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Reproducibility Category

Enabling Multi-shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE

Jan. 17, 2020—Nath V, Lyu I, Schilling KG, Parvathaneni P, Hansen CB, Huo Y, Janve VA, Gao Y, Stepniewska I, Anderson AW, Landman BA. Enabling Multi-shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE. In International Conference on Medical Image Computing and Computer-Assisted Intervention 2019 Oct 13 (pp. 573-581). Springer, Cham. Full text: https://arxiv.org/ftp/arxiv/papers/1907/1907.06319.pdf Abstract Intra-voxel...

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Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators

Jan. 17, 2020—Nath V, Remedios S, Parvathaneni P, Hansen CB, Bayrak RG, Bermudez C, Blaber JA, Schilling KG, Janve VA, Gao Y, Huo Y. Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators. In Medical Imaging 2019: Image Processing 2019 Mar 15 (Vol. 10949, p. 109490O). International Society for Optics and Photonics....

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Tractography reproducibility challenge with empirical data (TRAceD): The 2017 ISMRM diffusion study group challenge

Jan. 17, 2020—Nath V, Schilling KG, Parvathaneni P, Huo Y, Blaber JA, Hainline AE, Barakovic M, Romascano D, Rafael‐Patino J, Frigo M, Girard G. Tractography reproducibility challenge with empirical data (traced): The 2017 ISMRM diffusion study group challenge. Journal of Magnetic Resonance Imaging. 2020 Jan;51(1):234-49. Full text: https://www.ncbi.nlm.nih.gov/pubmed/31179595 Abstract BACKGROUND: Fiber tracking with diffusion-weighted MRI has become an...

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Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI

Jan. 17, 2020—Nath V, Schilling KG, Parvathaneni P, Hansen CB, Hainline AE, Huo Y, Blaber JA, Lyu I, Janve V, Gao Y, Stepniewska I, Anderson AW, Landman BA. Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI. Magnetic resonance imaging. 2019 Oct 1;62:220-7. Abstract PURPOSE: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance...

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Challenges in diffusion MRI tractography – Lessons learned from international benchmark competitions

Dec. 15, 2018—Kurt G Schilling, Alessandro Daducci, Klaus Maier-Hein, Cyril Poupon, Jean-Christophe Houde, Vishwesh Nath, Adam W Anderson, Bennett A Landman, Maxime Descoteaux. “Challenges in Diffusion MRI Tractography – Lessons Learned from International Benchmark Competitions”. Magnetic Resonance Imaging. 2018. doi: 10.1016/j.mri.2018.11.014 Full text: NIHMSID https://www.sciencedirect.com/science/article/pii/S0730725X18305162#f0005 Abstract Diffusion MRI (dMRI) fiber tractography has become a pillar of the neuroimaging...

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Technology Enablers for Big Data, Multi-Stage Analysis in Medical Image Processing

Oct. 26, 2018—Shunxing Bao, Prasanna Parvathaneni, Yuankai Huo, Yogesh Barve, Andrew J. Plassard, Yuang Yao, Hongyang Sun, Ilwoo Lyu, David H. Zald, Bennett A. Landman and Aniruddha Gokhale. “Technology Enablers for Big Data, Multi-Stage Analysis in Medical Image Processing.” Big Data (Big Data), 2018 IEEE International Conference. (accepted) (acceptance rate 18.9%) Full text: TBD Abstract Big data medical image processing...

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Harmonization of white and gray matter features in diffusion microarchitecture for cross sectional studies

Jun. 25, 2018—Prasanna Parvathaneni, Shunxing Bao , Allison Hainline , Yuankai Huo , Kurt G. Schilling , Hakmook Kang , Owen Williams , Neil D. Woodward , Susan M. Resnick , David H. Zald  , Ilwoo Lyu , Bennett A. Landman “Harmonization of white and gray matter features in diffusion microarchitecture for cross sectional studies.”  In International Conference on Clinical and Medical Image Analysis 2018 (ICCMIA’18) – Accepted Abstract Understanding of the specific processes...

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Empirical Reproducibility, Sensitivity, and Optimization of Acquisition Protocol for Neurite Orientation Dispersion and Density Imaging using AMICO

Apr. 6, 2018—Prasanna Parvathaneni, Vishwesh Nath, Justin A. Blaber, Kurt G Schilling, Allison E. Hainline, Adam W Anderson, and Bennett A. Landman “Empirical Reproducibility, Sensitivity, and Optimization of Acquisition Protocol for Neurite Orientation Dispersion and Density Imaging using AMICO”. Magnetic Resonance Imaging. Mar 2018. Abstract Neurite Orientation Dispersion and Density Imaging (NODDI) is a relatively new model for diffusion weighted...

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Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging

May. 5, 2017—Kurt G. Schilling, Vishwesh Nath, Justin Blaber Prasanna Parvathaneni, Adam W. Anderson, Bennett A. Landman. “Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging” Magnetic Resonance Imaging. Submitted November 2016. (In Press). Full Text: https://www.ncbi.nlm.nih.gov/pubmed/28438712 Abstract Q-ball imaging (QBI) is a popular high angular resolution diffusion imaging (HARDI) technique used...

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Improving Cerebellar Segmentation with Statistical Fusion

Feb. 27, 2016—Andrew J. Plassard, Zhen Yang, Swati D. Rane, Jerry L. Prince, Daniel O. Claassen, Bennett A. Landman. “Improving Cerebellar Segmentation with Statistical Fusion. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Improving+Cerebellar+Segmentation+with+Statistical+Fusion Abstract The cerebellum is a somatotopically organized central component of the central nervous system well known...

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