Mr. Justin Akiro Blaber Profile

Justin Akiro Blaber

at Vanderbilt Univ

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Author

Publications (13)

Proceedings Article | 10 March 2020 Presentation + Paper

Generalizing deep whole brain segmentation for pediatric and post-contrast MRI with augmented transfer learning

Camilo Bermudez, Justin Blaber, Samuel Remedios, Jess Reynolds, Catherine Lebel, Maureen McHugo, Stephan Heckers, Yuankai Huo, Bennett Landman

Proceedings Volume 11313, 113130L (2020) https://doi.org/10.1117/12.2548622

KEYWORDS: Image segmentation, Brain, Magnetic resonance imaging, Neuroimaging, Data acquisition, Neural networks, Image processing, Databases, Medical imaging

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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

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 | 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 | 9 March 2018 Paper

Phantom-based field maps for gradient nonlinearity correction in diffusion imaging

Baxter Rogers, Justin Blaber, Allen Newton, Colin Hansen, E. Brian Welch, Adam Anderson, Jeffrey Luci, Carlo Pierpaoli, Bennett Landman

Proceedings Volume 10573, 105733N (2018) https://doi.org/10.1117/12.2293786

KEYWORDS: Diffusion, Anisotropy, Magnetic resonance imaging

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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

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 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 | 9 March 2017 Paper

Stability of gradient field corrections for quantitative diffusion MRI

Baxter Rogers, Justin Blaber, E. Brian Welch, Zhaohua Ding, Adam Anderson, Bennett Landman

Proceedings Volume 10132, 101324X (2017) https://doi.org/10.1117/12.2254609

KEYWORDS: Diffusion, Calibration, Magnetic resonance imaging, Diffusion magnetic resonance imaging, Scanners, Magnetism, Anisotropy, Solids, Brain imaging, 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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