Song Zhang, David Laidlaw Brown University Computer Science

Slides:

Advertisements

Similar presentations

Hybrid Visualization for White Matter Tracts using Triangle Strips and Point Sprites D. Merhof 1,2, M. Sonntag 1, F. Enders 1,2, C. Nimsky 2, P. Hastreiter.

Advertisements

NA-MIC National Alliance for Medical Image Computing © 2010, All Rights Reserved Diffusion Tensor Imaging Tutorial Sonia Pujol, Ph.D.

NA-MIC National Alliance for Medical Image Computing © 2010, All Rights Reserved Diffusion Tensor Imaging Tutorial Sonia Pujol, Ph.D.

Imaging Methods: Gait, Cognition and Mood Richard Camicioli MD University of Alberta Edmonton, Alberta

Emmanuel A Stamatakis Centre for Speech, Language and the Brain, Department of Experimental Psychology, University of Cambridge School of Psychological.

1 Université de Montréal 2 INRIA Sophia Antipolis The Simulation of Paint Cracking and Peeling E. Paquette 1,2, P. Poulin 1, G. Drettakis 2.

Diffusion Tensor MRI And Fiber Tacking Presented By: Eng. Inas Yassine.

NAMIC: UNC – PNL collaboration- 1 - October 7, 2005 Fiber tract-oriented quantitative analysis of Diffusion Tensor MRI data Postdoctoral fellow, Dept of.

DTI-Based White Matter Fiber Analysis and Visualization Jun Zhang, Ph.D. Laboratory for Computational Medical Imaging & Data Analysis Laboratory for High.

1212 / mhj BM/i 2 Visualization of Diffusion Tensor Imaging Guus Berenschot May 2003 Supervisor: Bart ter Haar Romeny Daily Supervisor: Anna Vilanova i.

Video summarization by video structure analysis and graph optimization M. Phil 2 nd Term Presentation Lu Shi Dec 5, 2003.

High-Quality Video View Interpolation

J OHNS H OPKINS U NIVERSITY S CHOOL O F M EDICINE Statistically-Based Reorientation of Diffusion Tensor Field XU, D ONGRONG S USUMU M ORI D INGGANG S HEN.

NAMIC: UNC – PNL collaboration- 1 - October 7, 2005 Quantitative Analysis of Diffusion Tensor Measurements along White Matter Tracts Postdoctoral fellow,

Applied Anatomical Diagnostic Framework for Visualizing the Human Body in a 3-D, Immersive, Navigable and Interactive VR Environment May 4, 2012 Steven.

Diffusion-Tensor Imaging Tractography: Correlation with Processing Speed in Aging Stephen Correia 1, Stephanie Y. Lee 2, Song Zhang 2, Stephen P. Salloway.

Gwangju Institute of Science and Technology Intelligent Design and Graphics Laboratory Multi-scale tensor voting for feature extraction from unstructured.

Visualization of Fibers at Risk for Neuronal Tract Injury in Early MS by Streamtube Diffusion Tractography at 3 Tesla Jack H Simon 1 David E Miller 1 Mark.

Landing a UAV on a Runway Using Image Registration Andrew Miller, Don Harper, Mubarak Shah University of Central Florida ICRA 2008.

Visualizing Fiber Tracts in the Brain Using Diffusion Tensor Data Masters Project Presentation Yoshihito Yagi Thursday, July 28 th, 10:00 a.m. 499 Dirac.

R24 Pilot Project: Quantitatively Imaging Internal Muscle Activities from Multi-channel Surface EMG Recordings Yingchun Zhang, PhD Assistant Professor.

Ramifications of Isotropic Sampling and Acquisition Orientation on DTI Analyses David H. Laidlaw 1, Song Zhang 1, Mark Bastin 2,3, Stephen Correia 4, Stephen.

College of Health and Human Development Sciences, University of Illinois at Chicago Dept. of Ophthalmology and Visual Sciences NearReal-TimeCutting Paul.

References: [1]S.M. Smith et al. (2004) Advances in functional and structural MR image analysis and implementation in FSL. Neuroimage 23: [2]S.M.

Visualization of Anatomic Covariance Tensor Fields Gordon L. Kindlmann, David M. Weinstein, Agatha D. Lee, Arthur W. Toga, and Paul M. Thompson Presented.

University of Chicago, Section of General Surgery UC Hospitals, Center for Clinical Information Rigorous Exploration of Medical Data.

Fiber Demixing with the Tensor Distribution Function avoids errors in Fractional Anisotropy maps Liang Zhan 1,Alex D. Leow 2,3, Neda Jahanshad 1, Arthur.

Exploring Peritumoral White Matter Fibers for Neurosurgical Planning Sonia Pujol, Ph.D. Ron Kikinis, M.D. Surgical Planning Laboratory Harvard University.

New Techniques for Visualizing and Evaluating Left Ventricular Performance Burkhard Wünsche 1 & Alistair Young 2 1 Division for Biomedical Imaging & Visualization.

NA-MIC National Alliance for Medical Image Computing Diffusion Tensor Imaging tutorial Sonia Pujol, PhD Surgical Planning Laboratory.

NA-MIC - Contrasting Tractography Method Conference Utah/UNC results Sylvain Gouttard Guido Gerig Casey Goodlett Santa Fe, October 1 st & 2 nd 2007.

Microstructural White Matter Abnormalities and Cognitive Functioning in Type 2 Diabetes A Diffusion Tensor Imaging Study Featured Article: Yael D. Reijmer,

Strategies for Direct Volume Rendering of Diffusion Tensor Fields Gordon Kindlmann, David Weinstein, and David Hart Presented by Chris Kuck.

Visual Optimality And Stability Analysis Of 3DCT Scan Positions Artem Amirkhanov 1,2 Michael Reiter 2 Christoph Heinzl 2 M. Eduard Gröller 1 1 Institute.

Exploring Connectivity of the Brain’s White Matter with Dynamic Queries Presented by: Eugene (Austin) Stoudenmire 14 Feb 2007 Anthony Sherbondy, David.

NA-MIC National Alliance for Medical Image Computing NAMIC UNC Site Update Site PI: Martin Styner UNC Site NAMIC folks: C Vachet, G Roger,

Geometry Synthesis Ares Lagae Olivier Dumont Philip Dutré Department of Computer Science Katholieke Universiteit Leuven 10 August, 2004.

Sonia Pujol, PhD -1- National Alliance for Medical Image Computing Neuroimage Analysis Center Diffusion Tensor Imaging tutorial Sonia Pujol, Ph.D. Surgical.

A comparative approach for gene network inference using time-series gene expression data Guillaume Bourque* and David Sankoff *Centre de Recherches Mathématiques,

Shadows David Luebke University of Virginia. Shadows An important visual cue, traditionally hard to do in real-time rendering Outline: –Notation –Planar.

Asta K. Håberg Trondheim fMRI gruppe Department of Neuroscience Norwegian University of Science and Technology (NTNU) Human navigation abilities elucidated.

Implicit Uncertainty Visualization: Aligning Perception and Statistics Michael Correll Michael Gleicher.

Coached Active Learning for Interactive Video Search Xiao-Yong Wei, Zhen-Qun Yang Machine Intelligence Laboratory College of Computer Science Sichuan University,

Detecting Occlusion from Color Information to Improve Visual Tracking

The past and future of virtual reality simulation in neurologic surgery Longwei F

Looking at connections between brain regions

Similarity Measures for Enhancing Interactive Streamline Seeding

RESULTS AND DISCUSSION

Data Visualization at Swansea University

© The Author(s) Published by Science and Education Publishing.

Visualizing Diffusion Tensor Imaging Data with Merging Ellipsoids

2017 HUMAN CAPITAL INVESTMENT CONFERENCE

Enumeration of Time Series Motifs of All Lengths

Human Brain Mapping Conference 2003 # 653

Linear Equations and Rational Equations

Wei Chen1, Song Zhang2, Stephan Correia3, and David S. Ebert4

What Visualization can do for Data Clustering?

Tensor Visualization Chap. 7 October 21, 2008 Jie Zhang Copyright ©

(A) Diffusion-tensor imaging assessment of white matter damage after traumatic brain injury (TBI). (A) Diffusion-tensor imaging assessment of white matter.

Figure 3 Associations of [11C](R)-PK11195 binding to disability and diffusion tensor imaging (DTI) changes in patients with MS Associations of [11C](R)-PK11195.

Maguire (2000) Navigation-related structural changes in the hippocampi of taxi drivers.

An Introduction of Marker and Markerless In AR

Jennifer N.A. Silva et al. BTS 2018;3:

Joseph I. Friedman et al. JIMG 2014;7:

Direct Comparison Tests

컴퓨터 그래픽스를 위한 기하학적 기초 (Line Geometry for Computer Graphics)

– Graphics and Visualization

Diffusion Tensor MRI of White Matter of Healthy Full-term Newborns: Relationship to Neurodevelopmental Outcomes Higher white matter integrity (as reflected.

Intergroup comparisons of fractional anisotropy values using TBSS

Fig. 6 SVR analysis relating the spatial fingerprint of head impacts to hemispheric asymmetries in white matter. SVR analysis relating the spatial fingerprint.

Presentation transcript:

Song Zhang, David Laidlaw Brown University Computer Science Elucidating Neural Structure in DT-MRI Volumes Using Streamtubes and Streamsurfaces Song Zhang, David Laidlaw Brown University Computer Science

Overview Interaction, geometry, exploration Anisotropy Streamtubes and streamsurfaces Anatomical landmarks Culling Discussion and conclusions

Anisotropy metrics(Westin et al) Linear anisotropy Planar anisotropy Isotropy(Not visualized) In our method, we try to distinguish linear anisotropy and planar anisotropy, that is, our anisotropy metric should be direction dependent. Thus, we can’t use Rational anisotropy or Fractional anisotropy, cause they are directionless, they won’t tell the difference between linear and planar anisotropy. Intuitively, we use the difference of the first 2 eigenvalues for the metric for linear anisotropy, and difference between 2nd and 3rd eigenvalues for metrics for planar anisotropy. We set a threshold alpha and beta to determine whether a certain sample has linear anisotropy or planar anisotropy.

Streamtubes

Streamsurfaces

Landmarks

Culling Reduce 1.2M to 472 Anisotropy threshold (>0.20) Avg. anisotropy (>0.30) Tube length (>18.0mm) Tube similarity (>4.5mm) Noise distance threshold (<0.9mm) Average distance between tubes in regions of dissimilarity.

Culling Reduce 1.2M to 4,538 (was 472) Anisotropy threshold > 0.20 Average anisotropy > 0.20 (was 0.30) Tube length > 4.5mm (was 18.0mm) Tube similarity > 1.8mm (was 4.5mm) Average distance between tubes in regions of dissimilarity.

Many more tubes, more obstruction, interaction slower

Many more tubes, more obstruction, interaction slower

Applications Exploration of DT-MRI data Preoperative surgical planning Longitudinal changes Beyond video – immersive virtual reality Only way to image white matter tracts Changes due to development or pathology

Average distance between tubes in regions of dissimilarity.

Conclusions Explore entire tensor Use human visual system 3D, interaction, stereo, graphic design No hard thresholds Too much geometry

Thanks Cagatay Demiralp, Marco da Silva, Charlie Curry, Dan Morris Many visitors for feedback Susumu Mori, JHU (DT-MRI) HBP (NIDA & NIMH) NSF (CCR-0086065)

Internal Capsule

Culling Reduce 150,000 to several thousand Anisotropy threshold (>0.20) Tube length (>20mm) Tube similarity (>45mm) Average distance between tubes in regions of dissimilarity.