Diffusion Weighted MRI Category

Atlas interface and functionality. The toolbars, menus, and display associated with MRI (a) and histology (b) modalities are shown

A Web-based Combined MRI-Histology Digital Atlas of the Squirrel Monkey Brain.

Dec. 15, 2018—Kurt G. Schilling, Yurui Gao, Vaibhav Janve, Matthew Christian, Iwona Stepniewska, Bennett A. Landman, Adam W. Anderson. “A Web-based Combined MRI-Histology Digital Atlas of the Squirrel Monkey Brain.” Neuroinformatics (2018) 1-15. doi:10.1007/s12021-018-9391-z. Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=A+Web-based+Combined+MRI-Histology+Digital+Atlas Abstract The squirrel monkey (Saimiri sciureus) is a commonly-used surrogate for humans in biomedical research. In the neuroimaging community, MRI...

Methodology pipeline. High resolution BDA micrographs are registered to the corresponding digital photograph of the frozen tissue block, which is registered to the 3D diffusion MRI volume. From the micrograph, BDA is automatically segmented, resulting in a BDA density map. From diffusion MRI, tractography is performed, resulting in tract density maps. Direct, voxel-by-voxel comparisons can now be made between histology and diffusion tractography.

Anatomical Accuracy of Standard-Practice Tractography Algorithms in the Motor System – a Histological Validation in the Squirrel Monkey Brain

Dec. 15, 2018—Kurt G. Schilling, Yurui Gao, Vaibhav Janve, Iwona Stepniewska, Bennett Landman, Adam Anderson. “Anatomical Accuracy of Standard-Practice Tractography Algorithms in the Motor System – a Histological Validation in the Squirrel Monkey Brain”. Magnetic Resonance Imaging. 2018. doi: 1016/j.mri.2018.09.004 Full text: https://www.ncbi.nlm.nih.gov/pubmed/30213755 Abstract For two decades diffusion fiber tractography has been used to probe both the spatial extent of...

Diffusion tractograms for randomly selected submissions. Tractography is shown in the coronal and sagittal planes, for both macaque pathways (A), the squirrel monkey pathway (B), and all 16 phantom bundles (C).

Limits to anatomical accuracy of diffusion tractography using modern approaches

Dec. 15, 2018—Kurt G Schilling, Vishwesh Nath, Colin Hansen, Prasanna Parvathaneni, Justin Blaber, Yurui Gao, Peter Neher, Dogu Baran Aydogan, Yonggang Shi, Mario Ocampo-Pineda, Simona Schiavi, Alessandro Daducci , Gabriel Girard, Muhamed Barakovic, Jonathan Rafael-Patino, David Romascano, Gaëtan Rensonnet, Marco Pizzolato, Alice Bates, Elda Fischi, Jean-Philippe Thiran, Erick J. Canales-Rodríguez, Chao Huang, Hongtu Zhu, Liming Zhong, Ryan...

Fig. 1. Past challenges in fiber tractography. Detailed description of data, ground truth, and evaluation are described in the text. (A) FiberCup Phantom pathways with 16 ground truth bundles [21]. (B) Eight example CST reconstructions from the DTI Challenge [23]. (C) Synthetic fiber fields from the HARDI Reconstruction Challenge [26]. (D) Phantomas [27] dataset for Tractometer evaluation [28]. (E) Creation of simulated in vivo human dataset for the ISMRM Tractography Challenge [29]. (F) Example submissions from the TraCED Reproducibility Challenge for two white matter pathways. (G) 3D-VoTEM ground truths defined on the macaque, squirrel monkey, and phantom (from left to right).

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...

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....

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...

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...

Gray Matter Surface based Spatial Statistics in Neuroimaging Studies

Jun. 1, 2017—Citation: Gray Matter Surface based Spatial Statistics in Neuroimaging Studies. Authors: Prasanna Parvatheni, Baxter P. Rogers, Yuankai Huo, Kurt G. Schilling, Allison E. Hainline, Adam W. Anderson, Neil D. Woodward, Bennett A. Landman. Frontiers in Biomedical Imaging Science VI. May 2017. Abstract. Abstract In this study, we propose gray matter surface based spatial statistics (GS-BSS)...

$QBI glyphs in crossing fiber (left) and single fiber (right) regions, for various b-values, number of gradient directions, and maximum SH order fit. Glyphs are shown min-max normalized and are displayed on top of fractional anisotropy maps.$

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...

Figure 1. LEFT: Image of the gradient field mapping phantom. RIGHT: Measured field map for the X gradient coil. Fields were measured for all three linear gradient coils.

Stability of Gradient Field Corrections for Quantitative Diffusion MRI

Nov. 1, 2016—Baxter P. Rogers, Justin Blaber, E. Brian Welch, Zhaohua Ding, Adam W. Anderson, Bennett A. Landman. “Stability of Gradient Field Corrections for Quantitative Diffusion MRI” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Full Text: Abstract In diffusion-weighted MRI, gradient nonlinearity causes significant bias in the estimation of quantitative diffusion parameters...

« Previous Page