December, 2018

Imaging Biomarkers in Thyroid Eye Disease and their Clinical Associations

Dec. 18, 2018—Shikha Chaganti, Katrina Nelson, Kevin Mundy, Robert Harrigan, Robert Galloway, Louise A. Mawn, Bennett Landman, “Imaging biomarkers in thyroid eye disease and their clinical associations,” Journal of Medical Imaging. (2018), doi: 10.1117/1.JMI.5.4.044001. Abstract Purpose: The purpose of this study is to understand the phenotypes of thyroid eye disease (TED) through data derived from a multi-atlas...

Electronic Medical Record Context Signatures Improve Diagnostic Classification using Medical Image Computing

Dec. 17, 2018—Shikha Chaganti, Louise A. Mawn, Hakmook Kang, Josephine Egan, Susan M. Resnick, Lori L. Beason-Held, Bennett A. Landman, Thomas A. Lasko. “Electronic Medical Record Context Signatures Improve Diagnostic Classification using Medical Image Computing”. IEEE Journal of Biomedical and Health Informatics. (In Press) Abstract Composite models that combine medical imaging with electronic medical records (EMR) improve...

Subset of DTI streamlines for each tracking strategy. Labels for crown, wall, and fundi are shown with a zoomed in view of the SFG. DTI streamlines are shown for M1 (whole brain seeding), M2 (WM seeding), and M3 (WMGM boundary seeding), and are colored based on streamline orientation. The dashed arrow highlights a fundus, where no streamlines are able to propagate. The solid arrow points toward the increased curvature of streamlines entering the GM. And the oval highlights a large, homogenous, area of WM, where seeding will contribute to over‐representation of fibers terminating at the crown

Confirmation of a Gyral Bias in Diffusion MRI Fiber Tractography

Dec. 15, 2018—Kurt G Schilling, Yurui Gao, Iwona Stepniewska, Bennett A. Landman, and Adam W Anderson. “Confirmation of a Gyral Bias in Diffusion MRI Fiber Tractography”. Human Brain Mapping. 2018 Mar;39(3):1449-1466. doi: 10.1002/hbm.23936. Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Confirmation+of+a+Gyral+Bias+in+Diffusion+MRI+Fiber+Tractography Abstract Diffusion MRI fiber tractography has been increasingly used to map the structural connectivity of the human brain. However, this technique...

Fiber orientation estimation in a crossing fiber region. The results of structure tensor analysis on a myelin-stained histological slice shown as a color-coded orientation map (A), is shown zoomed in on two regions containing crossing (yellow box), and disperse (blue box) fibers. The resulting FODs are displayed at varying resolution levels (B). From the resulting FODs, voxels are characterized as crossing fiber (C; green) vs. not-crossing fibers (C; red), as well as single fiber (D; red) vs. complex fibers (D; green), at all resolutions. Note that “not-crossing” voxels are those that with single fiber populations in addition to complex fibers that do not contain two discrete local maxima. Voxels from diffusion MRI in the same region are also displayed as single fiber (E; red) vs. crossing fibers (E; green). Histograms for this specific region of interest show percentages of crossing fibers (F; left) and percentages of complex fibers (F; middle) for histology, as well as percentage of crossing fibers (F; right) for dMRI.

Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?

Dec. 15, 2018—Kurt G Schilling, Yurui Gao, Vaibhav Janve, Iwona Stepniewska, Bennett A Landman, Adam W Anderson. “Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?”. NMR in Biomedicine. (2017) 30(12),e3787. https://doi.org/10.1002/nbm.3787. Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Can+increased+spatial+resolution+solve+the+crossing+fiber+problem+for+diffusion+MRI%3F Abstract It is now widely recognized that voxels with crossing fibers or complex geometrical configurations present a challenge for...

Qualitative confocal images. Representative confocal data (of a single slice) are shown for single (A) and crossing (B) fiber regions. Overview images highlight location of full 3D z-stacks (shown as a single, middle slice). A zoomed regions of interest in the middle of the z-stack (equivalent in size to an MRI voxel) are shown in the middle column. Results from structure tensor analysis are shown as color-coded images (with colors scheme as described in Fig. 1), with the histologically-defined FOD overlaid as 3D glyphs (right).

Histological Validation of Diffusion MRI Fiber Orientation Distributions and Dispersion

Dec. 15, 2018—Kurt G Schilling, Vaibhav Janve; Yurui Gao; Iwona Stepniewska; Bennett A Landman; Adam W Anderson. “Histological Validation of Diffusion MRI Fiber Orientation Distributions and Dispersion”. NeuroImage. 2018 Jan 15;165:200-221. doi: 10.1016/j.neuroimage.2017.10.046. Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Histological+Validation+of+Diffusion+MRI+Fiber+Orientation+Distributions+and+Dispersion Abstract Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to...

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...

HARFI is able to capture complex, asymmetric functional distributions. Fiber fanning (A, B; red arrows), three‐way crossings (C; blue arrows), and bending (D; black arrows) voxels are reconstructed in regions expected to contain complex distributions

Functional Tractography of White Matter by High Angular Resolution Functional-correlation Imaging (HARFI)

Dec. 15, 2018—Kurt G. Schilling, Yurui Gao, Muwei Li, Tung-Lin Wu, Justin Blaber, Bennett A Landman, Adam W Anderson, Zhaohua Ding, John C Gore. “Functional Tractography of White Matter by High Angular Resolution Functional-correlation Imaging (HARFI)”. Magn Reson Med. 2018;00:1-14. https://doi.org/10.1002/mrm.27512. Full text: https://www.ncbi.nlm.nih.gov/pubmed/30277272 https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.27512 Abstract Purpose Functional magnetic resonance imaging with BOLD contrast is widely used for detecting...

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...

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