast, two-tensor UKF tractography tracked fanning projections of the CST despite peritumoral edema and crossing fibers. Based on visual inspection, the two-tensor UKF tractography delineated tracts that were closer to motor fMRI activations, and it was apparently more sensitive than single-tensor streamline tractography to define the tracts directed to the motor sites. The volume of the CST was significantly larger on two-tensor UKF than on single-tensor streamline tractography (