Ms. Allison Elisabeth Hainline Profile

Allison Elisabeth Hainline

Graduate Student Research Assistant

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Publications (8)

Proceedings Article | 15 March 2019 Paper

Towards machine learning prediction of deep brain stimulation (DBS) intra-operative efficacy maps

Camilo Bermudez, William Rodriguez, Yuankai Huo, Allison Hainline, Rui Li, Robert Shults, Pierre D’Haese, Peter Konrad, Benoit Dawant, Bennett Landman

Proceedings Volume 10949, 1094922 (2019) https://doi.org/10.1117/12.2509728

KEYWORDS: Magnetic resonance imaging, Machine learning, Brain mapping, Surgery, Brain stimulation, Data acquisition, Electrodes, Brain

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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

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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SPIE Journal Paper | 29 December 2017

Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results

Hakmook Kang, Allison Hainline, Lori R. Arlinghaus, Stephanie Elderidge, Xia Li, Vandana G. Abramson, Anuradha Bapsi Chakravarthy, Richard G. Abramson, Brian Bingham, Kareem Fakhoury, Thomas E. Yankeelov

JMI, Vol. 5, Issue 01, 011015, (December 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011015

KEYWORDS: Receptors, Data modeling, Tumors, Magnetic resonance imaging, Breast cancer, Chromium, Statistical modeling, Cancer, Breast, Tumor growth modeling

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SPIE Journal Paper | 24 November 2017 Open Access

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis

John Virostko, Allison Hainline, Hakmook Kang, Lori R. Arlinghaus, Richard G. Abramson, Stephanie L. Barnes, Jeffrey D. Blume, Sarah Avery, Debra Patt, Boone Goodgame, Thomas E. Yankeelov, Anna G. Sorace

JMI, Vol. 5, Issue 01, 011011, (November 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011011

KEYWORDS: Magnetic resonance imaging, Breast cancer, Tumors, Diagnostics, Temporal resolution, Data modeling, Statistical analysis, Receptors, Error analysis, Cancer

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