Publications by Year: 2009

PURPOSE: To investigate the utility of a proposed clinical diffusion imaging scheme for rapidly generating multiple b-value diffusion contrast in brain magnetic resonance imaging (MRI) with high signal-to-noise ratio (SNR). MATERIALS AND METHODS: Our strategy for efficient image acquisition relies on the invariance property of the diffusion tensor eigenvectors to b-value. A simple addition to the conventional diffusion tensor MR imaging (DTI) data acquisition scheme used for tractography yields diffusion-weighted images at twice and three times the conventional b-value. An example from a neurosurgical brain tumor is shown. Apparent diffusion-weighted (ADW) images were calculated for b-values 800, 1600, and 2400 s/mm(2), and a map of excess diffusive kurtosis was computed from the three ADWs. RESULTS: High b-value ADW images demonstrated decreased contrast between normal gray and white matter, while the heterogeneity and contrast of the lesion was emphasized relative to conventional b-value data. Kurtosis maps indicated the deviation from Gaussian diffusive behavior. CONCLUSION: DTI data with multiple b-values and good SNR can be acquired in clinically reasonable times. High b-value ADW images show increased contrast and add information to conventional DWI. Ambiguity in conventional b-value images over whether hyperintense signal results from abnormally low diffusion, or abnormally long T(2), is better resolved in high b-value images.

Functional MRI (fMRI) has become one of the leading methods for brain mapping in neuroscience. Recent advances in fMRI analysis were used to define the default state of brain activity, functional connectivity and basal activity. Basal activity measured with fMRI raised tremendous interest among neuroscientists since synchronized brain activity pattern could be retrieved while the subject rests (resting state fMRI). During recent years, a few signal processing schemes have been suggested to analyze the resting state blood oxygenation level dependent (BOLD) signal from simple correlations to spectral decomposition. In most of these analysis schemes, the question asked was which brain areas "behave" in the time domain similarly to a pre-specified ROI. In this work we applied short time frequency analysis and clustering to study the spatial signal characteristics of resting state fMRI time series. Such analysis revealed that clusters of similar BOLD fluctuations are found in the cortex but also in the white matter and sub-cortical gray matter regions (thalamus). We found high similarities between the BOLD clusters and the neuro-anatomical appearance of brain regions. Additional analysis of the BOLD time series revealed a strong correlation between head movements and clustering quality. Experiments performed with T1-weighted time series also provided similar quality of clustering. These observations led us to the conclusion that non-functional contributions to the BOLD time series can also account for symmetric appearance of signal fluctuations. These contributions may include head motions, the underling microvasculature anatomy and cellular morphology.

PURPOSE: To determine if diffusion tensor imaging (DTI) of the median nerve could allow identification of patients with carpal tunnel syndrome (CTS). MATERIALS AND METHODS: A total of 13 healthy subjects and 9 CTS patients were scanned on a 3T magnetic resonance imaging (MRI) scanner. The MRI protocol included a DTI sequence from which the fractional anisotropy (FA), apparent diffusion coefficient (ADC), and the parallel and radial diffusivities could be extracted. Those parameters were quantified at different locations along the median nerve (proximal to the carpal tunnel, within the carpal tunnel, and distal to the carpal tunnel). RESULTS: At the carpal tunnel, the FA, radial diffusivity, and ADC differed significantly between healthy subjects and CTS patients (P

PURPOSE: A method to register endoscopic and laparoscopic ultrasound (US) images in real time with pre-operative computed tomography (CT) data sets has been developed with the goal of improving diagnosis, biopsy guidance, and surgical interventions in the abdomen. METHODS: The technique, which has the potential to operate in real time, is based on a new phase correlation technique: LEPART, which specifies the location of a plane in the CT data which best corresponds to the US image. Validation of the method was carried out using an US phantom with cyst regions and with retrospective analysis of data sets from animal model experiments. RESULTS: The phantom validation study shows that local translation displacements can be recovered for each US frame with a root mean squared error of 1.56 +/- 0.78 mm in less than 5 sec, using non-optimized algorithm implementations. CONCLUSION: A new method for multimodality (preoperative CT and intraoperative US endoscopic images) registration to guide endoscopic interventions was developed and found to be efficient using clinically realistic datasets. The algorithm is inherently capable of being implemented in a parallel computing system so that full real time operation appears likely.

Regional assessment of lung disease (such as chronic obstructive pulmonary disease) is a critical component to accurate patient diagnosis. Software tools than enable such analysis are also important for clinical research studies. In this work, we present an image segmentation and data representation framework that enables quantitative analysis specific to different lung regions on high resolution computed tomography (HRCT) datasets. We present an offline, fully automatic image processing chain that generates airway, vessel, and lung mask segmentations in which the left and right lung are delineated. We describe a novel lung lobe segmentation tool that produces reproducible results with minimal user interaction. A usability study performed across twenty datasets (inspiratory and expiratory exams including a range of disease states) demonstrates the tool’s ability to generate results within five to seven minutes on average. We also describe a data representation scheme that involves compact encoding of label maps such that both "regions" (such as lung lobes) and "types" (such as emphysematous parenchyma) can be simultaneously represented at a given location in the HRCT.

We describe a technique to simultaneously estimate a local neural fiber model and trace out its path. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by the previous. To do this we model the signal as a mixture of Gaussian tensors and perform tractography within a filter framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the local model and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. Synthetic experiments demonstrate that this approach reduces signal reconstruction error and significantly improves the angular resolution at crossings and branchings. In vivo experiments confirm the ability to trace out fibers in areas known to contain such crossing and branching while providing inherent path regularization.

We describe a technique to simultaneously estimate a weighted, positive-definite multi-tensor fiber model and perform tractography. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by the previous. To do this we model the signal as a weighted mixture of Gaussian tensors and perform tractography within a filter framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the local model and propagate in the most consistent direction. Further, we modify the Kalman filter to enforce model constraints, i.e. positive eigenvalues and convex weights. Despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. Synthetic experiments demonstrate that this approach significantly improves the angular resolution at crossings and branchings while consistently estimating the mixture weights. In vivo experiments confirm the ability to trace out fibers in areas known to contain such crossing and branching while providing inherent path regularization.

BACKGROUND: CT scan measures of emphysema and airway disease have been correlated with lung function in cohorts of subjects with a range of COPD severity. The contribution of CT scan-assessed airway disease to objective measures of lung function and respiratory symptoms such as dyspnea in severe emphysema is less clear. METHODS: Using data from 338 subjects in the National Emphysema Treatment Trial (NETT) Genetics Ancillary Study, densitometric measures of emphysema using a threshold of -950 Hounsfield units (%LAA-950) and airway wall phenotypes of the wall thickness (WT) and the square root of wall area (SRWA) of a 10-mm luminal perimeter airway were calculated for each subject. Linear regression analysis was performed for outcome variables FEV(1) and percent predicted value of FEV(1) with CT scan measures of emphysema and airway disease.

The relationship between quantitative airway measurements on computed tomography (CT) and airflow limitation in individuals with severe alpha (1)-antitrypsin deficiency (AATD) is undefined. Thus, we planned to clarify the relationship between CT-based airway indices and airflow limitation in AATD. 52 patients with AATD underwent chest CT and pre-bronchodilator spirometry at three institutions. In the right upper (RUL) and lower (RLL) lobes, wall area percent (WA%) and luminal area (Ai) were measured in the third, fourth, and fifth generations of the bronchi. The severity of emphysema was also calculated in each lobe and expressed as low attenuation area percent (LAA%). Correlations between obtained measurements and FEV(1)% predicted (FEV(1)%P) were evaluated by the Spearman rank correlation test. In RUL, WA% of all generations was significantly correlated with FEV(1)%P (3rd, R = -0.33, p = 0.02; 4th, R = -0.39, p = 0.004; 5th, R = -0.57, p 0.001; respectively). Ai also showed significant correlations (3rd, R = 0.32, p = 0.02; 4th, R = 0.34, p = 0.01; 5th, R = 0.56, p 0.001; respectively). Measured correlation coefficients improved when the airway progressed distally from the third to fifth generations. LAA% also correlated with FEV(1)%P (R = -0.51, p 0.001). In RLL, WA% showed weak correlations with FEV(1)%P in all generations (3rd, R = -0.34, p = 0.01; 4th, R = -0.30, p = 0.03; 5th, R = -0.31, p = 0.03; respectively). Only Ai from the fifth generation significantly correlated with FEV(1)%P in this lobe (R = 0.34, p = 0.01). LAA% strongly correlated with FEV(1)%P (R = -0.71, p 0.001). We conclude therefore that quantitative airway measurements are significantly correlated with airflow limitation in AATD, particularly in the distal airways of RUL. Emphysema of the lower lung is the predominant component; however, airway disease also has a significant impact on airflow limitation in AATD.

Particle systems have gained importance as a methodology for sampling implicit surfaces and segmented objects to improve mesh generation and shape analysis. We propose that particle systems have a significantly more general role in sampling structure from unsegmented data. We describe a particle system that computes samplings of crease features (i.e. ridges and valleys, as lines or surfaces) that effectively represent many anatomical structures in scanned medical data. Because structure naturally exists at a range of sizes relative to the image resolution, computer vision has developed the theory of scale-space, which considers an n-D image as an (n+1)-D stack of images at different blurring levels. Our scale-space particles move through continuous four-dimensional scale-space according to spatial constraints imposed by the crease features, a particle-image energy that draws particles towards scales of maximal feature strength, and an inter-particle energy that controls sampling density in space and scale. To make scale-space practical for large three-dimensional data, we present a spline-based interpolation across scale from a small number of pre-computed blurrings at optimally selected scales. The configuration of the particle system is visualized with tensor glyphs that display information about the local Hessian of the image, and the scale of the particle. We use scale-space particles to sample the complex three-dimensional branching structure of airways in lung CT, and the major white matter structures in brain DTI.