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DETROIT, MI, United States

Feng W.,Wayne State University | Utriainen D.,Wayne State University | Trifan G.,Magnetic Resonance Innovations, Inc. | Sethi S.,Magnetic Resonance Imaging Institute for Biomedical Research | And 3 more authors.
Reviews on Recent Clinical Trials | Year: 2012

Purpose: To study the blood flow through the internal jugular veins (IJVS) of the MS population. Materials and Methods: Two hundred MS patients and 14 normal volunteers were evaluated with magnetic resonance imaging (MRI) at 3T. Contrast-enhanced time-resolved 3D MR angiography and 2D time-of-flight imaging were performed to assess abnormalities in the extracranial vascular anatomy. Based on this assessment, the MS population was divided into subgroups of non-stenotic (NST), cervical 1 stenotic only (C1ST) and cervical 6 stenotic (C6ST) subjects. In this study, 2D phase contrast MR imaging was used to quantify blood flow through major veins and arteries in the neck and flow differences among the groups were analyzed. Results: Of the 200 MS patients, 87 (43.5%) belonged to the NST group, 50 (25%) belonged to the C1ST group and 63 (31.5%) belonged to the C6ST group. The total IJV flow normalized to the total arterial flow of the NST group was 75.12 ± 12.22%. This was significantly higher than that of the C1ST group, 63.93 ± 16.08% (p<0.0001), which in turn was significantly higher than that of the C6ST group, 52.13 ± 20.71% (p = 0.001). Seventy-nine percent of the stenotic groups had a normalized subdominant IJV flow of less than 20%, a combined IJV flow of less than 50% and/or a sub-dominant IJV flow vs. dominant IJV flow ratio of less than 1/3. Only 2% of the NST group had a combined IJV flow of less than 50%, compared to 35% of the stenotic groups. Conclusion: Blood flow through the IJVs was reduced in the MS population with stenoses compared to those without. © 2012 Bentham Science Publishers. Source


A method of generating a susceptibility map of an object utilizes a regularizing inverse function, oversampling k-space, removing external phase noise and rapid phase change effects, accounting for the known geometry of the object, and using modified SWI phase data to generate reasonable susceptibility maps and digital images therefrom, such as SWI images. The inventors refers to the inventive methods set forth herein as Susceptibility Weighted Imaging and Mapping (SWIM).


A method of generating a susceptibility map of an object utilizes a regularizing inverse function, oversampling k-space, removing external phase noise and rapid phase change effects, accounting for the known geometry of the object, and using modified SWI phase data to generate reasonable susceptibility maps and digital images therefrom, such as SWI images. The inventors refers to the inventive methods set forth herein as Susceptibility Weighted Imaging and Mapping (SWIM).


Grant
Agency: Department of Health and Human Services | Branch: | Program: STTR | Phase: Phase II | Award Amount: 999.99K | Year: 2013

DESCRIPTION (provided by applicant): There has been a huge increase in demand for comprehensive quantitative analysis of neurovascular imaging data produced in the clinical setting for diseases such as multiple sclerosis, traumatic brain injury, stroke anddementia. Our objective in this project is to design and develop advanced image processing software that can rapidly and accurately analyze such data. To achieve this objective, we propose a range of novel algorithms to process data from the following MRimaging sequences widely used in the aforementioned applications: time resolved 3D contrast enhanced MR angiography (CE-MRA) for the assessment of vascular anatomy, time resolved 2D phase contrast flow imaging (PC-MRI) for the evaluation of vascular hemodynamics, susceptibility weighted imaging (SWI) for quantifying iron deposition in the brain, and fluid attenuated inversion recovery (FLAIR) imaging for the detection of white matter hyperintensities (WMH) and lesions. A variety of tools will be designed and implemented to tackle these problems including: tissue similarity mapping and active shape models to segment the vasculature in both CE-MRA and PC-MRI images; automatic tissue segmentation in the basal ganglia and thalamus for a two-region of interest analysis for iron quantification with SWI; and finally adaptive approaches incorporating fuzzy C-means, shape factor analysis, compactness and fractional anisotropy to quantify lesions and WMHs. To exploit the advantages provided by different imaging sequences, co-registration algorithms will be used to improve segmentation of vessels between CE-MRA and PC-MRI, and between 3D T1 weighted imaging and SWI. Upon finishing this project, we expect a multi-fold increase in processing efficiency and a significant increase in accuracy will be achieved. The resulting software will not only help the growth of our company, but also improve the diagnosis and treatment of neurovascular diseases. PUBLIC HEALTH RELEVANCE The huge increase in demand for a more comprehensive and accurate analysis of the vast amount of clinical MR imaging data for neurovascular diseases such as multiple sclerosis, traumatic brain injury, stroke and dementia is the driving force for th development of more advanced image processing software in our company. In this project, we propose an integrated approach to develop a set of processing software for imaging sequences that target the assessment of both anatomy and function of the neurovasculature system. The results will lead to a better access to quantitative data about the brain's vasculature, flow, hemodynamics and iron content present in neurovascular diseases. The completion of this project will not only help the growth of our company by increasing processing throughput and accuracy, but also improve the diagnosis and treatment of patients with neurovascular disease.


Bai Y.,Zhengzhou University | Lin Y.,Zhengzhou University | Tian J.,CAS Institute of Automation | Shi D.,Zhengzhou University | And 8 more authors.
Radiology | Year: 2016

Purpose: To quantitatively compare the potential of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models and diffusion kurtosis imaging in the grading of gliomas. Materials and Methods: This study was approved by the local ethics committee, and written informed consent was obtained from all subjects. Both diffusion-weighted imaging and diffusion kurtosis imaging were performed in 69 patients with pathologically proven gliomas by using a 3-T magnetic resonance (MR) imaging unit. An isotropic apparent diffusion coefficient (ADC), true ADC, pseudo- ADC, and perfusion fraction were calculated from diffusionweighted images by using a biexponential model. A water molecular diffusion heterogeneity index and distributed diffusion coefficient were calculated from diffusion-weighted images by using a stretched exponential model. Mean diffusivity, fractional anisotropy, and mean kurtosis were calculated from diffusion kurtosis images. All values were compared between high-grade and low-grade gliomas by using a Mann-Whitney U test. Receiver operating characteristic and Spearman rank correlation analysis were used for statistical evaluations. Results: ADC, true ADC, perfusion fraction, water molecular diffusion heterogeneity index, distributed diffusion coefficient, and mean diffusivity values were significantly lower in high-grade gliomas than in low-grade gliomas (U = 109, 56, 129, 6, 206, and 229, respectively; P <05). Pseudo-ADC and mean kurtosis values were significantly higher in high-grade gliomas than in low-grade gliomas (U = 98 and 8, respectively; P <05). Both water molecular diffusion heterogeneity index (area under the receiver operating characteristic curve [AUC] = 0.993) and mean kurtosis (AUC = 0.991) had significantly greater AUC values than ADC (AUC = 0.866), mean diffusivity (AUC = 0.722), and fractional anisotropy (AUC = 0.500) in the differentiation of low-grade and high-grade gliomas (P <05). Conclusion: Water molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters. © RSNA, 2015. Source

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