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Histologically derived fiber response functions for diffusion MRI vary across white matter fibers – an ex vivo validation study in the squirrel monkey brain

Aug. 25, 2020—Kurt G Schilling, Yurui Gao, Iwona Stepniewska, Vaibhav Janve, Bennett A. Landman, Adam W. Anderson. “Histologically derived fiber response functions for diffusion MRI vary across white matter fibers – an ex vivo validation study in the squirrel monkey brain”. NMR in Biomedicine. 2019 Jun;32(6):e4090. doi: 10.1002/nbm.4090. Epub 2019 Mar 25. Full text: https://pubmed.ncbi.nlm.nih.gov/30908803/ Abstract Understanding the...

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Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

Aug. 25, 2020—Kurt G Schilling*, Justin Blaber*, Colin Hansen, Leon Cai, Baxter Rogers, Adam W Anderson, Seth A Smith, Praitayini Kanakaraj, Tonia Rex, Susan M. Resnick, Andrea T. Shafer, Laurie Cutting, Neil Woodward, David Zald, Bennett A Landman.“ Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps ”. PLoS ONE 15(7): e0236418. https://doi.org/10.1371/journal.pone.0236418 Full text: https:...

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Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort

Apr. 20, 2020—Lingyan Hao, Shunxing Bao, Yucheng Tang, Riqiang Gao, Prasanna Parvathaneni, Jacob Miller, Willa Voorhies, Jewelia Yao, Silvia Bunge, Kevin Weiner, Bennett Landman, Ilwoo Lyu. “Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort”. IEEE International Symposium on Biomedical Imaging (ISBI) 2020, IEEE, 412-415, Iowa City, Iowa, USA, 2020. [Full text][Code] Abstract...

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Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives

Feb. 12, 2020—Olivia Tang, Yuchen Xu, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman, “Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives”, SPIE IP:MI 2020. Houston, TX. https://arxiv.org/abs/2002.04102 Abstract Segmentation of abdominal computed tomography (CT) provides spatial context, morphological...

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Outlier Guided Optimization of Abdominal Segmentation

Feb. 12, 2020—Yuchen Xu*, Olivia Tang*, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman, “Outlier Guided Optimization of Abdomen Segmentation”, SPIE IP:MI 2020. Houston, TX https://arxiv.org/abs/2002.04098 Abstract Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive...

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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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Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection

Jan. 2, 2020—Remedios, S. W., Wu, Z., Bermudez, C., Kerley, C. I., Roy, S., Patel, M. B., Butman, J. A., Landman, B. A., Pham, D. L. (2019). Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection. arXiv preprint arXiv:1911.05650. Full Text: Arxiv Link Abstract Multiple instance learning (MIL) is a supervised learning methodology...

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