Dr. Kurt G. Schilling Profile

Dr. Kurt G. Schilling

Grad Student at Vanderbilt Univ Institute of Imaging Science

SPIE Involvement:

Author

Publications (34)

Proceedings Article | 2 April 2024 Presentation + Paper

Learning-based free-water correction using single-shell diffusion MRI

Tianyuan Yao, Derek Archer, Praitayini Kanakaraj, Nancy Newlin, Shunxing Bao, Daniel Moyer, Kurt Schilling, Bennett Landman, Yuankai Huo

Proceedings Volume 12926, 1292607 (2024) https://doi.org/10.1117/12.3006901

KEYWORDS: Data modeling, Deep learning, Diffusion magnetic resonance imaging, White matter

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Proceedings Article | 2 April 2024 Poster + Paper

Evaluation of mean shift, ComBat, and CycleGAN for harmonizing brain connectivity matrices across sites

Hanliang Xu, Nancy Newlin, Michael Kim, Chenyu Gao, Praitayini Kanakaraj, Aravind Krishnan, Lucas Remedios, Nazirah Mohd Khairi, Kimberly Pechman, Derek Archer, Timothy Hohman, Angela Jefferson, Ivana Išgum, Yuankai Huo, Daniel Moyer, Kurt Schilling, Bennett Landman

Proceedings Volume 12926, 129261X (2024) https://doi.org/10.1117/12.3005563

KEYWORDS: Matrices, Brain, Scanners, Alzheimer disease, Biological research, Anatomy

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Proceedings Article | 2 April 2024 Poster + Paper

Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI

Tian Yu, Yunhe Li, Michael Kim, Chenyu Gao, Qi Yang, Leon Cai, Susan Resnick, Lori Beason-Held, Daniel Moyer, Kurt Schilling, Bennett Landman

Proceedings Volume 12926, 129262B (2024) https://doi.org/10.1117/12.3006286

KEYWORDS: Diffusion, Magnetic resonance imaging, Anatomy, Brain, White matter

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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 | 2 April 2024 Poster + Paper

Predicting age from white matter diffusivity with residual learning

Chenyu Gao, Michael Kim, Ho Hin Lee, Qi Yang, Nazirah Mohd Khairi, Praitayini Kanakaraj, Nancy Newlin, Derek Archer, Angela Jefferson, Warren Taylor, Brian Boyd, Lori Beason-Held, Susan Resnick, Yuankai Huo, Katherine Van Schaik, Kurt Schilling, Daniel Moyer, Ivana Išgum, Bennett Landman

Proceedings Volume 12926, 129262I (2024) https://doi.org/10.1117/12.3006525

KEYWORDS: Brain, Diffusion tensor imaging, Data modeling, White matter, Neuroimaging, Deep learning, Alzheimer disease, Convolutional neural networks, Computer vision technology, Image registration

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Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI’s advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are nonlinearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Proceedings Article | 2 April 2024 Presentation + Paper

Assessment of subject head motion in diffusion MRI

Ema Topolnjak, Chenyu Gao, Lori Beason-Held, Susan Resnick, Kurt Schilling, Bennett Landman

Proceedings Volume 12926, 129261B (2024) https://doi.org/10.1117/12.3006633

KEYWORDS: Head, Diffusion weighted imaging, Motion analysis, Magnetic resonance imaging, Brain, Diffusion magnetic resonance imaging, Neuroimaging, Motion measurement

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Proceedings Article | 1 April 2024 Poster + Paper

Nonlinear gradient field estimation in diffusion MRI tensor simulation

Praitayini Kanakaraj, Tianyuan Yao, Nancy Newlin, Leon Cai, Kurt Schilling, Baxter Rogers, Adam Anderson, Daniel Moyer, Bennett Landman

Proceedings Volume 12925, 1292549 (2024) https://doi.org/10.1117/12.3005364

KEYWORDS: Diffusion, Signal to noise ratio, Calibration, Scanners, Matrices, Statistical analysis, Distortion, Diffusion magnetic resonance imaging, Solids, Magnetic resonance imaging

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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

Harmonization of repetition time and scanner effects on estimates of brain hemodynamic response function

Lucie Dole, Kurt Schilling, Hakmook Kang, John Gore, Bennett Landman

Proceedings Volume 12464, 124640X (2023) https://doi.org/10.1117/12.2653903

KEYWORDS: Scanners, Hemodynamics, Neuroimaging, Functional magnetic resonance imaging, Brain, Data modeling, Alzheimer's disease, White matter

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Proceedings Article | 3 April 2023 Poster + Paper

Mapping the impact of approximate gradient nonlinearity fields correction on tractography

Praitayini Kanakaraj, Francois Rheault, Leon Cai, Nancy Newlin, Fang-Cheng Yeh, Baxter Rogers, Kurt Schilling, Bennett Landman

Proceedings Volume 12464, 1246427 (2023) https://doi.org/10.1117/12.2653884

KEYWORDS: Diffusion, Diffusion tensor imaging, Scanners, Reproducibility, Distortion, Brain

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Proceedings Article | 3 April 2023 Poster + Paper

Comparing voxel- and feature-wise harmonization of complex graph measures from multiple sites for structural brain network investigation of aging

Nancy Newlin, Leon Cai, Tianyuan Yao, Derek Archer, Kurt Schilling, Timothy Hohman, Kimberly Pechman, Angela Jefferson, Andrea Shafer, Susan Resnick, Bennett Landman

Proceedings Volume 12464, 124642B (2023) https://doi.org/10.1117/12.2653947

KEYWORDS: Diffusion weighted imaging, Scanners, Brain, Statistical analysis, Neuroimaging, Voxels, Cognitive modeling, Alzheimer's disease

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Complex graph theory measures of brain structural connectomes derived from diffusion weighted images (DWI) provide insight into the network structure of the brain. Further, as the number of available DWI datasets grows, so does the ability to investigate associations in these measures with major biological factors, like age. However, one key hurdle that remains is the presence of scanner effects that can arise from different DWI datasets and confound multisite analyses. Two common approaches to correct these effects are voxel-wise and feature-wise harmonization. However, it is still unclear how to best leverage them for graph-theory analysis of an aging population. Thus, there is a need to better characterize the impact of each harmonization method and their ability to preserve age related features. We investigate this by characterizing four complex graph theory measures (modularity, characteristic path length, global efficiency, and betweenness centrality) in 48 participants aged 55 to 86 from Baltimore Longitudinal Study of Aging (BLSA) and Vanderbilt Memory and Aging Project (VMAP) before and after voxel- and feature-wise harmonization with the Null Space Deep Network (NSDN) and ComBat, respectively. First, we characterize across dataset coefficients of variation (CoV) and find the combination of NSDN, and ComBat causes the greatest reduction in CoV followed by ComBat alone then NSDN alone. Second, we reproduce published associations of modularity with age after correcting for other covariates with linear models. We find that harmonization with ComBat or ComBat and NSDN together improves the significance of existing age effects, reduces model residuals, and qualitatively reduces separation between datasets. These results reinforce the efficiency of statistical harmonization on the feature-level with ComBat and suggest that harmonization on the voxel-level is synergistic but may have reduced effect after running through the multiple layers of the connectomics pipeline. Thus, we conclude that feature-wise harmonization improves statistical results, but the addition of biologically informed voxel-based harmonization offers further improvement.

Proceedings Article | 3 April 2023 Presentation + Paper

SynBOLD-DisCo: synthetic BOLD images for distortion correction of fMRI without additional calibration scans

Tian Yu, Leon Cai, Victoria Morgan, Sarah Goodale, Dario Englot, Catherine Chang, Bennett Landman, Kurt Schilling

Proceedings Volume 12464, 1246417 (2023) https://doi.org/10.1117/12.2653647

KEYWORDS: Distortion, Magnetic resonance imaging, Functional magnetic resonance imaging, Brain, Neuroimaging, Visualization, Signal attenuation, Image processing, Deep learning

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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 | 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 | 4 April 2022 Presentation + Paper

Mapping pediatric spinal cord development with age

Ashwin Kumar, Simon Vandekar, Kurt Schilling, Aashim Bhatia, Bennett Landman, Seth Smith

Proceedings Volume 12032, 1203213 (2022) https://doi.org/10.1117/12.2612210

KEYWORDS: Spinal cord, Spine, Brain, Image segmentation, Radiology, Pathology, Medicine

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Proceedings Article | 4 April 2022 Presentation + Paper

Joint independent component analysis for hypothesizing spatiotemporal relationships between longitudinal gray and white matter changes in preclinical Alzheimer’s disease

Leon Cai, Francois Rheault, Cailey Kerley, Katherine Aboud, Lori Beason-Held, Andrea Shafer, Susan Resnick, Lori Jordan, Adam Anderson, Kurt Schilling, Bennett Landman

Proceedings Volume 12032, 120321H (2022) https://doi.org/10.1117/12.2611562

KEYWORDS: Independent component analysis, Visualization, Magnetic resonance imaging, Alzheimer's disease, Brain, Diffusion tensor imaging, Principal component analysis, Neuroimaging, Data modeling, Shape analysis

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Proceedings Article | 4 April 2022 Presentation + Paper

Mapping the impact of non-linear gradient fields on diffusion MRI tensor estimation

Praitayini Kanakaraj, Colin Hansen, Francois Rheault, Leon Cai, Karthik Ramadass, Baxter Rogers, Kurt Schilling, Bennett Landman

Proceedings Volume 12032, 1203203 (2022) https://doi.org/10.1117/12.2611900

KEYWORDS: Diffusion, Tissues, Lawrencium, Anisotropy, Magnetism, Error analysis, Distortion, Diffusion magnetic resonance imaging, Scanners, Optical spheres

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Proceedings Article | 4 April 2022 Paper

Longitudinal changes of connectomes and graph theory measures in aging

Yuzhe Wang, Francois Rheault, Kurt Schilling, Lori Beason-Held, Andrea Shafer, Susan Resnick, Bennett Landman

Proceedings Volume 12032, 120321U (2022) https://doi.org/10.1117/12.2611845

KEYWORDS: Brain, Matrices, Diffusion, Neuroimaging, Image processing, Diffusion weighted imaging, Statistical analysis, Data modeling, Brain mapping

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Joint cortical surface and structural connectivity analysis of Alzheimer’s disease

Leon Cai, Cailey Kerley, Chang Yu, Katherine Aboud, Lori Beason-Held, Andrea Shafer, Susan Resnick, Lori Jordan, Adam Anderson, Kurt Schilling, Ilwoo Lyu, Bennett Landman

Proceedings Volume 11596, 1159630 (2021) https://doi.org/10.1117/12.2580956

KEYWORDS: Alzheimer's disease, Principal component analysis, Magnetic resonance imaging, Electroencephalography, Neuroimaging, Independent component analysis, Hemodynamics, Functional magnetic resonance imaging, Diffusion, Control systems

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Proceedings Article | 15 February 2021 Presentation + Paper

Joint analysis of structural connectivity and cortical surface features: correlates with mild traumatic brain injury

Cailey Kerley, Leon Cai, Chang Yu, Logan Crawford, Jason Elenberger, Eden Singh, Kurt Schilling, Katherine Aboud, Bennett Landman, Tonia Rex

Proceedings Volume 11596, 115960R (2021) https://doi.org/10.1117/12.2580902

KEYWORDS: Traumatic brain injury, Independent component analysis, Principal component analysis, Pathology, Magnetic resonance imaging, Injuries, Diffusion, Brain mapping, Brain

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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 | 10 March 2020 Paper

MRI correlates of chronic symptoms in mild traumatic brain injury

Cailey Kerley, Kurt Schilling, Justin Blaber, Beth Miller, Allen Newton, Adam Anderson, Bennett Landman, Tonia Rex

Proceedings Volume 11313, 113132Q (2020) https://doi.org/10.1117/12.2549493

KEYWORDS: Diffusion weighted imaging, Principal component analysis, Magnetic resonance imaging, Traumatic brain injury, Control systems, Visualization, Shape analysis, Injuries, Brain, Image segmentation

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Some veterans with a history of mild traumatic brain injury (mTBI) have reported experiencing auditory and visual dysfunction that persist beyond the acute phase of the incident. The etiology behind these symptoms is difficult to characterize, since mTBI is defined by negative imaging findings on current clinical imaging. There are several competing hypotheses that could explain functional deficits; one example is shear injury, which may manifest in diffusion-weighted magnetic resonance (MR) imaging (DWI). Herein, we explore this alternative hypothesis in a pilot study of multiparametric MR imaging. Briefly, we consider a cohort of 8 mTBI patients relative to 22 control subjects using structural T1-weighted imaging (T1w) and connectivity with DWI. 1,344 metrics were extracted per subject from whole brain regions and connectivity patterns in sensory networks. For each set of imaging-derived metrics, the control subject metrics were embedded in a low-dimensional manifold with principal component analysis, after which mTBI subject metrics were projected into the same space. These manifolds were employed to train support vector machines (SVM) to classify subjects as controls or mTBI. Two of the SVMs trained achieved near-perfect accuracy averaged across four-fold cross-validation. Additionally, we present correlations between manifold dimensions and 22 self-reported mTBI symptoms and find that five principal components from the manifolds (one component from the T1w manifold and four components from the DWI manifold) are significantly correlated with symptoms (p<0.05, uncorrected). The novelty of this work is that the DWI and T1w imaging metrics seem to contain information critical for distinguishing between mTBI and control subjects. This work presents an analysis of the pilot phase of data collection of the Quantitative Evaluation of Visual and Auditory Dysfunction and Multi-Sensory Integration in Complex TBI Patients study and defines specific hypotheses to be tested in the full sample.

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

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 | 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 | 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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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

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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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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