David W. Shattuck

YNIMG Journal 2015 Journal Article

Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization

• Chitresh Bhushan
• Justin P. Haldar
• Soyoung Choi
• Anand A. Joshi
• David W. Shattuck
• Richard M. Leahy

Diffusion MRI provides quantitative information about microstructural properties which can be useful in neuroimaging studies of the human brain. Echo planar imaging (EPI) sequences, which are frequently used for acquisition of diffusion images, are sensitive to inhomogeneities in the primary magnetic (B0) field that cause localized distortions in the reconstructed images. We describe and evaluate a new method for correction of susceptibility-induced distortion in diffusion images in the absence of an accurate B0 fieldmap. In our method, the distortion field is estimated using a constrained non-rigid registration between an undistorted T1-weighted anatomical image and one of the distorted EPI images from diffusion acquisition. Our registration framework is based on a new approach, INVERSION (Inverse contrast Normalization for VERy Simple registratION), which exploits the inverted contrast relationship between T1- and T2-weighted brain images to define a simple and robust similarity measure. We also describe how INVERSION can be used for rigid alignment of diffusion images and T1-weighted anatomical images. Our approach is evaluated with multiple in vivo datasets acquired with different acquisition parameters. Compared to other methods, INVERSION shows robust and consistent performance in rigid registration and shows improved alignment of diffusion and anatomical images relative to normalized mutual information for non-rigid distortion correction.

YNIMG Journal 2010 Journal Article

Comparison of landmark-based and automatic methods for cortical surface registration

• Dimitrios Pantazis
• Anand Joshi
• Jintao Jiang
• David W. Shattuck
• Lynne E. Bernstein
• Hanna Damasio
• Richard M. Leahy

Group analysis of structure or function in cerebral cortex typically involves, as a first step, the alignment of cortices. A surface-based approach to this problem treats the cortex as a convoluted surface and coregisters across subjects so that cortical landmarks or features are aligned. This registration can be performed using curves representing sulcal fundi and gyral crowns to constrain the mapping. Alternatively, registration can be based on the alignment of curvature metrics computed over the entire cortical surface. The former approach typically involves some degree of user interaction in defining the sulcal and gyral landmarks while the latter methods can be completely automated. Here we introduce a cortical delineation protocol consisting of 26 consistent landmarks spanning the entire cortical surface. We then compare the performance of a landmark-based registration method that uses this protocol with that of two automatic methods implemented in the software packages FreeSurfer and BrainVoyager. We compare performance in terms of discrepancy maps between the different methods, the accuracy with which regions of interest are aligned, and the ability of the automated methods to correctly align standard cortical landmarks. Our results show similar performance for ROIs in the perisylvian region for the landmark-based method and FreeSurfer. However, the discrepancy maps showed larger variability between methods in occipital and frontal cortex and automated methods often produce misalignment of standard cortical landmarks. Consequently, selection of the registration approach should consider the importance of accurate sulcal alignment for the specific task for which coregistration is being performed. When automatic methods are used, the users should ensure that sulci in regions of interest in their studies are adequately aligned before proceeding with subsequent analysis.

YNIMG Journal 2010 Journal Article

Sulcal set optimization for cortical surface registration

• Anand A. Joshi
• Dimitrios Pantazis
• Quanzheng Li
• Hanna Damasio
• David W. Shattuck
• Arthur W. Toga
• Richard M. Leahy

Flat mapping based cortical surface registration constrained by manually traced sulcal curves has been widely used for inter subject comparisons of neuroanatomical data. Even for an experienced neuroanatomist, manual sulcal tracing can be quite time consuming, with the cost increasing with the number of sulcal curves used for registration. We present a method for estimation of an optimal subset of size N C from N possible candidate sulcal curves that minimizes a mean squared error metric over all combinations of N C curves. The resulting procedure allows us to estimate a subset with a reduced number of curves to be traced as part of the registration procedure leading to optimal use of manual labeling effort for registration. To minimize the error metric we analyze the correlation structure of the errors in the sulcal curves by modeling them as a multivariate Gaussian distribution. For a given subset of sulci used as constraints in surface registration, the proposed model estimates registration error based on the correlation structure of the sulcal errors. The optimal subset of constraint curves consists of the N C sulci that jointly minimize the estimated error variance for the subset of unconstrained curves conditioned on the N C constraint curves. The optimal subsets of sulci are presented and the estimated and actual registration errors for these subsets are computed.

YNIMG Journal 2009 Journal Article

Active fibers: Matching deformable tract templates to diffusion tensor images

• Ilya Eckstein
• David W. Shattuck
• Jason L. Stein
• Katie L. McMahon
• Greig de Zubicaray
• Margaret J. Wright
• Paul M. Thompson
• Arthur W. Toga

Reliable quantitative analysis of white matter connectivity in the brain is an open problem in neuroimaging, with common solutions requiring tools for fiber tracking, tractography segmentation and estimation of intersubject correspondence. This paper proposes a novel, template matching approach to the problem. In the proposed method, a deformable fiber-bundle model is aligned directly with the subject tensor field, skipping the fiber tracking step. Furthermore, the use of a common template eliminates the need for tractography segmentation and defines intersubject shape correspondence. The method is validated using phantom DTI data and applications are presented, including automatic fiber-bundle reconstruction and tract-based morphometry.

YNIMG Journal 2009 Journal Article

Online resource for validation of brain segmentation methods

• David W. Shattuck
• Gautam Prasad
• Mubeena Mirza
• Katherine L. Narr
• Arthur W. Toga

One key issue that must be addressed during the development of image segmentation algorithms is the accuracy of the results they produce. Algorithm developers require this so they can see where methods need to be improved and see how new developments compare with existing ones. Users of algorithms also need to understand the characteristics of algorithms when they select and apply them to their neuroimaging analysis applications. Many metrics have been proposed to characterize error and success rates in segmentation, and several datasets have also been made public for evaluation. Still, the methodologies used in analyzing and reporting these results vary from study to study, so even when studies use the same metrics their numerical results may not necessarily be directly comparable. To address this problem, we developed a web-based resource for evaluating the performance of skull-stripping in T1-weighted MRI. The resource provides both the data to be segmented and an online application that performs a validation study on the data. Users may download the test dataset, segment it using whichever method they wish to assess, and upload their segmentation results to the server. The server computes a series of metrics, displays a detailed report of the validation results, and archives these for future browsing and analysis. We applied this framework to the evaluation of 3 popular skull-stripping algorithms — the Brain Extraction Tool [Smith, S. M. , 2002. Fast robust automated brain extraction. Hum. Brain Mapp. 17 (3), 143–155 (Nov)], the Hybrid Watershed Algorithm [Ségonne, F. , Dale, A. M. , Busa, E. , Glessner, M. , Salat, D. , Hahn, H. K. , Fischl, B. , 2004. A hybrid approach to the skull stripping problem in MRI. NeuroImage 22 (3), 1060–1075 (Jul)], and the Brain Surface Extractor [Shattuck, D. W. , Sandor-Leahy, S. R. , Schaper, K. A. , Rottenberg, D. A. , Leahy, R. M. , 2001. Magnetic resonance image tissue classification using a partial volume model. NeuroImage 13 (5), 856–876 (May) under several different program settings. Our results show that with proper parameter selection, all 3 algorithms can achieve satisfactory skull-stripping on the test data.

YNIMG Journal 2008 Journal Article

Construction of a 3D probabilistic atlas of human cortical structures

• David W. Shattuck
• Mubeena Mirza
• Vitria Adisetiyo
• Cornelius Hojatkashani
• Georges Salamon
• Katherine L. Narr
• Russell A. Poldrack
• Robert M. Bilder

We describe the construction of a digital brain atlas composed of data from manually delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal volunteers. This labeling was performed according to a set of protocols developed for this project. Pairs of raters were assigned to each structure and trained on the protocol for that structure. Each rater pair was tested for concordance on 6 of the 40 brains; once they had achieved reliability standards, they divided the task of delineating the remaining 34 brains. The data were then spatially normalized to well-known templates using 3 popular algorithms: AIR5. 2. 5’s nonlinear warp (Woods et al. , 1998) paired with the ICBM452 Warp 5 atlas (Rex et al. , 2003), FSL’s FLIRT (Smith et al. , 2004) was paired with its own template, a skull-stripped version of the ICBM152 T1 average; and SPM5’s unified segmentation method (Ashburner and Friston, 2005) was paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced 3 variants of our atlas, where each was constructed from 40 representative samples of a data processing stream that one might use for analysis. For each normalization algorithm, the individual structure delineations were then resampled according to the computed transformations. We next computed averages at each voxel location to estimate the probability of that voxel belonging to each of the 56 structures. Each version of the atlas contains, for every voxel, probability densities for each region, thus providing a resource for automated probabilistic labeling of external data types registered into standard spaces; we also computed average intensity images and tissue density maps based on the three methods and target spaces. These atlases will serve as a resource for diverse applications including meta-analysis of functional and structural imaging data and other bioinformatics applications where display of arbitrary labels in probabilistically defined anatomic space will facilitate both knowledge-based development and visualization of findings from multiple disciplines.

YNIMG Journal 2008 Journal Article

Erratum to “A meta-algorithm for brain extraction in MRI” [NeuroImage 23 (2004) 625–637]

• David E. Rex
• David W. Shattuck
• Roger P. Woods
• Katherine L. Narr
• Eileen Luders
• Kelly Rehm
• Sarah E. Stoltzner
• David A. Rottenberg

YNIMG Journal 2006 Journal Article

Cerebellar cortical atrophy in experimental autoimmune encephalomyelitis

• Allan Mackenzie-Graham
• Matthew R. Tinsley
• Kaanan P. Shah
• Cynthia Aguilar
• Lauren V. Strickland
• Jyl Boline
• Melanie Martin
• Laurie Morales

Brain atrophy measured by MRI is an important correlate with clinical disability and disease duration in multiple sclerosis (MS). Unfortunately, neuropathologic mechanisms which lead to this grey matter atrophy remain unknown. The objective of this study was to determine whether brain atrophy occurs in the mouse model, experimental autoimmune encephalomyelitis (EAE). Postmortem high-resolution T2-weighted magnetic resonance microscopy (MRM) images from 32 mouse brains (21 EAE and 11 control) were collected. A minimum deformation atlas was constructed and a deformable atlas approach was used to quantify volumetric changes in neuroanatomical structures. A significant decrease in the mean cerebellar cortex volume in mice with late EAE (48–56 days after disease induction) as compared to normal strain, gender, and age-matched controls was observed. There was a direct correlation between cerebellar cortical atrophy and disease duration. At an early time point in disease, 15 days after disease induction, cerebellar white matter lesions were detected by both histology and MRM. These data demonstrate that myelin-specific autoimmune responses can lead to grey matter atrophy in an otherwise normal CNS. The model described herein can now be used to investigate neuropathologic mechanisms that lead to the development of gray matter atrophy in this setting.

YNIMG Journal 2004 Journal Article

A meta-algorithm for brain extraction in MRI

• David E. Rex
• David W. Shattuck
• Roger P. Woods
• Katherine L. Narr
• Eileen Luders
• Kelly Rehm
• Sarah E. Stolzner
• David A. Rottenberg

Accurate identification of brain tissue and cerebrospinal fluid (CSF) in a whole-head MRI is a critical first step in many neuroimaging studies. Automating this procedure can eliminate intra- and interrater variance and greatly increase throughput for a labor-intensive step. Many available procedures perform differently across anatomy and under different acquisition protocols. We developed the Brain Extraction Meta-Algorithm (BEMA) to address these concerns. It executes many extraction algorithms and a registration procedure in parallel to combine the results in an intelligent fashion and obtain improved results over any of the individual algorithms. Using an atlas space, BEMA performs a voxelwise analysis of training data to determine the optimal Boolean combination of extraction algorithms to produce the most accurate result for a given voxel. This allows the provided extractors to be used differentially across anatomy, increasing both the accuracy and robustness of the procedure. We tested BEMA using modified forms of BrainSuite's Brain Surface Extractor (BSE), FSL's Brain Extraction Tool (BET), AFNI's 3dIntracranial, and FreeSurfer's MRI Watershed as well as FSL's FLIRT for the registration procedure. Training was performed on T1-weighted scans of 136 subjects from five separate data sets with different acquisition parameters on separate scanners. Testing was performed on 135 separate subjects from the same data sets. BEMA outperformed the individual algorithms, as well as interrater results from a subset of the scans, when compared for the mean Dice coefficient, a rating of the similarity of output masks to the manually defined gold standards.

YNIMG Journal 2001 Journal Article

Evaluation of six algorithms for correcting intensity non-uniformity effects in MRI volumes

• Kirt A. Schaper
• James B. Arnold
• Jeih-San Liow
• Joshua J. Stern
• John G. Sled
• David W. Shattuck
• Andrew J. Worth
• Mark S. Cohen

YNIMG Journal 2001 Journal Article

Magnetic Resonance Image Tissue Classification Using a Partial Volume Model

• David W. Shattuck
• Stephanie R. Sandor-Leahy
• Kirt A. Schaper
• David A. Rottenberg
• Richard M. Leahy

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average κ indices of κ = 0. 746 ± 0. 114 for gray matter (GM) and κ = 0. 798 ± 0. 089 for white matter (WM) compared to expert labeled data. Our method achieved average κ indices κ = 0. 893 ± 0. 041 for GM and κ = 0. 928 ± 0. 039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom.

YNIMG Journal 2001 Journal Article

Qualitative and Quantitative Evaluation of Six Algorithms for Correcting Intensity Nonuniformity Effects

• James B. Arnold
• Jeih-San Liow
• Kirt A. Schaper
• Joshua J. Stern
• John G. Sled
• David W. Shattuck
• Andrew J. Worth
• Mark S. Cohen

The desire to correct intensity nonuniformity in magnetic resonance images has led to the proliferation of nonuniformity-correction (NUC) algorithms with different theoretical underpinnings. In order to provide end users with a rational basis for selecting a given algorithm for a specific neuroscientific application, we evaluated the performance of six NUC algorithms. We used simulated and real MRI data volumes, including six repeat scans of the same subject, in order to rank the accuracy, precision, and stability of the nonuniformity corrections. We also compared algorithms using data volumes from different subjects and different (1. 5T and 3. 0T) MRI scanners in order to relate differences in algorithmic performance to intersubject variability and/or differences in scanner performance. In phantom studies, the correlation of the extracted with the applied nonuniformity was highest in the transaxial (left-to-right) direction and lowest in the axial (top-to-bottom) direction. Two of the six algorithms demonstrated a high degree of stability, as measured by the iterative application of the algorithm to its corrected output. While none of the algorithms performed ideally under all circumstances, locally adaptive methods generally outperformed nonadaptive methods.