Navid Shiee

National Institutes of Health, Maryland, United States

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Publications (25)59.96 Total impact

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    ABSTRACT: This study examines the spatial distribution of microhemorrhages defined using susceptibility weighted images (SWI) in 46 patients with Traumatic Brain Injury (TBI) and applying region of interest (ROI) analysis using a brain atlas. SWI and 3D T1-weighted images were acquired on a 3T clinical Siemens scanner. A neuroradiologist reviewed all SWI images and manually labeled all identified microhemorrhages. To characterize the spatial distribution of microhemorrhages in standard Montreal Neurological Institute (MNI) space, the T1-weighted images were nonlinearly registered to the MNI template. This transformation was then applied to the co-registered SWI images and to the microhemorrhage coordinates. The frequencies of microhemorrhages were determined in major structures from ROIs defined in the digital Talairach brain atlas and in white matter tracts defined using a diffusion tensor imaging atlas. A total of 629 microhemorrhages were found with an average of 22±42 (range=1-179) in the 24 positive TBI patients. Microhemorrhages mostly congregated around the periphery of the brain and were fairly symmetrically distributed, although a number were found in the corpus callosum. From Talairach ROI analysis, microhemorrhages were most prevalent in the frontal lobes (65.1%). Restricting the analysis to WM tracts, microhemorrhages were primarily found in the corpus callosum (56.9%).
    02/2014;
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    ABSTRACT: Cortical atrophy has been reported in a number of diseases, such as multiple sclerosis and Alzheimer's disease, that are also associated with white matter (WM) lesions. However, most cortical reconstruction techniques do not account for these pathologies, thereby requiring additional processing to correct for the effect of WM lesions. In this work, we introduce CRUISE+, an automated process for cortical reconstruction from magnetic resonance brain images with WM lesions. The process extends previously well validated methods to allow for multichannel input images and to accommodate for the presence of WM lesions. We provide new validation data and tools for measuring the accuracy of cortical reconstruction methods on healthy brains as well as brains with multiple sclerosis lesions. Using this data, we validate the accuracy of CRUISE+ and compare it to another state-of-the-art cortical reconstruction tool. Our results demonstrate that CRUISE+ has superior performance in the cortical regions near WM lesions, and similar performance in other regions. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.
    Human Brain Mapping 12/2013; · 6.88 Impact Factor
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    ABSTRACT: Daclizumab is a monoclonal antibody that reduces inflammation in multiple sclerosis (MS). Through a retrospective analysis, our objective was to determine whether daclizumab treatment reduces the rate of brain structure atrophy in comparison to a mixture of other disease-modifying therapies (mainly different interferon β preparations). We analyzed MRI examinations (1332 scans from 70 MS cases) obtained between 2000 and 2011 in a single center and processed with an automated brain segmentation method. We used mixed-effects multivariable linear regression models to determine whether a median of 4.3 years of daclizumab therapy in 26 patients altered rates of brain-volume change, controlling for variations in MRI protocol. The control group consisted of 44 patients not treated with daclizumab. We found that supratentorial brain volume declined by 5.17 ml per year (95% confidence limits: 3.58-6.77) off daclizumab therapy. On daclizumab, the annual rate of volume loss decreased to 3.72 ml (p=0.01). The rate of ventricular enlargement decreased from 1.26 to 0.42 ml per year (p<0.001). Focused analysis suggests that reduction in gray matter atrophy rate most likely underlies these results. In summary, in this retrospective analysis, daclizumab therapy substantially decreased the rate of brain atrophy in relapsing-remitting MS in comparison to other disease-modifying therapies, predominantly interferon β.
    Multiple sclerosis and related disorders. 04/2013; 2(2):133-140.
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    ABSTRACT: OBJECTIVE To determine the relationships between conventional and segmentation-derived optical coherence tomography (OCT) retinal layer thickness measures with intracranial volume (a surrogate of head size) and brain substructure volumes in multiple sclerosis (MS). DESIGN Cross-sectional study. SETTING Johns Hopkins University, Baltimore, Maryland. PARTICIPANTS A total of 84 patients with MS and 24 healthy control subjects. MAIN OUTCOME MEASURES High-definition spectral-domain OCT conventional and automated segmentation-derived discrete retinal layer thicknesses and 3-T magnetic resonance imaging brain substructure volumes. RESULTS Peripapillary retinal nerve fiber layer as well as composite ganglion cell layer + inner plexiform layer thicknesses in the eyes of patients with MS without a history of optic neuritis were associated with cortical gray matter (P = .01 and P = .04, respectively) and caudate (P = .04 and P = .03, respectively) volumes. Inner nuclear layer thickness, also in eyes without a history of optic neuritis, was associated with fluid-attenuated inversion recovery lesion volume (P = .007) and inversely associated with normal-appearing white matter volume (P = .005) in relapsing-remitting MS. As intracranial volume was found to be related with several of the OCT measures in patients with MS and healthy control subjects and is already known to be associated with brain substructure volumes, all OCT-brain substructure relationships were adjusted for intracranial volume. CONCLUSIONS Retinal measures reflect global central nervous system pathology in multiple sclerosis, with thicknesses of discrete retinal layers each appearing to be associated with distinct central nervous system processes. Moreover, OCT measures appear to correlate with intracranial volume in patients with MS and healthy control subjects, an important unexpected factor unaccounted for in prior studies examining the relationships between peripapillary retinal nerve fiber layer thickness and brain substructure volumes.
    JAMA neurology. 01/2013; 70(1):34-43.
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    ABSTRACT: This paper proposes a longitudinal intensity normalization algorithm for T1-weighted magnetic resonance images of human brains in the presence of multiple sclerosis lesions, aiming towards stable and consistent longitudinal segmentations. Unlike previous longitudinal segmentation methods, we propose a 4D intensity normalization that can be used as a preprocessing step to any segmentation method. The variability in intensities arising from the relapsing and remitting nature of the multiple sclerosis lesions is modeled into an otherwise smooth intensity transform based on first order autoregressive models, resulting in smooth changes in segmentation statistics of normal tissues, while keeping the lesion information unaffected. We validated our method on both simulated and real longitudinal normal subjects and on multiple sclerosis subjects.
    Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on; 01/2013
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    ABSTRACT: Longitudinal magnetic resonance (MR) images of the same subject often vary significantly in their overall contrast. Intensity standardization aims to minimize the inter-scan intensity variations by transforming the intensities into a standard gray scale, but true anatomical changes over time are often masked out. We propose an intensity standardization method based on four dimensional Fuzzy C-means (FCM) clustering over longitudinal images. Assuming that the images in the longitudinal series of the same subject have been spatially aligned, our method tries to find for each image a piecewise linear intensity transformation function that minimizes the 4D energy function. The performance of our method is evaluated through the volume measurements of the tissue segmentation. Results show that our method can minimize the scanner induced intensity variation among longitudinal images, while preserving intensity variations caused by anatomical changes.
    Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on; 01/2013
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    ABSTRACT: Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.
    NeuroImage : clinical. 01/2013; 2:402-13.
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    ABSTRACT: In the study of brain disease processes and aging, longitudinal imaging studies are becoming increasingly commonplace. Indeed, there are hundreds of studies collecting multi-sequence multi-modality brain images at multiple time points on hundreds of subjects over many years. A fundamental problem in this context is how to classify subjects according to their baseline and longitudinal changes in the presence of strong spatio-temporal biological and technological measurement error. We propose a fast and scalable clustering approach by defining a metric between latent trajectories of brain images. Methods were motivated by and applied to a longitudinal voxel-based morphometry study of multiple sclerosis. Results indicate that there are two distinct patterns of ventricular change that are associated with clinical outcomes.
    Pattern Recognition in Neuroimaging (PRNI), 2013 International Workshop on; 01/2013
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    ABSTRACT: OBJECTIVE To determine the relationships between conventional and segmentation-derived optical coherence tomography (OCT) retinal layer thickness measures with intracranial volume (a surrogate of head size) and brain substructure volumes in multiple sclerosis (MS). DESIGN Cross-sectional study. SETTING Johns Hopkins University, Baltimore, Maryland. PARTICIPANTS A total of 84 patients with MS and 24 healthy control subjects. MAIN OUTCOME MEASURES High-definition spectral-domain OCT conventional and automated segmentation-derived discrete retinal layer thicknesses and 3-T magnetic resonance imaging brain substructure volumes. RESULTS Peripapillary retinal nerve fiber layer as well as composite ganglion cell layer + inner plexiform layer thicknesses in the eyes of patients with MS without a history of optic neuritis were associated with cortical gray matter (P = .01 and P = .04, respectively) and caudate (P = .04 and P = .03, respectively) volumes. Inner nuclear layer thickness, also in eyes without a history of optic neuritis, was associated with fluid-attenuated inversion recovery lesion volume (P = .007) and inversely associated with normal-appearing white matter volume (P = .005) in relapsing-remitting MS. As intracranial volume was found to be related with several of the OCT measures in patients with MS and healthy control subjects and is already known to be associated with brain substructure volumes, all OCT-brain substructure relationships were adjusted for intracranial volume. CONCLUSIONS Retinal measures reflect global central nervous system pathology in multiple sclerosis, with thicknesses of discrete retinal layers each appearing to be associated with distinct central nervous system processes. Moreover, OCT measures appear to correlate with intracranial volume in patients with MS and healthy control subjects, an important unexpected factor unaccounted for in prior studies examining the relationships between peripapillary retinal nerve fiber layer thickness and brain substructure volumes.
    Archives of neurology 10/2012; · 7.58 Impact Factor
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    ABSTRACT: Mapping brain structure in relation to neurological development, function, plasticity, and disease is widely considered to be one of the most essential challenges for opening new lines of neuro-scientific inquiry. Recent developments with MRI analysis of structural connectivity, anatomical brain segmentation, cortical surface parcellation, and functional imaging have yielded fantastic advances in our ability to probe the neurological structure-function relationship in vivo. To date, the image analysis efforts in each of these areas have typically focused on a single modality. Here, we extend the cortical reconstruction using implicit surface evolution (CRUISE) methodology to perform efficient, consistent, and topologically correct analyses in a natively multi-parametric manner. This effort combines and extends state-of-the-art techniques to simultaneously consider and analyze structural and diffusion information alongside quantitative and functional imaging data. Robust and consistent estimates of the cortical surface extraction, cortical labeling, diffusion-inferred contrasts, diffusion tractography, and subcortical parcellation are demonstrated in a scan-rescan paradigm. Accompanying this demonstration, we present a fully automated software system complete with validation data.
    Neuroinformatics 08/2012; · 3.14 Impact Factor
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    ABSTRACT: Although diffusion tensor imaging (DTI) and the magnetization transfer ratio (MTR) have been extensively studied in multiple sclerosis (MS), it is still unclear if they are more effective biomarkers of disability than conventional MRI. MRI scans were performed on 117 participants with MS in addition to 26 healthy volunteers. Mean values were obtained for DTI indices and MTR for supratentorial brain and three white matter tracts of interest. DTI and MTR values were tested for correlations with measures of atrophy and lesion volume and were compared with these more conventional indices for prediction of disability. All DTI and MTR values correlated to an equivalent degree with lesion volume and cerebral volume fraction (CVF). Thalamic volumes correlated with all indices in the optic radiations and with mean and perpendicular diffusivity in the corpus callosum. Nested model regression analysis demonstrated that, compared with CVF, DTI indices in the optic radiations were more strongly correlated with Expanded Disability Status Scale and were also more strongly correlated than both CVF and lesion volume with low-contrast visual acuity. Abnormalities in DTI and MTR are equivalently linked with brain atrophy and inflammatory lesion burden, suggesting that for practical purposes they are markers of multiple aspects of MS pathology. Our findings that some DTI and MTR indices are more strongly linked with disability than conventional MRI measures justifies their potential use as targeted, functional system-specific clinical trial outcomes in MS.
    Journal of Neurology 08/2012; · 3.58 Impact Factor
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    ABSTRACT: Brain atrophy is a well-accepted imaging biomarker of multiple sclerosis (MS) that partially correlates with both physical disability and cognitive impairment. Based on MRI scans of 60 MS cases and 37 healthy volunteers, we measured the volumes of white matter (WM) lesions, cortical gray matter (GM), cerebral WM, caudate nucleus, putamen, thalamus, ventricles, and brainstem using a validated and completely automated segmentation method. We correlated these volumes with the Expanded Disability Status Scale (EDSS), MS Severity Scale (MSSS), MS Functional Composite (MSFC), and quantitative measures of ankle strength and toe sensation. Normalized volumes of both cortical and subcortical GM structures were abnormally low in the MS group, whereas no abnormality was found in the volume of the cerebral WM. High physical disability was associated with low cerebral WM, thalamus, and brainstem volumes (partial correlation coefficients ~0.3-0.4) but not with low cortical GM volume. Thalamus volumes were inversely correlated with lesion load (r = -0.36, p<0.005). The GM is atrophic in MS. Although lower WM volume is associated with greater disability, as might be expected, WM volume was on average in the normal range. This paradoxical result might be explained by the presence of coexisting pathological processes, such as tissue damage and repair, that cause both atrophy and hypertrophy and that underlie the observed disability.
    PLoS ONE 01/2012; 7(5):e37049. · 3.73 Impact Factor
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    ABSTRACT: Activated microglia are thought to be an important contributor to tissue damage in multiple sclerosis (MS). The level of microglial activation can be measured non-invasively using [(11)C]-R-PK11195, a radiopharmaceutical for positron emission tomography (PET). Prior studies have identified abnormalities in the level of [(11)C]-R-PK11195 uptake in patients with MS, but treatment effects have not been evaluated. Nine previously untreated relapsing-remitting MS patients underwent PET and magnetic resonance imaging of the brain at baseline and after 1 year of treatment with glatiramer acetate. Parametric maps of [(11)C]-R-PK11195 uptake were obtained for baseline and post-treatment PET scans, and the change in [(11)C]-R-PK11195 uptake pre- to post-treatment was evaluated across the whole brain. Region-of-interest analysis was also applied to selected subregions. Whole brain [(11)C]-R-PK11195 binding potential per unit volume decreased 3.17% (95% CI: -0.74, -5.53%) between baseline and 1 year (p = 0.018). A significant decrease was noted in cortical gray matter and cerebral white matter, and a trend towards decreased uptake was seen in the putamen and thalamus. The results are consistent with a reduction in inflammation due to treatment with glatiramer acetate, though a larger controlled study would be required to prove that association. Future research will focus on whether the level of baseline microglial activation predicts future tissue damage in MS and whether [(11)C]-R-PK11195 uptake in cortical gray matter correlates with cortical lesion load.
    Journal of Neurology 12/2011; 259(6):1199-205. · 3.58 Impact Factor
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    ABSTRACT: Diffusion-weighted images of the human brain are acquired more and more routinely in clinical research settings, yet segmenting and labeling white matter tracts in these images is still challenging. We present in this paper a fully automated method to extract many anatomical tracts at once on diffusion tensor images, based on a Markov random field model and anatomical priors. The approach provides a direct voxel labeling, models explicitly fiber crossings and can handle white matter lesions. Experiments on simulations and repeatability studies show robustness to noise and reproducibility of the algorithm, which has been made publicly available.
    NeuroImage 06/2011; 58(2):458-68. · 6.25 Impact Factor
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    ABSTRACT: Topological concepts are critically important in the computation of anatomical representations, establishing correspondences between structures, and quantifying shape differences in medical image analysis. Early work in the preservation of an object’s topology to brain imaging involved generating digital reconstructions of the cerebral cortex, which is known to have a fixed topology. Such reconstructions facilitate the analysis and visualization of functional activity and allow group comparisons of cortical geometry in a standardized space. These applications are possible because topology preservation guarantees a mapping equivalence between specific shapes, such as between two cortical surfaces or between the cortex and a sphere. More recent work has shown that enforcing topology-preserving segmentations can maintain relationships between multiple objects and are robust to noise without biasing shape. In this chapter, we present the core principles of digital topology and describe their use in several fundamental tools for topology-preserving image analysis. We further demonstrate how these principles and tools can be applied to derive brain segmentation and registration algorithms that explicitly maintain multi-object topology. The advantage of these algorithms is that structures are reconstructed in a manner consistent with the underlying anatomy, thereby improving accuracy and readily enabling diffeomorphic shape analyses.
    05/2011: pages 339-375;
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    ABSTRACT: Diffusion tensor imaging provides rich information about human brain connectivity in vivo, yet most current methods for fiber tractography or tract segmentation do not address white matter pathologies such as multiple sclerosis lesions, which can alter the diffusion tensor characteristics. We study here the effects of MS lesions on estimated diffusion tensors and how they affect the processing of fibers and tracts. An efficient correction algorithm is proposed to compensate for lesion areas in two different approaches to fiber tracking and tract segmentation. Application of the algorithm to real data acquired from MS patients demonstrates improved fiber tracking through lesion regions. the diffusion properties of WM changes in the lesion areas, many fibers that should pass through WM lesions cannot be extracted by these algorithms. Probabilistic tractography algorithms which have been introduced more recently [6, 7], are more robust to the presence of lesions. However, they still can assign low connectivity probabilities in the lesion areas, confounding the study of connectivity in the presence of disease. Atlas registration approaches [8] avoid the problems of diffusing through the lesions, but they rely on accurate registration between subject and the atlas which is a challenging task in MS studies, due to both intensity abnormalities within the lesion areas and pronounced brain atrophy. Statistical mapping approaches based on WM skeletonization [9] are also disturbed by the presence of lesions as they rely primarily on anisotropy for normalization. In this work, we more thoroughly examine the effects of MS lesions on the properties of the diffusion tensor, namely its eigenvalues and its principal direction, which we refer to as the tensor direction. Although both radial and parallel diffusivities have been studied in MS extensively [10], the relative change of tensor directions has not been studied to a great extent. We first propose a way to quantify these changes. Then, by employing lesion masks computed from structural MR images, we provide preprocessing steps for diffusion data in MS lesions for two previously developed algorithms for tractography [3] and direct tract segmentation [11]. We demonstrate significant improvement in the performance of both methods in MS lesions using this step. To our knowledge, this is the first work that characterizes the tensor directions inside MS lesions and presents an algorithm for improved DTI processing in their presence.
    Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
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    ABSTRACT: To estimate longitudinal changes in a quantitative whole-brain and tract-specific MRI study of multiple sclerosis (MS), with the intent of assessing the feasibility of this approach in clinical trials. A total of 78 individuals with MS underwent a median of 3 scans over 2 years. Diffusion tensor imaging indices, magnetization transfer ratio, and T2 relaxation time were analyzed in supratentorial brain, corpus callosum, optic radiations, and corticospinal tracts by atlas-based tractography. Linear mixed-effect models estimated annualized rates of change for each index, and sample size estimates for potential clinical trials were determined. There were significant changes over time in fractional anisotropy and perpendicular diffusivity in the supratentorial brain and corpus callosum, mean diffusivity in the supratentorial brain, and magnetization transfer ratio in all areas studied. Changes were most rapid in the corpus callosum, where fractional anisotropy decreased 1.7% per year, perpendicular diffusivity increased 1.2% per year, and magnetization transfer ratio decreased 0.9% per year. The T2 relaxation time changed more rapidly than diffusion tensor imaging indices and magnetization transfer ratio but had higher within-participant variability. Magnetization transfer ratio in the corpus callosum and supratentorial brain declined at an accelerated rate in progressive MS relative to relapsing-remitting MS. Power analysis yielded reasonable sample sizes (on the order of 40 participants per arm or fewer) for 1- or 2-year trials. Longitudinal changes in whole-brain and tract-specific diffusion tensor imaging indices and magnetization transfer ratio can be reliably quantified, suggesting that small clinical trials using these outcome measures are feasible.
    Neurology 01/2011; 76(2):179-86. · 8.25 Impact Factor
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    ABSTRACT: Segmentation of brain images often requires a statistical atlas for providing prior information about the spatial position of different structures. A major limitation of atlas-based segmentation algorithms is their deficiency in analyzing brains that have a large deviation from the population used in the construction of the atlas. We present an expectation-maximization framework based on a Dirichlet distribution to adapt a statistical atlas to the underlying subject. Our model combines anatomical priors with the subject's own anatomy, resulting in a subject specific atlas which we call an "adaptive atlas". The generation of this adaptive atlas does not require the subject to have an anatomy similar to that of the atlas population, nor does it rely on the availability of an ensemble of similar images. The proposed method shows a significant improvement over current segmentation approaches when applied to subjects with severe ventriculomegaly, where the anatomy deviates significantly from the atlas population. Furthermore, high levels of accuracy are maintained when the method is applied to subjects with healthy anatomy.
    Information processing in medical imaging: proceedings of the ... conference 01/2011; 22:1-12.
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    ABSTRACT: The magnetic resonance contrast of a neuroimaging data set has strong impact on the utility of the data in image analysis tasks, such as registration and segmentation. Lengthy acquisition times often prevent routine acquisition of multiple MR contrast images, and opportunities for detailed analysis using these data would seem to be irrevocably lost. This paper describes an example based approach which uses patch matching from a multiple contrast atlas with the intended goal of generating an alternate MR contrast image, thus effectively simulating alternative pulse sequences from one another. In this paper, we deal specifically with Fluid Attenuated Inversion Recovery (FLAIR) sequence generation from T1 and T2 pulse sequences. The applicability of this synthetic FLAIR for estimating white matter lesions segmentation is demonstrated.
    Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 06/2010; 2010:932-935.
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    ABSTRACT: The magnetic resonance contrast of a neuroimaging data set has strong impact on the utility of the data in image analysis tasks, such as registration and segmentation. Lengthy acquisition times often prevent routine acquisition of multiple MR contrast images, and opportunities for detailed analysis using these data would seem to be irrevocably lost. This paper describes an example based approach which uses patch matching from a multiple contrast atlas with the intended goal of generating an alternate MR contrast image, thus effectively simulating alternative pulse sequences from one another. In this paper, we deal specifically with Fluid Attenuated Inversion Recovery (FLAIR) sequence generation from T1 and T2 pulse sequences. The applicability of this synthetic FLAIR for estimating white matter lesions segmentation is demonstrated.
    Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on; 05/2010

Publication Stats

176 Citations
59.96 Total Impact Points

Institutions

  • 2013
    • National Institutes of Health
      Maryland, United States
  • 2009–2012
    • Johns Hopkins University
      • • Department of Neurology
      • • Department of Radiology
      Baltimore, MD, United States
  • 2011
    • Max Planck Institute for Human Cognitive and Brain Sciences
      • Department of Neurophysics
      Leipzig, Saxony, Germany