Publications by Year: 2016

We have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tracts from diffusion MRI volumes. Further, our method simplifies the quantification and statistical analysis of white matter tracts on large diffusion MRI databases. This work reflects the careful syntactical definition of major white matter fiber tracts in the human brain based on a neuroanatomist’s expert knowledge. The framework is based on a novel query language with a near-to-English textual syntax. This query language makes it possible to construct a dictionary of anatomical definitions that describe white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This novel method makes it possible to automatically label white matter anatomy across subjects. After describing this method, we provide an example of its implementation where we encode anatomical knowledge in human white matter for ten association and 15 projection tracts per hemisphere, along with seven commissural tracts. Importantly, this novel method is comparable in accuracy to manual labeling. Finally, we present results applying this method to create a white matter atlas from 77 healthy subjects, and we use this atlas in a small proof-of-concept study to detect changes in association tracts that characterize schizophrenia.

Diffusion nuclear magnetic resonance (NMR) is a powerful technique for studying porous media, but yields ambiguous results when the sample comprises multiple regions with different pore sizes, shapes, and orientations. Inspired by solid-state NMR techniques for correlating isotropic and anisotropic chemical shifts, we propose a diffusion NMR method to resolve said ambiguity. Numerical data inversion relies on sparse representation of the data in a basis of radial and axial diffusivities. Experiments are performed on a composite sample with a cell suspension and a liquid crystal.

PURPOSE: MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume. METHODS: The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms. RESULTS: The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers. CONCLUSION: It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR.

In recent years evidence has accumulated to suggest that neuroinflammation might be an early pathology of schizophrenia that later leads to neurodegeneration, yet the exact role in the etiology, as well as the source of neuroinflammation, are still not known. The hypothesis of neuroinflammation involvement in schizophrenia is quickly gaining popularity, and thus it is imperative that we have reliable and reproducible tools and measures that are both sensitive, and, most importantly, specific to neuroinflammation. The development and use of appropriate human in vivo imaging methods can help in our understanding of the location and extent of neuroinflammation in different stages of the disorder, its natural time-course, and its relation to neurodegeneration. Thus far, there is little in vivo evidence derived from neuroimaging methods. This is likely the case because the methods that are specific and sensitive to neuroinflammation are relatively new or only just being developed. This paper provides a methodological review of both existing and emerging positron emission tomography and magnetic resonance imaging techniques that identify and characterize neuroinflammation. We describe \how these methods have been used in schizophrenia research. We also outline the shortcomings of existing methods, and we highlight promising future techniques that will likely improve state-of-the-art neuroimaging as a more refined approach for investigating neuroinflammation in schizophrenia.

The effects of lesions on syntactic comprehension were studied in thirty-one people with aphasia (PWA). Participants were tested for the ability to parse and interpret four types of syntactic structures and elements - passives, object extracted relative clauses, reflexives and pronouns - in three tasks - object manipulation, sentence picture matching with full sentence presentation and sentence picture matching with self-paced listening presentation. Accuracy, end-of-sentence RT and self-paced listening times for each word were measured. MR scans were obtained and analyzed for total lesion volume and for lesion size in 48 cortical areas. Lesion size in several areas of the left hemisphere was related to accuracy in particular sentence types in particular tasks and to self-paced listening times for critical words in particular sentence types. The results support a model of brain organization that includes areas that are specialized for the combination of particular syntactic and interpretive operations and the use of the meanings produced by those operations to accomplish task-related operations.

This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out 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 the study of complex tissue architecture.

Stejskal and Tanner’s ingenious pulsed field gradient design from 1965 has made diffusion NMR and MRI the mainstay of most studies seeking to resolve microstructural information in porous systems in general and biological systems in particular. Methods extending beyond Stejskal and Tanner’s design, such as double diffusion encoding (DDE) NMR and MRI, may provide novel quantifiable metrics that are less easily inferred from conventional diffusion acquisitions. Despite the growing interest on the topic, the terminology for the pulse sequences, their parameters, and the metrics that can be derived from them remains inconsistent and disparate among groups active in DDE. Here, we present a consensus of those groups on terminology for DDE sequences and associated concepts. Furthermore, the regimes in which DDE metrics appear to provide microstructural information that cannot be achieved using more conventional counterparts (in a model-free fashion) are elucidated. We highlight in particular DDE’s potential for determining microscopic diffusion anisotropy and microscopic fractional anisotropy, which offer metrics of microscopic features independent of orientation dispersion and thus provide information complementary to the standard, macroscopic, fractional anisotropy conventionally obtained by diffusion MR. Finally, we discuss future vistas and perspectives for DDE.

The technique of stray field diffusion NMR is adapted to study the diffusion properties of water in monodisperse wet foams. We show for the first time, that the technique is capable of observing q-space diffusion diffraction peaks in monodisperse aqueous foams with initial bubble sizes in the range of 50-85μm. The position of the peak maximum can be correlated simply to the bubble size in the foam leading to a technique that can investigate the stability of the foam over time. The diffusion technique, together with supplementary spin-spin relaxation analysis of the diffusion data is used to follow the stability and coarsening behaviour of monodisperse foams with a water fraction range between 0.24 and 0.33. The monodisperse foams remain stable for a period of hours in terms of the initial bubble size. The duration of this stable period correlates to the initial size of the bubbles. Eventually the bubbles begin to coarsen and this is observed in changes in the position of the diffusion diffraction maxima.

BACKGROUND: Structural alterations of the lateral temporal cortex (LTC) in association with memory impairments have been reported in schizophrenia. This study investigated whether alterations of LTC structure were linked with impaired facial and/or verbal memory in young first-degree relatives of people with schizophrenia and, thus, may be indicators of vulnerability to the illness. METHODS: Subjects included 27 non-psychotic, first-degree relatives of schizophrenia patients, and 48 healthy controls, between the ages of 13 and 28. Participants underwent high-resolution magnetic resonance imaging (MRI) at 1.5Tesla. The LTC was parcellated into superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, and temporal pole. Total cerebral and LTC volumes were measured using semi-automated morphometry. The Wechsler Memory Scale - Third Edition and the Children’s Memory Scale - Third Edition assessed facial and verbal memory. General linear models tested for associations among LTC subregion volumes, familial risk and memory. RESULTS: Compared with controls, relatives had significantly smaller bilateral middle temporal gyri. Moreover, right middle temporal gyral volume showed a significant positive association with delayed facial memory in relatives. CONCLUSION: These results support the hypothesis that smaller middle temporal gyri are related to the genetic liability to schizophrenia and may be linked with reduced facial memory in persons at genetic risk for the illness. The findings add to the growing evidence that children at risk for schizophrenia on the basis of positive family history have cortical and subcortical structural brain abnormalities well before psychotic illness occurs.

Patients with chronic thromboembolic pulmonary hypertension (CTEPH) have morphologic changes to the pulmonary vasculature. These include pruning of the distal vessels, dilation of the proximal vessels, and increased vascular tortuosity. Advances in image processing and computer vision enable objective detection and quantification of these processes in clinically acquired computed tomographic (CT) scans. Three-dimensional reconstructions of the pulmonary vasculature were created from the CT angiograms of 18 patients with CTEPH diagnosed using imaging and hemodynamics as well as 15 control patients referred to our Dyspnea Clinic and found to have no evidence of pulmonary vascular disease. Compared to controls, CTEPH patients exhibited greater pruning of the distal vasculature (median density of small-vessel volume: 2.7 [interquartile range (IQR): 2.5-3.0] vs. 3.2 [3.0-3.8]; P = 0.008), greater dilation of proximal arteries (median fraction of blood in large arteries: 0.35 [IQR: 0.30-0.41] vs. 0.23 [0.21-0.31]; P = 0.0005), and increased tortuosity in the pulmonary arterial tree (median: 4.92% [IQR: 4.85%-5.21%] vs. 4.63% [4.39%-4.92%]; P = 0.004). CTEPH was not associated with dilation of proximal veins or increased tortuosity in the venous system. Distal pruning of the vasculature was correlated with the cardiac index (R = 0.51, P = 0.04). Quantitative models derived from CT scans can be used to measure changes in vascular morphology previously described subjectively in CTEPH. These measurements are also correlated with invasive metrics of pulmonary hemodynamics, suggesting that they may be used to assess disease severity. Further work in a larger cohort may enable the use of such measures as a biomarker for diagnostic, phenotyping, and prognostic purposes.