Publications by Year: 2015

BACKGROUND: Placement accuracy of ventriculostomy catheters is reported in a wide and variable range. Development of an efficient image-guidance system may improve physician performance and patient safety. OBJECTIVE: We evaluate the prototype of Smart Stylet, a new electromagnetic image-guidance system for use during bedside ventriculostomy. METHODS: Accuracy of the Smart Stylet system was assessed. System operators were evaluated for their ability to successfully target the ipsilateral frontal horn in a phantom model. RESULTS: Target registration error across 15 intracranial targets ranged from 1.3 to 4.6 mm (mean 3.1 mm). Using Smart Stylet guidance, a test operator successfully passed a ventriculostomy catheter to a shifted ipsilateral frontal horn 20/20 (100%) times from the frontal approach in a skull phantom. Without Smart Stylet guidance, the operator was successful 4/10 (40%) times from the right frontal approach and 6/10 (60%) times from the left frontal approach. In a separate experiment, resident operators were successful 2/4 (50%) times when targeting the shifted ipsilateral frontal horn with Smart Stylet guidance and 0/4 (0%) times without image guidance using a skull phantom. CONCLUSIONS: Smart Stylet may improve the ability to successfully target the ventricles during frontal ventriculostomy.

We introduce a nuclear magnetic resonance method for quantifying the shape of axially symmetric microscopic diffusion tensors in terms of a new diffusion anisotropy metric, DΔ, which has unique values for oblate, spherical, and prolate tensor shapes. The pulse sequence includes a series of equal-amplitude magnetic field gradient pulse pairs, the directions of which are tailored to give an axially symmetric diffusion-encoding tensor b with variable anisotropy bΔ. Averaging of data acquired for a range of orientations of the symmetry axis of the tensor b renders the method insensitive to the orientation distribution function of the microscopic diffusion tensors. Proof-of-principle experiments are performed on water in polydomain lyotropic liquid crystals with geometries that give rise to microscopic diffusion tensors with oblate, spherical, and prolate shapes. The method could be useful for characterizing the geometry of fluid-filled compartments in porous solids, soft matter, and biological tissues.

RATIONALE: Previous studies on chronic bronchitis (CB) have used varying definitions. OBJECTIVES: We sought to compare an alternative CB definition, using the St. George’s Respiratory Questionnaire (SGRQ), a commonly used assessment tool, with the classic definition and to investigate if it had independent or additive value. METHODS: We analyzed data from 4,513 subjects from Global Initiative for Chronic Obstructive Lung Disease groups 1 to 4 in the COPDGene cohort. We compared the classic definition of CB with the SGRQ definition, defined by their answers to the questions about both cough and phlegm. We compared the Classic CB+ versus CB- groups, and the SGRQ CB+ and CB- groups. We also analyzed the cohort split into four groups: Classic CB+/SGRQ CB+, Classic CB+/SGRQ CB-, Classic CB-/SGRQ CB+, Classic CB-/SGRQ CB-. MEASUREMENTS AND MAIN RESULTS: A total of 26.1% subjects were Classic CB+, whereas 39.9% were SGRQ CB+. When the SGRQ definition was compared with the Classic CB definition, using this as the gold standard, the SGRQ CB definition had a sensitivity and specificity of 0.87 and 0.77, respectively. The SGRQ CB+ and Classic CB+ groups were strikingly similar, with more respiratory symptoms and exacerbations, worse lung function, and greater airway wall thickness. In addition, the Classic CB+/SGRQ CB+, Classic CB+/SGRQ CB-, and Classic CB-/SGRQ CB+ groups shared similar characteristics as well. CONCLUSIONS: The SGRQ CB definition identifies more subjects with chronic cough and sputum who share a similar phenotype identified by the Classic CB definition. The addition of the SGRQ CB definition to the classic one can be used to identify more patients with chronic obstructive pulmonary disease at risk for poor outcomes.

Pulmonary hypertension is associated with advanced chronic obstructive pulmonary disease (COPD), although pulmonary vascular changes occur early in the course of the disease. Pulmonary artery (PA) enlargement (PAE) measured by computed tomography correlates with pulmonary hypertension and COPD exacerbation frequency. Genome-wide association studies of PAE in subjects with COPD have not been reported. To investigate whether genetic variants are associated with PAE within subjects with COPD, we investigated data from current and former smokers from the COPDGene Study and the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints study. The ratio of the diameter of the PA to the diameter of the aorta (A) was measured using computed tomography. PAE was defined as PA/A greater than 1. A genome-wide association study for COPD with PAE was performed using subjects with COPD without PAE (PA/A = 1) as a control group. A secondary analysis used smokers with normal spirometry as a control group. Genotyping was performed on Illumina platforms. The results were summarized using fixed-effect meta-analysis. Both meta-analyses revealed a genome-wide significant locus on chromosome 15q25.1 in IREB2 (COPD with versus without PAE, rs7181486; odds ratio [OR] = 1.32; P = 2.10 × 10(-8); versus smoking control subjects, rs2009746; OR = 1.42; P = 1.32 × 10(-9)). PAE was also associated with a region on 14q31.3 near the GALC gene (rs7140285; OR = 1.55; P = 3.75 × 10(-8)). Genetic variants near IREB2 and GALC likely contribute to genetic susceptibility to PAE associated with COPD. This study provides evidence for genetic heterogeneity associated with a clinically important COPD vascular subtype.

In this paper we propose a new method for shape analysis based on the ordering of shapes using band-depth. We use this band-depth to non-parametrically define a global depth for a shape with respect to a reference population, typically consisting of normal control subjects. This allows us to globally quantify differences with respect to "normality". Using the depth-ordering of shapes also allows the detection of localized shape differences by using α-central values of shapes. We propose permutation tests to statistically assess global and local shape differences. We further determine the directionality of shape differences (local inflation versus deflation). The method is evaluated on a synthetically generated striatum dataset, and applied to detect shape differences in the hippocampus between subjects with first-episode schizophrenia and normal controls.

PURPOSE: To use T2 and diffusion MR to determine the change in the mechanical function of human disks with increased degenerative state. MATERIALS AND METHODS: Spatial changes in T2 and diffusion were quantified in five cadaveric human lumbar disks under compressive loads. Regression models were used to investigate the relationship between the change in MR parameters and the disk’s dynamic and viscoelastic properties.

BACKGROUND: Diffusion imaging tractography is increasingly used to trace critical fiber tracts in brain tumor patients to reduce the risk of post-operative neurological deficit. However, the effects of peritumoral edema pose a challenge to conventional tractography using the standard diffusion tensor model. The aim of this study was to present a novel technique using a two-tensor unscented Kalman filter (UKF) algorithm to track the arcuate fasciculus (AF) in brain tumor patients with peritumoral edema. METHODS: Ten right-handed patients with left-sided brain tumors in the vicinity of language-related cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-Tesla magnetic resonance imaging (MRI) including a diffusion-weighted dataset with 31 directions. Fiber tractography was performed using both single-tensor streamline and two-tensor UKF tractography. A two-regions-of-interest approach was applied to perform the delineation of the AF. Results from the two different tractography algorithms were compared visually and quantitatively. RESULTS: Using single-tensor streamline tractography, the AF appeared disrupted in four patients and contained few fibers in the remaining six patients. Two-tensor UKF tractography delineated an AF that traversed edematous brain areas in all patients. The volume of the AF was significantly larger on two-tensor UKF than on single-tensor streamline tractography (p 

We present an innovative framework for reconstructing high-spatial-resolution diffusion magnetic resonance imaging (dMRI) from multiple low-resolution (LR) images. Our approach combines the twin concepts of compressed sensing (CS) and classical super-resolution to reduce acquisition time while increasing spatial resolution. We use subpixel-shifted LR images with down-sampled and non-overlapping diffusion directions to reduce acquisition time. The diffusion signal in the high resolution (HR) image is represented in a sparsifying basis of spherical ridgelets to model complex fiber orientations with reduced number of measurements. The HR image is obtained as the solution of a convex optimization problem which can be solved using the proposed algorithm based on the alternating direction method of multipliers (ADMM). We qualitatively and quantitatively evaluate the performance of our method on two sets of in-vivo human brain data and show its effectiveness in accurately recovering very high resolution diffusion images.

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.