Ms. Prasanna Parvathaneni Profile

Prasanna Parvathaneni

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Publications (12)

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 | 15 March 2019 Presentation + Paper

Coronary calcium detection using 3D attention identical dual deep network based on weakly supervised learning

Yuankai Huo, James Terry, Jiachen Wang, Vishwesh Nath, Camilo Bermudez, Shunxing Bao, Prasanna Parvathaneni, J. Jeffery Carr, Bennett Landman

Proceedings Volume 10949, 1094917 (2019) https://doi.org/10.1117/12.2512541

KEYWORDS: Calcium, Computed tomography, Arteries, Machine learning, Image segmentation, Lung, Chest, 3D image processing, Medical imaging, Visualization

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Proceedings Article | 1 March 2019 Paper

Consideration of cerebrospinal fluid intensity variation in diffusion weighted MRI

Colin Hansen, Vishwesh Nath, Allison Hainline, Kurt Schilling, Prasanna Parvathaneni, Roza Bayrak, Justin Blaber, Owen Williams, Susan Resnick, Lori Beason-Held, Okan Irfanoglu, Carlo Pierpaoli, Adam Anderson, Baxter Rogers, Bennett Landman

Proceedings Volume 10948, 109482G (2019) https://doi.org/10.1117/12.2512949

KEYWORDS: Diffusion, Brain, Statistical modeling, Magnetic resonance imaging, Neuroimaging, Image segmentation, Visualization, Data modeling, Error analysis, Computer science

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Proceedings Article | 6 March 2018 Presentation + Paper

A data colocation grid framework for big data medical image processing: backend design

Shunxing Bao, Yuankai Huo, Prasanna Parvathaneni, Andrew Plassard, Camilo Bermudez, Yuang Yao, Ilwoo Lyu, Aniruddha Gokhale, Bennett Landman

Proceedings Volume 10579, 105790A (2018) https://doi.org/10.1117/12.2293694

KEYWORDS: Image processing, Medical imaging, Data storage, Statistical analysis, Data processing, Image retrieval, Brain, Statistical modeling, Interfaces

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When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework’s performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop and HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available.

SPIE Journal Paper | 6 March 2018

Empirical estimation of intravoxel structure with persistent angular structure and Q-ball models of diffusion weighted MRI

Vishwesh Nath, Kurt Schilling, Prasanna Parvathaneni, Justin Blaber, Allison Hainline, Zhaohua Ding, Adam Anderson, Bennett Landman

JMI, Vol. 5, Issue 01, 014005, (March 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.014005

KEYWORDS: Diffusion, Gold, Signal to noise ratio, Data modeling, Magnetic resonance imaging, Biological research, Brain, Spherical lenses, In vivo imaging, Data acquisition

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Proceedings Article | 2 March 2018 Presentation + Paper

Constructing statistically unbiased cortical surface templates using feature-space covariance

Prasanna Parvathaneni, Ilwoo Lyu, Yuankai Huo, Justin Blaber, Allison Hainline, Hakmook Kang, Neil Woodward, Bennett Landman

Proceedings Volume 10574, 1057406 (2018) https://doi.org/10.1117/12.2293641

KEYWORDS: Image segmentation, Distance measurement, Neuroimaging, Brain, Natural surfaces, Data modeling, Optical spheres, Electrical engineering, Computer science, Psychiatry

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Proceedings Article | 2 March 2018 Paper

Improved stability of whole brain surface parcellation with multi-atlas segmentation

Yuankai Huo, Shunxing Bao, Prasanna Parvathaneni, Bennett Landman

Proceedings Volume 10574, 1057438 (2018) https://doi.org/10.1117/12.2281509

KEYWORDS: Brain, Image segmentation, Volume rendering, Magnetic resonance imaging, Neuroimaging, Natural surfaces, Brain mapping, Baryon acoustic oscillations, Electrical engineering, Computer science

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Proceedings Article | 2 March 2018 Presentation + Paper

Evaluation of inter-site bias and variance in diffusion-weighted MRI

Allison Hainline, Vishwesh Nath, Prasanna Parvathaneni, Justin Blaber, Baxter Rogers, Allen Newton, Jeffrey Luci, Heidi Edmonson, Hakmook Kang, Bennett Landman

Proceedings Volume 10574, 1057413 (2018) https://doi.org/10.1117/12.2293735

KEYWORDS: Data acquisition, Diffusion tensor imaging, Data modeling, Error analysis, Magnetic resonance imaging, Diffusion weighted imaging, Scanners, Statistical analysis, Visualization, Process modeling

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Proceedings Article | 2 March 2018 Presentation + Paper

SHARD: spherical harmonic-based robust outlier detection for HARDI methods

Vishwesh Nath, Kurt Schilling, Allison Hainline, Prasanna Parvathaneni, Justin Blaber, Ilwoo Lyu, Adam Anderson, Hakmook Kang, Allen Newton, Baxter Rogers, Bennett Landman

Proceedings Volume 10574, 105740X (2018) https://doi.org/10.1117/12.2293727

KEYWORDS: Data modeling, Motion models, Diffusion, Brain, Diffusion tensor imaging, Signal detection, Head, Visualization, Brain mapping

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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

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

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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Showing 5 of 12 publications

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