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Different classes of methods have been used to infer tissue microstructure from single shell and multi shell DW-MRI data. The gap addressed herein is in data-driven machine learning models for multi-shell DW-MRI data.

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...

The base regression neural network is depicted in the center which describes the parameters of the fullyconnected dense layers with respective activation functions. The plot at left: Depicts three inputs where the center input
comes from corresponding DW-MRI with histology. The other two are pairwise inputs from corresponding voxels of
scanner 1.5T and 3T. The plot at right: Depicts the loss function which uses the hypothesis that the outcome/prediction
should be same irrespective of the scanner gradient strength.

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....

Violin plots of intrasession submissions across both the scanners per tract. (A) Dice similarity coefficients, (B) intraclass correlation coefficients. The top row depicts the median of the top five intrasession submissions. The tracts are in the following order (L/R): a) Uncinate, b) Fornix, c) Fminor & Fmajor, d) Cingulum, e) Corticospinal tract, f) Inferior longitudinal fasciculus, g) Superior longitudinal fasciculus, h) Inferior fronto‐occipital tract.

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...

A) Middle axial slice of a human brain acquired using DW-MRI. B, C) CSD FOD's of the same subject on two different scanners showing inconsistency which cannot be validated without ground truth. D) Coronal slice of a Squirrel Monkey. E, F, G, H) Right: CSD FOD's at b-value of 3000 s/mm2 Left: Ground truth reconstruction using aggregated histological structural tensors depicting a loss in precision.

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...

Learning 3D White Matter Microstructure from 2D Histology

Apr. 1, 2019—Histological analysis is typically the gold standard for validating measures of tissue microstructure derived from magnetic resonance imaging (MRI) contrasts. However, most histological investigations are inherently 2-dimensional (2D), due to increased field-of-view, higher in-plane resolutions, ease of acquisition, decreased costs, and a large number of available contrasts compared to 3-dimensional (3D) analysis. Because of this,...

Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning

Sep. 10, 2018—Vishwesh Nath, Prasanna Parvathaneni, Colin B. Hansen, Allison E. Hainline, Camilo Bermudez, Samuel Remedios, Justin A. Blaber, Kurt G. Schilling, Ilwoo Lyu, Vaibhav Janve, Yurui Gao, Iwona Stepniewska, Baxter P. Rogers, Allen T. Newton, L. Taylor Davis, Jeff Luci, Adam W. Anderson and Bennett A. Landman (Accepted at Computation Diffusion MRI Workshop at MICCAI 2018) Abstract....

SHARD: Spherical Harmonic-based Robust Outlier Detection for HARDI Methods

Dec. 19, 2017—Citation: ” SHARD: Spherical Harmonic-based Robust Outlier Detection for HARDI Methods”. Vishwesh Nath, Ilwoo Lyu, Kurt G. Schilling, Allison E. Hainline, Prasanna Parvathaneni, Justin A. Blaber, Ilwoo Lyu, Adam W. Anderson, Hakmook Kang, Allen T. Newton, Baxter P. Rogers, Bennett A. Landman In SPIE Medical Imaging, International Society for Optics and Photonics, 2018 (Accepted) Abstract: High Angular...

Qualitative comparisons between body-wise and organ-wise approaches on spleen (red), right kidney (yellow), and left kidney (blue). Padded bounding boxes in correspondent colors indicate the ROIs for organ-wise registrations.

Evaluation of Body-Wise and Organ-Wise Registrations for Abdominal Organs

Apr. 15, 2016—Zhoubing Xu, Sahil A. Panjwani, Christopher P. Lee, Ryan P. Burke, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman, “Evaluation of Body-Wise and Organ-Wise Registrations for Abdominal Organs”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845963/ Abstract Identifying cross-sectional and...

Intermediary results from image synthesis. The cited values for T2-relaxation in gray matter, white matter are approximately 100 and 80 ms respectively. The cited values for T1-relaxation in the gray matter and white matter are approximately 1350 and 800 ms respectively. These values appear consistent with the intermediary results of image synthesis.

One the Fallacy of Quantitative Segmentation for T1-Weighted MRI.

Feb. 15, 2016—Andrew J. Plassard, Robert L. Harrigan, Allen T. Newton, Swati D. Rane, Srivatsan Pallavaram, Pierre F. D’Haese, Benoit M. Dawant, Daniel O. Claassen, Bennett A. Landman. “One the Fallacy of Quantitative Segmentation for T1-Weighted MRI.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845960/ Abstract T1-weighted...

Results for prediction of high-risk discharge GCS cases (a) Average ROC curves internal cross-validated sets (b) Average effect of each new feature that is added via the greedy feature selection method (c) ROC curve for the holdout dataset (d) Effect of each new feature that is added via the greedy feature selection method in the holdout dataset.

A Bayesian Framework for Early Risk Prediction in Traumatic Brain Injury

Feb. 15, 2016—Shikha Chaganti, Andrew J. Plassard, Laura Wilson, Miya A. Smith, Mayur B. Patel, Bennett A. Landman. A Bayesian Framework for Early Risk Prediction in Traumatic Brain Injury. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845965/ Abstract Early detection of risk is critical in determining the course...

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