Richard M Leahy

University of Southern California, Los Angeles, California, United States

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Publications (287)477.67 Total impact

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    ABSTRACT: Obesity is a global health problem, particularly in the U.S. where one third of adults are obese. A reliable and accurate method of quantifying obesity is necessary. Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) are two measures of obesity that reflect different associated health risks, but accurate measurements in humans or rodent models are difficult. In this paper we present an automatic, registration-based segmentation method for mouse adiposity studies using microCT images. We co-register the subject CT image and a mouse CT atlas. Our method is based on surface matching of the microCT image and an atlas. Surface-based elastic volume warping is used to match the internal anatomy. We acquired a whole body scan of a C57BL6/J mouse injected with contrast agent using microCT and created a whole body mouse atlas by manually delineate the boundaries of the mouse and major organs. For method verification we scanned a C57BL6/J mouse from the base of the skull to the distal tibia. We registered the obtained mouse CT image to our atlas. Preliminary results show that we can warp the atlas image to match the posture and shape of the subject CT image, which has significant differences from the atlas. We plan to use this software tool in longitudinal obesity studies using mouse models.
    03/2014;
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    ABSTRACT: We present a method based on spectral theory for the shape analysis of carpal bones of the human wrist. We represent the cortical surface of the carpal bone in a coordinate system based on the eigensystem of the two-dimensional Helmholtz equation. We employ a metric-global point signature (GPS)-that exploits the scale and isometric invariance of eigenfunctions to quantify overall bone shape. We use a fast finite-element-method to compute the GPS metric. We capitalize upon the properties of GPS representation-such as stability, a standard Euclidean (ℓ(2)) metric definition, and invariance to scaling, translation and rotation-to perform shape analysis of the carpal bones of ten women and ten men from a publicly-available database. We demonstrate the utility of the proposed GPS representation to provide a means for comparing shapes of the carpal bones across populations.
    Physics in Medicine and Biology 02/2014; 59(4):961-73. · 2.70 Impact Factor
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    ABSTRACT: Time-of-flight (TOF) information improves the signal-to-noise ratio in positron emission tomography (PET). The computation cost in processing TOF-PET sinograms is substantially higher than for nonTOF data because the data in each line of response is divided among multiple TOF bins. This additional cost has motivated research into methods for rebinning TOF data into lower dimensional representations that exploit redundancies inherent in TOF data. We have previously developed approximate Fourier methods that rebin TOF data into either three-dimensional (3D) nonTOF or 2D nonTOF formats. We refer to these methods respectively as FORET-3D and FORET-2D. Here we describe maximum a posteriori (MAP) estimators for use with FORET rebinned data. We first derive approximate expressions for the variance of the rebinned data. We then use these results to rescale the data so that the variance and mean are approximately equal allowing us to use the Poisson likelihood model for MAP reconstruction. MAP reconstruction from these rebinned data uses a system matrix in which the detector response model accounts for the effects of rebinning. Using these methods we compare the performance of FORET-2D and 3D with TOF and nonTOF reconstructions using phantom and clinical data. Our phantom results show a small loss in contrast recovery at matched noise levels using FORET compared to reconstruction from the original TOF data. Clinical examples show FORET images that are qualitatively similar to those obtained from the original TOF-PET data but with a small increase in variance at matched resolution. Reconstruction time is reduced by a factor of 5 and 30 using FORET3D+MAP and FORET2D+MAP respectively compared to 3D TOF MAP, which makes these methods attractive for clinical applications.
    Physics in Medicine and Biology 02/2014; 59(4):925-949. · 2.70 Impact Factor
  • Yu-Teng Chang, Dimitrios Pantazis, Richard M Leahy
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    ABSTRACT: A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks.
    NeuroImage 01/2014; · 6.25 Impact Factor
  • W. Zhu, Q. Li, B. Bai, P.S. Conti, R.M. Leahy
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    ABSTRACT: We investigate using dual time-point PET data to perform Patlak modeling. This approach can be used for whole body dynamic PET studies in which we compute voxel-wise estimates of Patlak parameters using two frames of data for each bed position. Our approach directly uses list-mode arrival times for each event to estimate the Patlak parametric image. We use a penalized likelihood method in which the penalty function uses spatially variant weighting to ensure a count independent local impulse response. We evaluate performance of the method in comparison to fractional changes in SUV values (%DSUV) between the two frames using Cramer Rao analysis and Monte Carlo simulation. Receiver operating characteristic (ROC) curves are used to compare performance in differentiating tumors relative to background based on the dynamic data sets. Using area under the ROC curve as a performance metric, we show superior performance of Patlak relative to %DSUV over a range of dynamic data sets and parameters. These results suggest that Patlak analysis may be appropriate for analysis of dual time-point whole body PET data and could lead to superior detection of tumors relative to %DSUV metrics.
    IEEE Transactions on Medical Imaging 01/2014; 33(4):913-924. · 4.03 Impact Factor
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    ABSTRACT: To enable high-quality correction of susceptibility-induced geometric distortion artifacts in diffusion magnetic resonance imaging (MRI) images without increasing scan time. A new method for distortion correction is proposed based on subsampling a generalized version of the state-of-the-art reversed-gradient distortion correction method. Rather than acquire each q-space sample multiple times with different distortions (as in the conventional reversed-gradient method), we sample each q-space point once with an interlaced sampling scheme that measures different distortions at different q-space locations. Distortion correction is achieved using a novel constrained reconstruction formulation that leverages the smoothness of diffusion data in q-space. The effectiveness of the proposed method is demonstrated with simulated and in vivo diffusion MRI data. The proposed method is substantially faster than the reversed-gradient method, and can also provide smaller intensity errors in the corrected images and smaller errors in derived quantitative diffusion parameters. The proposed method enables state-of-the-art distortion correction performance without increasing data acquisition time. Magn Reson Med, 2013. © 2013 Wiley Periodicals, Inc.
    Magnetic Resonance in Medicine 11/2013; · 3.27 Impact Factor
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    ABSTRACT: The estimation and analysis of kinetic parameters in dynamic PET is frequently confounded by tissue heterogeneity and partial volume effects. We propose a new constrained model of dynamic PET to address these limitations. The proposed formulation incorporates an explicit mixture model in which each image voxel is represented as a mixture of different pure tissue types with distinct temporal dynamics.We use Cram´er-Rao lower bounds to demonstrate that the use of prior information is important to stabilize parameter estimation with this model. As a result, we propose a constrained formulation of the estimation problem that we solve using a two-stage algorithm. In the first stage, a sparse signal processing method is applied to estimate the rate parameters for the different tissue compartments from the noisy PET time series. In the second stage, tissue fractions and the linear parameters of different time activity curves are estimated using a combination of spatial-regularity and fractional mixture constraints. A block coordinate descent algorithm is combined with a manifold search to robustly estimate these parameters. The method is evaluated with both simulated and experimental dynamic PET data.
    IEEE transactions on medical imaging. 11/2013;
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    ABSTRACT: Estimating and modeling functional connectivity in the brain is a challenging problem with potential applications in the understanding of brain organization and various neurological and neuropsychological conditions. An important objective in connectivity analysis is to determine the connections between regions of interest in the brain. However, traditional functional connectivity analyses have frequently focused on modeling interactions between time series recordings at individual sensors, voxels, or vertices despite the fact that a single region of interest will often include multiple such recordings. In this paper, we present a novel measure of interaction between regions of interest rather than individual signals. The proposed measure,termed canonical Granger causality, combines ideas from canonical correlation and Granger causality analysis to yield a measure that reflects directed causality between two regions of interest. In particular, canonical Granger causality uses optimized linear combinations of signals from each region of interest to enable accurate causality measurements from substantially less data compared to alternative multivariate methods that have previously been proposed for this scenario. The optimized linear combinations are obtained using a variation of a technique developed for optimization on the Steifel manifold. We demonstrate the advantages of canonical Granger causality in comparison to alternative causality measures for a range of different simulated datasets. We also apply the proposed measure to local field potential data recorded in a macaque brain during a visuomotor task. Results demonstrate that canonical Granger causality can be used to identify causal relationships between striate and prestriate cortex in cases where standard Granger causality is unable to identify statistically significant interactions.
    NeuroImage 06/2013; · 6.25 Impact Factor
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    19th Annual Meeting of the Organization for Human Brain Mapping, Seattle, WA, USA; 06/2013
  • Sergul Aydore, Dimitrios Pantazis, Richard M Leahy
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    ABSTRACT: We investigate the properties of the Phase Locking Value (PLV) and the Phase Lag Index (PLI) as metrics for quantifying interactions in bivariate local field potential (LFP), electroencephalography (EEG) and magnetoencephalography (MEG) data. In particular we describe the relationship between nonparametric estimates of PLV and PLI and the parameters of two distributions that can both be used to model phase interactions. The first of these is the von Mises distribution, for which the sample PLV is a maximum likelihood estimator. The second is the relative phase distribution associated with bivariate circularly symmetric complex Gaussian data. We derive an explicit expression for the PLV for this distribution and show that it is a function of the cross-correlation between the two signals. We compare the bias and variance of the sample PLV and the PLV computed from the cross-correlation. We also show that both the von Mises and Gaussian models are suitable for representing relative phase in application to LFP data from a visually-cued motor study in macaque. We then compare results using the two different PLV estimators and conclude that, for this data, the sample PLV provides equivalent information to the cross-correlation of the two complex time series.
    NeuroImage 02/2013; · 6.25 Impact Factor
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    ABSTRACT: The ability to use electrophysiological brain signals to decode various parameters of voluntary movement is a central question in Brain Machine Interface (BMI) research. Invasive BMI systems can successfully decode movement trajectories from the spiking activity of neurons in primary motor cortex and posterior parietal cortex. It has long been assumed that non-invasive techniques do not provide sufficient signal resolution to decode the kinematics of complex time-varying movements. This view stems from the hypothesis that movement parameters such as direction, position, velocity, or acceleration are primarily encoded by neuronal firing in motor cortex. Consequently, the fact that such signals cannot be detected using non-invasive techniques such as Electroencephalography (EEG) or Magnetoencephalography (MEG) has led to the claim that fine movement properties cannot be decoded with non-invasive methods. However, this view has been proven wrong by numerous studies in recent years. First, a growing body of research over the last decade has shown that the local field potential (LFP) signal, which represents the summed activity of a neuronal population, can encode movement parameters at a level comparable to unit recordings. These findings were confirmed in humans by the successful use of electrocorticography (ECoG) to achieve continuous movement decoding via invasive human BMI approaches. Very recently, a number of non-invasive studies were able to provide striking evidence that even surface-level MEG or EEG data can contain sufficient information on hand movement in order to infer movement direction and hand kinematics from brain signals recorded using non-invasive methods. Here we provide a brief review of this recent literature and discuss its importance on the future of BMI research and its implications on the development of novel motor rehabilitation strategies.
    ITBM-RBM 02/2013; 32:8-18.
  • Justin P Haldar, Richard M Leahy
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    ABSTRACT: This paper presents a novel family of linear transforms that can be applied to data collected from the surface of a 2-sphere in three-dimensional Fourier space. This family of transforms generalizes the previously-proposed Funk-Radon Transform (FRT), which was originally developed for estimating the orientations of white matter fibers in the central nervous system from diffusion magnetic resonance imaging data. The new family of transforms is characterized theoretically, and efficient numerical implementations of the transforms are presented for the case when the measured data is represented in a basis of spherical harmonics. After these general discussions, attention is focused on a particular new transform from this family that we name the Funk-Radon and Cosine Transform (FRACT). Based on theoretical arguments, it is expected that FRACT-based analysis should yield significantly better orientation information (e.g., improved accuracy and higher angular resolution) than FRT-based analysis,while maintaining the strong characterizability and computational efficiency of the FRT. Simulations are used to confirm these theoretical characteristics, and the practical significance of the proposed approach is illustrated with real diffusion weighted MRI brain data. These experiments demonstrate that, in addition to having strong theoretical characteristics, the proposed approach can outperform existing state-of-the-art orientation estimation methods with respect to measures such as angularresolution and robustness to noise and modeling errors.
    NeuroImage 01/2013; · 6.25 Impact Factor
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    Joyita Dutta, Richard M Leahy, Quanzheng Li
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    ABSTRACT: Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The objective of this paper is to develop and characterize a denoising framework for dynamic PET based on non-local means (NLM). NLM denoising computes weighted averages of voxel intensities assigning larger weights to voxels that are similar to a given voxel in terms of their local neighborhoods or patches. We introduce three key modifications to tailor the original NLM framework to dynamic PET. Firstly, we derive similarities from less noisy later time points in a typical PET acquisition to denoise the entire time series. Secondly, we use spatiotemporal patches for robust similarity computation. Finally, we use a spatially varying smoothing parameter based on a local variance approximation over each spatiotemporal patch. To assess the performance of our denoising technique, we performed a realistic simulation on a dynamic digital phantom based on the Digimouse atlas. For experimental validation, we denoised [Formula: see text] PET images from a mouse study and a hepatocellular carcinoma patient study. We compared the performance of NLM denoising with four other denoising approaches - Gaussian filtering, PCA, HYPR, and conventional NLM based on spatial patches. The simulation study revealed significant improvement in bias-variance performance achieved using our NLM technique relative to all the other methods. The experimental data analysis revealed that our technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.
    PLoS ONE 01/2013; 8(12):e81390. · 3.53 Impact Factor
  • Bing Bai, Quanzheng Li, Richard M Leahy
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    ABSTRACT: The resolution of positron emission tomography (PET) images is limited by the physics of positron-electron annihilation and instrumentation for photon coincidence detection. Model-based methods that incorporate accurate physical and statistical models have produced significant improvements in reconstructed image quality when compared with filtered backprojection reconstruction methods. However, it has often been suggested that by incorporating anatomical information, the resolution and noise properties of PET images could be further improved, leading to better quantitation or lesion detection. With the recent development of combined MR-PET scanners, we can now collect intrinsically coregistered magnetic resonance images. It is therefore possible to routinely make use of anatomical information in PET reconstruction, provided appropriate methods are available. In this article, we review research efforts over the past 20 years to develop these methods. We discuss approaches based on the use of both Markov random field priors and joint information or entropy measures. The general framework for these methods is described, and their performance and longer-term potential and limitations are discussed.
    Seminars in nuclear medicine 01/2013; 43(1):30-44. · 3.96 Impact Factor
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    ABSTRACT: New aggressive therapeutic options have recently become available to treat inflammatory arthritis (IA) and rheumatoid arthritis in particular. These treatments not only control joint destruction, they may also aid in new bone formation at sites of eroded bone. Separation of non-responders from responders to these treatments, is critical, and is known to lead to reduced disease burden, toxicity, side-effects and overall cost. The bones of the wrist are early targets of IA and are known to show response to therapy early. In this paper, we develop a method to quantify point-wise erosive changes of wrist bones in IA patients undergoing treatment. The method employs 3D registration-based morphometric analysis. Our results indicate that the proposed method has potential to improve sensitivity to small, early changes in bone erosion status. This study has potential to provide new imaging biomarkers to be used in clinical trials evaluating the efficacy of new arthritis drugs.
    Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 01/2013;
  • J.P. Haldar, R.M. Leahy
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    ABSTRACT: This work provides a theoretical analysis of linear spherical deconvolution methods in diffusion MRI, building off of a theoretical framework that was previously developed for model-free linear transforms of the Fourier 2-sphere. It is demonstrated that linear spherical deconvolution methods have an equivalent representation as model-free linear transform methods. This perspective is used to study the characteristics of linear spherical deconvolution from the point of view of the diffusion propagator. Practical results are shown with experimental brain MRI data.
    Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on; 01/2013
  • Yu-Teng Chang, R.M. Leahy, D. Pantazis
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    ABSTRACT: Brain networks are often explored with graph theoretical approaches, and community structures identified using modularity-based partitions. Despite the popularity of these methods, the significance of the obtained subnetworks is largely unaddressed in the literature. We present two parametric methods to assess the statistical significance of network partitions, and therefore control against spurious subnetworks that may arise in random graphs, rather than self-organized brain networks. We evaluate these methods with simulated data and resting state fMRI data.
    Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on; 01/2013
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    ABSTRACT: PURPOSE: To develop an automatic registration-based segmentation algorithm for measuring abdominal adipose tissue depot volumes and organ fat fraction content from three-dimensional (3D) water-fat MRI data, and to evaluate its performance against manual segmentation. MATERIALS AND METHODS: Data were obtained from 11 subjects at two time points with intermediate repositioning, and from four subjects before and after a meal with repositioning. Imaging was performed on a 3 Tesla MRI, using the IDEAL chemical-shift water-fat pulse sequence. Adipose tissue (subcutaneous-SAT, visceral-VAT) and organs (liver, pancreas) were manually segmented twice for each scan by a single trained observer. Automated segmentations of each subject's second scan were generated using a nonrigid volume registration algorithm for water-fat MRI images that used a b-spline basis for deformation and minimized image dissimilarity after the deformation. Manual and automated segmentations were compared using Dice coefficients and linear regression of SAT and VAT volumes, organ volumes, and hepatic and pancreatic fat fractions (HFF, PFF). RESULTS: Manual segmentations from the 11 repositioned subjects exhibited strong repeatability and set performance benchmarks. The average Dice coefficients were 0.9747 (SAT), 0.9424 (VAT), 0.9404 (liver), and 0.8205 (pancreas); the linear correlation coefficients were 0.9994 (SAT volume), 0.9974 (VAT volume), 0.9885 (liver volume), 0.9782 (pancreas volume), 0.9996 (HFF), and 0.9660 (PFF). When comparing manual and automated segmentations, the average Dice coefficients were 0.9043 (SAT volume), 0.8235 (VAT), 0.8942 (liver), and 0.7168 (pancreas); the linear correlation coefficients were 0.9493 (SAT volume), 0.9982 (VAT volume), 0.9326 (liver volume), 0.8876 (pancreas volume), 0.9972 (HFF), and 0.8617 (PFF). In the four pre- and post-prandial subjects, the Dice coefficients were 0.9024 (SAT), 0.7781 (VAT), 0.8799 (liver), and 0.5179 (pancreas); the linear correlation coefficients were 0.9889, 0.9902 (SAT, and VAT volume), 0.9523 (liver volume), 0.8760 (pancreas volume), 0.9991 (HFF), and 0.6338 (PFF). CONCLUSION: Automated intra-subject registration-based segmentation is potentially suitable for the quantification of abdominal and organ fat and achieves comparable quantitative endpoints with respect to manual segmentation. J. Magn. Reson. Imaging 2012;. © 2012 Wiley Periodicals, Inc.
    Journal of Magnetic Resonance Imaging 09/2012; · 2.57 Impact Factor
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    ABSTRACT: Despite accumulating evidence of structural deficits in individuals with psychopathy, especially in frontal regions, our understanding of systems-level disturbances in cortical networks remains limited. We applied novel graph theory-based methods to assess information flow and connectivity based on cortical thickness measures in 55 individuals with psychopathy and 47 normal controls. Compared with controls, the psychopathy group showed significantly altered interregional connectivity patterns. Furthermore, bilateral superior frontal cortices in the frontal network were identified as information flow control hubs in the psychopathy group in contrast to bilateral inferior frontal and medial orbitofrontal cortices as network hubs of the controls. Frontal information flow and connectivity may have a significant role in the neuropathology of psychopathy.
    The British journal of psychiatry: the journal of mental science 08/2012; · 6.62 Impact Factor
  • Anand A. Joshi, David W. Shattuck, Richard M. Leahy
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    ABSTRACT: Registration and delineation of anatomical features in MRI of the human brain play an important role in the investigation of brain development and disease. Accurate, automatic and computationally efficient cortical surface registration and delineation of surface-based landmarks, including regions of interest (ROIs) and sulcal curves (sulci), remain challenging problems due to substantial variation in the shapes of these features across populations. We present a method that performs a fast and accurate registration, labeling and sulcal delineation of brain images. The new method presented in this paper uses a multiresolution, curvature based approach to perform a registration of a subject brain surface model to a delineated atlas surface model; the atlas ROIs and sulcal curves are then mapped to the subject brain surface. A geodesic curvature flow on the cortical surface is then used to refine the locations of the sulcal curves sulci and label boundaries further, such that they follow the true sulcal fundi more closely. The flow is formulated using a level set based method on the cortical surface, which represents the curves as zero level sets. We also incorporate a curvature based weighting that drives the curves to the bottoms of the sulcal valleys in the cortical folds. Finally, we validate our new approach by comparing sets of automatically delineated sulcal curves it produced to corresponding sets of manually delineated sulcal curves. Our results indicate that the proposed method is able to find these landmarks accurately.
    Proceedings of the 5th international conference on Biomedical Image Registration; 07/2012

Publication Stats

8k Citations
477.67 Total Impact Points

Institutions

  • 1985–2014
    • University of Southern California
      • • Department of Electrical Engineering
      • • Department of Radiology
      Los Angeles, California, United States
  • 2010
    • Florida Atlantic University
      Boca Raton, Florida, United States
  • 1991–2010
    • University of California, Los Angeles
      • • Laboratory of Neuro Imaging
      • • Department of Molecular and Medical Pharmacology
      • • Department of Medicine
      • • Department of Electrical Engineering
      Los Angeles, CA, United States
  • 2009
    • Cleveland Clinic
      Cleveland, Ohio, United States
  • 2003–2009
    • University of California, Davis
      • Department of Biomedical Engineering
      Davis, CA, United States
  • 2004–2008
    • French National Centre for Scientific Research
      Lutetia Parisorum, Île-de-France, France
    • Washington University in St. Louis
      • Department of Radiology
      San Luis, Missouri, United States
  • 2006
    • The University of Tokushima
      • Institute of Technology and Science
      Tokusima, Tokushima, Japan
  • 2005
    • University of California, San Francisco
      San Francisco, California, United States
  • 1993–2003
    • Los Alamos National Laboratory
      Los Alamos, California, United States
  • 2002
    • University of Michigan
      • Department of Biostatistics
      Ann Arbor, MI, United States
  • 1992
    • TRW Automotive
      Livonia, Michigan, United States
  • 1984
    • Newcastle University
      • School of Electrical and Electronic Engineering
      Newcastle-on-Tyne, England, United Kingdom