Kelly Rehm

The Evaluation of Preprocessing Choices in Single-Subject BOLD fMRI Using NPAIRS Performance Metrics

This work proposes an alternative to simulation-based receiver operating characteristic (ROC) analysis for assessment of fMRI data analysis methodologies. Specifically, we apply the rapidly developing nonparametric prediction, activation, influence, and reproducibility resampling (NPAIRS) framework to obtain cross-validation-based model performance estimates of prediction accuracy and global reproducibility for various degrees of model complexity. We rely on the concept of an analysis chain meta-model in which all parameters of the preprocessing steps along with the final statistical model are treated as estimated model parameters. Our ROC analog, then, consists of plotting prediction vs. reproducibility results as curves of model complexity for competing meta-models. Two theoretical underpinnings are crucial to utilizing this new validation technique. First, we explore the relationship between global signal-to-noise and our reproducibility estimates as derived previously. Second, we submit our model complexity curves in the prediction versus reproducibility space as reflecting classic bias-variance tradeoffs. Among the particular analysis chains considered, we found little impact in performance metrics with alignment, some benefit with temporal detrending, and greatest improvement with spatial smoothing.

A meta-algorithm for brain extraction in MRI

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 BrainSuites Brain Surface Extractor (BSE), FSLs Brain Extraction Tool (BET), AFNIs 3dIntracranial, and FreeSurfers MRI Watershed as well as FSLs 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.

Quasi-Conformally Flat Mapping the Human Cerebellum

We present a novel approach to creating flat maps of the brain. It is impossible to flatten a curved surface in 3D space without metric and areal distortion; however, the Riemann Mapping Theorem implies that it is theoretically possible to preserve conformal (angular) information under flattening. Our approach attempts to preserve the conformal structure between the original cortical surface in 3-space and the flattened surface. We demonstrate this with data from the human cerebellum and we produce maps in the conventional Euclidean plane, as well as in the hyperbolic plane and on a sphere. Conformal mappings are uniquely determined once certain normalizations have been chosen, and this allows one to impose a coordinate system on the surface when flattening in the hyperbolic or spherical setting. Unlike existing methods, our approach does not require that cuts be introduced in the original surface. In addition, hyperbolic and spherical maps allow the map focus to be transformed interactively to correspond to any anatomical landmark.

Putting our heads together: a consensus approach to brain/non-brain segmentation in T1-weighted MR volumes.

We describe an approach to brain extraction from T1-weighted MR volumes that uses a hierarchy of masks created by different models to form a consensus mask. The algorithm (McStrip) incorporates atlas-based extraction via nonlinear warping, intensity-threshold masking with connectivity constraints, and edge-based masking with morphological operations. Volume and boundary metrics were computed to evaluate the reproducibility and accuracy of McStrip against manual brain extraction on 38 scans from normal and ataxic subjects. McStrip masks were reproducible across six repeat scans of a normal subject and were significantly more accurate than the masks produced by any of the individual algorithmic components.

Activation pattern reproducibility: measuring the effects of group size and data analysis models.

The reproducibility of patterns from brain activation experiments has been examined only for suprathreshold spatially localized foci. Scatter plots comparing signal levels across all pairs of Talairach voxels for pairs of functional activation images provide an alternative approach for assessing reproducibility. Image‐wide, signal‐level reproducibility may be quantitatively summarized using pattern similarity measures such as the Pearson product‐moment correlation, ρ. Empirical population distributions of ρ for many pair‐wise image comparisons, generated using statistical resampling techniques, may be used to examine the impact of a wide range of experimental variables. We demonstrate the use of such empirical ρ‐histograms to measure changes in reproducibility for [15O]‐water PET scans of a simple motor task as a function of group size and data analysis model. Hum. Brain Mapping 5:312–316, 1997.

A Geometric Method for Automatic Extraction of Sulcal Fundi

Sulcal fundi are 3-D curves that lie in the depths of the cerebral cortex and, in addition to their intrinsic value in brain research, are often used as landmarks for downstream computations in brain imaging. In this paper, we present a geometric algorithm that automatically extracts the sulcal fundi from magnetic resonance images and represents them as spline curves lying on the extracted triangular mesh representing the cortical surface. The input to our algorithm is a triangular mesh representation of an extracted cortical surface as computed by one of several available software packages for performing automated and semi-automated cortical surface extraction. Given this input we first compute a geometric depth measure for each triangle on the cortical surface mesh, and based on this information we extract sulcal regions by checking for connected regions exceeding a depth threshold. We then identify endpoints of each region and delineate the fundus by thinning the connected region while keeping the endpoints fixed. The curves, thus, defined are regularized using weighted splines on the surface mesh to yield high-quality representations of the sulcal fundi. We present the geometric framework and validate it with real data from human brains. Comparisons with expert-labeled sulcal fundi are part of this validation process

The Presurgical Status of the Alveolar Cleft and Success of Secondary Bone Grafting

OBJECTIVE The primary purpose of this study was to evaluate presurgical status of the alveolar cleft site and success of secondary alveolar bone grafting. DESIGN Thirty patient records were retrospectively reviewed. Patients selected for inclusion had isolated cleft of at least the primary palate. Patients with additional anomalies were not selected. The study population consisted of 15 female sites and 16 male cleft sites. There were two bilateral cleft lip and palate (CLP) patients and 28 unilateral CLP patients. The age at the time of the secondary alveolar bone grafting ranged from 7 years to 14 years, 4 months. SETTING The study was conducted at the Cleft Palate Clinic at the University of Minnesota, School of Dentistry. METHOD Presurgical radiographs taken at least 1 month prior to the secondary bone grafting and postsurgical radiographs taken at least 6 months after bone surgery were measured. Measurements included size of the cleft defect and bone support for distal and mesial teeth adjacent to the cleft. Evaluation of success was determined on the basis of postsurgical measurements of satisfactory, intermediate, and unsatisfactory outcomes. RESULTS AND CONCLUSION The size of the cleft defect was not correlated with the success rate of the secondary alveolar bone grafting. If the amount of distal bone support for the mesial tooth was the same as those in a periodontally healthy individual, a satisfactory outcome was 5.8 times more likely. If the amount of mesial bone support for the distal tooth was the same as those in a periodontally healthy individual, the satisfactory outcome was 3.8 times more likely. Although not a primary purpose of the study, it was found that in this study population, if the patient was female, a satisfactory outcome was 3.8 times more likely.

Quantitative evaluation of three cortical surface flattening methods

During the past decade, several computational approaches have been proposed for the task of mapping highly convoluted surfaces of the human brain to simpler geometric objects such as a sphere or a topological disc. We report the results of a quantitative comparison of FreeSurfer, CirclePack, and LSCM with respect to measurements of geometric distortion and computational speed. Our results indicate that FreeSurfer performs best with respect to a global measurement of metric distortion, whereas LSCM performs best with respect to angular distortion and best in all but one case with a local measurement of metric distortion. FreeSurfer provides more homogeneous distribution of metric distortion across the whole cortex than CirclePack and LSCM. LSCM is the most computationally efficient algorithm for generating spherical maps, while CirclePack is extremely fast for generating planar maps from patches.

Cortical surface flattening using least square conformal mapping with minimal metric distortion

Although flattening a cortical surface necessarily introduces metric distortion due to the non-constant Gaussian curvature of the surface, the Riemann mapping theorem states that continuously differentiable surfaces can be mapped without angular distortion. We apply the so-called least-square conformal mapping approach to flatten a patch of the cortical surface onto planar regions and to produce spherical conformal maps of the entire cortex while minimizing metric distortion within the class of conformal maps. Our method, which preserves angular information and controls metric distortion, only involves the solution of a linear system and a nonlinear minimization problem with three parameters and is a very fast approach.

Design and testing of artifact-suppressed adaptive histogram equalization : a contrast-enhancement technique for display of digital chest radiographs

One of the goals of our research in the field of digital radiography has been to develop contrast-enhancement algorithms for eventual use in the display of chest images on video devices with the aim of preserving the diagnostic information presently available with film, some of which would normally be lost because of the smaller dynamic range of video monitors. The ASAHE algorithm discussed in this article has been tested by investigating observer performance in a difficult detection task involving phantoms and simulated lung nodules, using film as the output medium. The results of the experiment showed that the algorithm is successful in providing contrast-enhanced, natural-looking chest images while maintaining diagnostic information. The algorithm did not effect an increase in nodule detectability, but this was not unexpected because film is a medium capable of displaying a wide range of gray levels. It is sufficient at this stage to show that there is no degradation in observer performance. Future tests will evaluate the performance of the ASAHE algorithm in preparing chest images for video display.