Dr. Vishwesh Nath Profile

Dr. Vishwesh Nath

Research Scientist

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Publications (13)

SPIE Journal Paper | 5 January 2024 Open Access

Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging

Tianyuan Yao, Francois Rheault, Leon Y. Cai, Vishwesh Nath, Zuhayr Asad, Nancy Newlin, Can Cui, Ruining Deng, Karthik Ramadass, Andrea Shafer, Susan Resnick, Kurt Schilling, Bennett A. Landman, Yuankai Huo

JMI, Vol. 11, Issue 01, 014005, (January 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.1.014005

KEYWORDS: Data modeling, Diffusion, Voxels, Education and training, Reproducibility, Modeling, 3D modeling, Brain, Silver, Performance modeling

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PurposeDiffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multisite DW-MRI datasets are being made available for multisite studies. However, measurement variabilities (e.g., inter- and intrasite variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods [e.g., constrained spherical deconvolution (CSD)] and learning-based methods (e.g., deep learning) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multisite and/or longitudinal diffusion studies.ApproachIn this paper, we propose a data-driven deep CSD method to explicitly constrain the scan–rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a three-dimensional volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test–retest group as well as the MASiVar dataset (with inter- and intrasite scan/rescan data). The Baltimore Longitudinal Study of Aging dataset is employed for external validation.ResultsFrom the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. By introducing the contrastive loss with scan/rescan data, the proposed method achieved a higher consistency while maintaining higher angular correlation coefficients with the CSD modeling. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers.ConclusionWe propose a deep CSD method to explicitly reduce the scan–rescan variabilities, so as to model a more reproducible and robust brain microstructure from repeated DW-MRI scans. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization problems in neuroimaging.

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Proceedings Article | 3 April 2023 Presentation + Paper

Deep constrained spherical deconvolution for robust harmonization

Tianyuan Yao, Francois Rheault, Leon Cai, Vishwesh Nath, Zuhayr Asad, Nancy Newlin, Can Cui, Ruining Deng, Karthik Ramadass, Kurt Schilling, Bennett Landman, Yuankai Huo

Proceedings Volume 12464, 124640W (2023) https://doi.org/10.1117/12.2654398

KEYWORDS: Diffusion, Voxels, Deep learning, Education and training, Data modeling, Spherical harmonics, Tolerancing, White matter, Spherical lenses, Reconstruction algorithms

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Proceedings Article | 4 April 2022 Presentation + Paper

Effective hyperparameter optimization with proxy data for multi-organ segmentation

Chen Shen, Holger Roth, Vishwesh Nath, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

Proceedings Volume 12032, 120320T (2022) https://doi.org/10.1117/12.2611422

KEYWORDS: Image segmentation, Data modeling, Network architectures, Convolution, Stomach, Spleen, Medical imaging, Computer aided design, Performance modeling, Optimization (mathematics)

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Proceedings Article | 10 March 2020 Presentation + Paper

Deep learning estimation of multi-tissue constrained spherical deconvolution with limited single shell DW-MRI

Vishwesh Nath, Sudhir Pathak, Kurt Schilling, Walt Schneider, Bennett Landman

Proceedings Volume 11313, 113130S (2020) https://doi.org/10.1117/12.2549455

KEYWORDS: Tissues, Medical research, Brain, Network architectures, Deconvolution, Magnetic resonance imaging, Diffusion, Diffusion tensor imaging, In vivo imaging

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Diffusion-weighted magnetic resonance imaging (DW-MRI) is the only non-invasive approach for estimation of intravoxel tissue microarchitecture and reconstruction of in vivo neural pathways for the human brain. With improvement in accelerated MRI acquisition technologies, DW-MRI protocols that make use of multiple levels of diffusion sensitization have gained popularity. A well-known advanced method for reconstruction of white matter microstructure that uses multishell data is multi-tissue constrained spherical deconvolution (MT-CSD). MT-CSD substantially improves the resolution of intra-voxel structure over the traditional single shell version, constrained spherical deconvolution (CSD). Herein, we explore the possibility of using deep learning on single shell data (using the b=1000 s/mm² from the Human Connectome Project (HCP)) to estimate the information content captured by 8^th order MT-CSD using the full three shell data (b=1000, 2000, and 3000 s/mm² from HCP). Briefly, we examine two network architectures: 1.) Sequential network of fully connected dense layers with a residual block in the middle (ResDNN), 2.) Patch based convolutional neural network with a residual block (ResCNN). For both networks an additional output block for estimation of voxel fraction was used with a modified loss function. Each approach was compared against the baseline of using MT-CSD on all data on 15 subjects from the HCP divided into 5 training, 2 validation, and 8 testing subjects with a total of 6.7 million voxels. The fiber orientation distribution function (fODF) can be recovered with high correlation (0.77 vs 0.74 and 0.65) and low root mean squared error ResCNN:0.0124, ResDNN:0.0168 and sCSD:0.0323 as compared to the ground truth of MT-CST, which was derived from the multi-shell DW-MRI acquisitions. The mean squared error between the MT-CSD estimates for white matter tissue fraction and for the predictions are ResCNN:0.0249 vs ResDNN:0.0264. We illustrate the applicability of high definition fiber tractography on a single testing subject with arcuate and corpus callosum Tractography. In summary, the proposed approach provides a promising framework to estimate MT-CSD with limited single shell data. Source code and models have been made publicly available.

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

Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury

Samuel Remedios, Snehashis Roy, Justin Blaber, Camilo Bermudez, Vishwesh Nath, Mayur Patel, John Butman, Bennett Landman, Dzung Pham

Proceedings Volume 10949, 109490A (2019) https://doi.org/10.1117/12.2511997

KEYWORDS: Data modeling, Image segmentation, Computed tomography, Traumatic brain injury, Brain, Neuroimaging, Machine learning, Neural networks

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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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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

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 | 24 February 2017 Presentation + Paper

Comparison of multi-fiber reproducibility of PAS-MRI and Q-ball with empirical multiple b-value HARDI

Vishwesh Nath, Kurt Schilling, Justin Blaber, Zhaohua Ding, Adam Anderson, Bennett Landman

Proceedings Volume 10133, 101330L (2017) https://doi.org/10.1117/12.2254736

KEYWORDS: Brain, Diffusion tensor imaging, Data modeling, Gold, Diffusion, Anisotropy, Spherical lenses, Magnetic resonance imaging, Data acquisition, Scanners

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Proceedings Article | 24 February 2017 Presentation + Paper

Effects of b-value and number of gradient directions on diffusion MRI measures obtained with Q-ball imaging

Kurt Schilling, Vishwesh Nath, Justin Blaber, Robert Harrigan, Zhaohua Ding, Adam Anderson, Bennett Landman

Proceedings Volume 10133, 101330N (2017) https://doi.org/10.1117/12.2254545

KEYWORDS: Diffusion, Diffusion magnetic resonance imaging, Diffusion tensor imaging, Magnetic resonance imaging, Gold, Tissues, Diffusion weighted imaging, Brain, Data acquisition, Spherical lenses

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Showing 5 of 13 publications

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