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 and dementia. 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 MR imaging 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.
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.
|Krishnamurthy, Uday; Yadav, Brijesh K; Jella, Pavan K et al. (2017) Quantitative Flow Imaging in Human Umbilical Vessels In Utero Using Nongated 2D Phase Contrast MRI. J Magn Reson Imaging :|
|Neelavalli, Jaladhar; Krishnamurthy, Uday; Jella, Pavan K et al. (2016) Magnetic resonance angiography of fetal vasculature at 3.0 T. Eur Radiol 26:4570-4576|
|Pacurar, Emil E; Sethi, Sean K; Habib, Charbel et al. (2016) Database integration of protocol-specific neurological imaging datasets. Neuroimage 124:1220-4|
|Krishnamurthy, Uday; Szalai, Gabor; Shen, Yimin et al. (2016) Longitudinal Changes in Placental Magnetic Resonance Imaging Relaxation Parameter in Murine Pregnancy: Compartmental Analysis. Gynecol Obstet Invest 81:193-201|
|Yadav, Brijesh Kumar; Neelavalli, Jaladhar; Krishnamurthy, Uday et al. (2016) A longitudinal study of placental perfusion using dynamic contrast enhanced magnetic resonance imaging in murine pregnancy. Placenta 43:90-7|
|Krishnamurthy, Uday; Neelavalli, Jaladhar; Mody, Swati et al. (2015) MR imaging of the fetal brain at 1.5T and 3.0T field strengths: comparing specific absorption rate (SAR) and image quality. J Perinat Med 43:209-20|
|Jiang, Jing; Kokeny, Paul; Ying, Wang et al. (2015) Quantifying errors in flow measurement using phase contrast magnetic resonance imaging: comparison of several boundary detection methods. Magn Reson Imaging 33:185-93|
|Liu, Manju; Xu, Haibo; Wang, Yuhui et al. (2015) Patterns of chronic venous insufficiency in the dural sinuses and extracranial draining veins and their relationship with white matter hyperintensities for patients with Parkinson's disease. J Vasc Surg 61:1511-20.e1|
|Zhong, Yi; Utriainen, David; Wang, Ying et al. (2014) Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR. Int J Biomed Imaging 2014:239123|
|Neelavalli, Jaladhar; Jella, Pavan Kumar; Krishnamurthy, Uday et al. (2014) Measuring venous blood oxygenation in fetal brain using susceptibility-weighted imaging. J Magn Reson Imaging 39:998-1006|
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