Dr. Yurui Gao Profile

Dr. Yurui Gao

Research Assistant Professor at Vanderbilt Univ Institute of Imaging Science

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Publications (11)

Proceedings Article | 2 April 2024 Presentation + Paper

Identification of functional white matter networks in BOLD fMRI

Alexa Eby, Lucas Remedios, Michael Kim, Muwei Li, Yurui Gao, John Gore, Kurt Schilling, Bennett Landman

Proceedings Volume 12926, 129260T (2024) https://doi.org/10.1117/12.3006231

KEYWORDS: White matter, Voxels, Functional magnetic resonance imaging, Brain, Visualization

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Proceedings Article | 3 April 2023 Presentation + Paper

Automatic preprocessing pipeline for white matter functional analyses of large-scale databases

Yurui Gao, Dylan Lawless, Muwei Li, Yu Zhao, Kurt Schilling, Lyuan Xu, Andrea Shafer, Lori Beason-Held, Susan Resnick, Baxter Rogers, Zhaohua Ding, Adam Anderson, Bennett Landman, John Gore

Proceedings Volume 12464, 124640U (2023) https://doi.org/10.1117/12.2653132

KEYWORDS: Functional magnetic resonance imaging, Matrices, Databases, White matter, Signal processing, Signal detection, Reliability, Quality control, Neuroimaging, Correlation coefficients

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Proceedings Article | 15 March 2019 Presentation + Paper

Quantitative assessment of dMRI-based dentate-rubro-thalamic tractography in squirrel monkey

Yurui Gao, Kurt Schilling, Iwona Stepniewska, Guozhen Luo, Bennett Landman, Hong Yu, Daniel Claassen, Benoit Dawant, Adam Anderson

Proceedings Volume 10953, 109530F (2019) https://doi.org/10.1117/12.2513113

KEYWORDS: Brain, Surgery, Diffusion magnetic resonance imaging, Neuroimaging, Diffusion, RGB color model, Photography, Visualization, Biomedical engineering, Radiology

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Proceedings Article | 15 March 2019 Presentation + Paper

Progressive degeneration of white matter functional connectivity in Alzheimer’s disease

Yurui Gao, Muwei Li, Zhongliang Zu, Baxter Rogers, Adam Anderson, Zhaohua Ding, John Gore

Proceedings Volume 10953, 109530C (2019) https://doi.org/10.1117/12.2512919

KEYWORDS: Alzheimer's disease, Functional magnetic resonance imaging, Neuroimaging, Diagnostics, Brain, Databases, Medical research, Magnetic resonance imaging, Blood, Image segmentation

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Proceedings Article | 15 March 2019 Presentation + Paper

Harmonizing 1.5T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators

Vishwesh Nath, Samuel Remedios, Prasanna Parvathaneni, Colin Hansen, Roza Bayrak, Camilo Bermudez, Justin Blaber, Kurt Schilling, Vaibhav Janve, Yurui Gao, Yuankai Huo, Ilwoo Lyu, Owen Williams, Susan Resnick, Lori Beason-Held, Baxter Rogers, Iwona Stepniewska, Adam Anderson, Bennett Landman

Proceedings Volume 10949, 109490O (2019) https://doi.org/10.1117/12.2512902

KEYWORDS: Scanners, Network architectures, Diffusion, Magnetic resonance imaging, Brain, Tissues, Diffusion tensor imaging

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Diffusion weighted magnetic resonance imaging (DW-MRI) is interpreted as a quantitative method that is sensitive to tissue microarchitecture at a millimeter scale. However, the sensitization is dependent on acquisition sequences (e.g., diffusion time, gradient strength, etc.) and susceptible to imaging artifacts. Hence, comparison of quantitative DW-MRI biomarkers across field strengths (including different scanners, hardware performance, and sequence design considerations) is a challenging area of research. We propose a novel method to estimate microstructure using DW-MRI that is robust to scanner difference between 1.5T and 3T imaging. We propose to use a null space deep network (NSDN) architecture to model DW-MRI signal as fiber orientation distributions (FOD) to represent tissue microstructure. The NSDN approach is consistent with histologically observed microstructure (on previously acquired ex vivo squirrel monkey dataset) and scan-rescan data. The contribution of this work is that we incorporate identical dual networks (IDN) to minimize the influence of scanner effects via scan-rescan data. Briefly, our estimator is trained on two datasets. First, a histology dataset was acquired on three squirrel monkeys with corresponding DW-MRI and confocal histology (512 independent voxels). Second, 37 control subjects from the Baltimore Longitudinal Study of Aging (67-95 y/o) were identified who had been scanned at 1.5T and 3T scanners (b-value of 700 s/mm² , voxel resolution at 2.2mm, 30-32 gradient volumes) with an average interval of 4 years (standard deviation 1.3 years). After image registration, we used paired white matter (WM) voxels for 17 subjects and 440 histology voxels for training and 20 subjects and 72 histology voxels for testing. We compare the proposed estimator with super-resolved constrained spherical deconvolution (CSD) and a previously presented regression deep neural network (DNN). NSDN outperformed CSD and DNN in angular correlation coefficient (ACC) 0.81 versus 0.28 and 0.46, mean squared error (MSE) 0.001 versus 0.003 and 0.03, and general fractional anisotropy (GFA) 0.05 versus 0.05 and 0.09. Further validation and evaluation with contemporaneous imaging are necessary, but the NSDN is promising avenue for building understanding of microarchitecture in a consistent and deviceindependent manner.

Proceedings Article | 12 March 2018 Presentation + Paper

Tests of clustering thalamic nuclei based on various dMRI models in the squirrel monkey brain

Yurui Gao, Kurt Schilling, Iwona Stepniewska, Junzhong Xu, Bennett Landman, Benoit Dawant, Adam Anderson

Proceedings Volume 10578, 1057805 (2018) https://doi.org/10.1117/12.2293879

KEYWORDS: Brain, Diffusion, Thalamus, Neuroimaging, Diffusion tensor imaging, Data modeling, Brain mapping, Magnetic resonance imaging, Machine learning

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Proceedings Article | 21 March 2016 Paper

A 3D high resolution ex vivo white matter atlas of the common squirrel monkey (saimiri sciureus) based on diffusion tensor imaging

Yurui Gao, Prasanna Parvathaneni, Kurt Schilling, Feng Wang, Iwona Stepniewska, Zhoubing Xu, Ann Choe, Zhaohua Ding, John Gore, Li min Chen, Bennett Landman, Adam Anderson

Proceedings Volume 9784, 97843K (2016) https://doi.org/10.1117/12.2217325

KEYWORDS: Diffusion tensor imaging, Magnetic resonance imaging, 3D image processing, Neuroimaging, Brain, Standards development, Image resolution, Biomedical optics, Structural imaging, Imaging systems, In vivo imaging, Diffusion, Binary data

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Proceedings Article | 2 July 2015 Open Access

Erratum: Integrating histology and MRI in the first digital brain of common squirrel monkey, Saimiri sciureus

Peizhen Sun, Prasanna Parvathaneni, Kurt Schilling, Yurui Gao, Vaibhav Janve, Adam Anderson, Bennett Landman

Proceedings Volume 9417, 941737 (2015) https://doi.org/10.1117/12.2206044

KEYWORDS: Magnetic resonance imaging, Brain, Sun, Medical imaging, Biomedical optics, Functional imaging, Current controlled current source

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Proceedings Article | 17 March 2015 Paper

Integrating histology and MRI in the first digital brain of common squirrel monkey, Saimiri sciureus

Peizhen Sun, Prasanna Parvathaneni, Kurt Schilling, Yurui Gao, Vaibhav Janve, Adam Anderson, Bennett Landman

Proceedings Volume 9417, 94171T (2015) https://doi.org/10.1117/12.2081443

KEYWORDS: Magnetic resonance imaging, Brain, Image resolution, Diffusion tensor imaging, Photography, Data acquisition, Neuroimaging, Image registration, In vivo imaging, Brain mapping

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Proceedings Article | 13 March 2014 Paper

A brain MRI atlas of the common squirrel monkey, Saimiri sciureus

Yurui Gao, Kurt Schilling, Shweta Khare, Swetasudha Panda, Ann Choe, Iwona Stepniewska, Xia Li, Zhoahua Ding, Adam Anderson, Bennett Landman

Proceedings Volume 9038, 90380C (2014) https://doi.org/10.1117/12.2043589

KEYWORDS: Brain, Magnetic resonance imaging, In vivo imaging, Neuroimaging, Diffusion tensor imaging, Diffusion, Image resolution, Data acquisition, Tissues, Structural imaging

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Proceedings Article | 29 March 2013 Paper

Integration of XNAT/PACS, DICOM, and research software for automated multi-modal image analysis

Yurui Gao, Scott Burns, Carolyn Lauzon, Andrew Fong, Terry James, Joel Lubar, Robert Thatcher, David Twillie, Michael Wirt, Marc Zola, Bret Logan, Adam Anderson, Bennett Landman

Proceedings Volume 8674, 867405 (2013) https://doi.org/10.1117/12.2007621

KEYWORDS: Traumatic brain injury, Single photon emission computed tomography, Magnetic resonance imaging, Diffusion tensor imaging, Diffusion weighted imaging, Data conversion, Electroencephalography, Computing systems, Image analysis, Data transmission

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Showing 5 of 11 publications

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