High Angular Diffusion Imaging and its Visualization… Limitations of DTI Why HARDI is better?!? Different HARDI models My ideas and current work.

Slides:

Advertisements

Similar presentations

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

Advertisements

1 Detecting Subtle Changes in Structure Chris Rorden –Diffusion Tensor Imaging Measuring white matter integrity Tractography and analysis.

Methods for Medical Imaging– Prof. G. Baselli 2012 Diffusion weighted MRI and DTI tractography Maria Giulia Preti

Image Repairing: Robust Image Synthesis by Adaptive ND Tensor Voting IEEE Computer Society Conference on Computer Vision and Pattern Recognition Jiaya.

National Alliance for Medical Image Computing Diffusion Weighted MRI.

DTI group (Pitt) Instructor: Kevin Chan Kaitlyn Litcofsky & Toshiki Tazoe 7/12/2012.

DIFFUSION TENSOR IMAGING

MaxEnt 2007 Saratoga Springs, NY

ADC and ODF estimation from HARDI

The Danish National Research Foundation’s Center of Functionally Integrative Neuroscience Aarhus University / Aarhus University Hospital Diffusion weighted.

Diffusion Tensor Imaging Tim Hughes & Emilie Muelly 1.

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

Master thesis by H.C Achterberg

Introduction to diffusion MRI

Diffusion Physics H 2 O in the body is always in random motion due to thermal agitation B.M. Ellingson, Ph.D., Dept. of Radiological Sciences, David Geffen.

05/19/11Why’n’how | Diffusion model fitting and tractography0/25 Diffusion model fitting and tractography: A primer Anastasia Yendiki HMS/MGH/MIT Athinoula.

Multimodal Visualization for neurosurgical planning CMPS 261 May 17 th 2010 Uliana Popov.

TUD MedVis Overview

Expectation Maximization for GMM Comp344 Tutorial Kai Zhang.

Multimodal Visualization for neurosurgical planning CMPS 261 June 8 th 2010 Uliana Popov.

Diffusion Tensor Processing and Visualization Ross Whitaker University of Utah National Alliance for Medical Image Computing.

Combined fMRI and DTI of the human low level visual cortex I. Abstract We study the anatomical connectivity network in the human low-level visual cortex.

"THE POTENTIAL OF MULTIMODAL MRI FOR UNDERSTANDING SPORTS CONCUSSION" Victoria Morgan, Ph.D., Manus Donahue, Ph.D., Megan Strother, M.D., Allen Sills,

Trajectory Physics Based Fibertracking in Diffusion Tensor Magnetic Resonance Imaging Garrett Jenkinson, Advisor: José Moura, Graduate Student: Hsun-Hsien.

2 High Resolution T2 Imaging Rat versus Mouse T1 MP-RAGE Dark CSF Resolution Rat: 156 x 156 x 700 µ m 3 Mouse: 138 x 117 x 400 µm 3 Coil setup: Transmit:Integrated.

Benoit Scherrer, ISBI 2010, Rotterdam Why multiple b-values are required for multi-tensor models. Evaluation with a constrained log- Euclidean model. Benoit.

Tensor Distribution Function in Multiple Shell High Angular Resolution Diffusion Imaging Tensor Distribution Function in Multiple Shell High Angular Resolution.

Processing HARDI Data to Recover Crossing Fibers Maxime Descoteaux PhD student Advisor: Rachid Deriche Odyssée Laboratory, INRIA/ENPC/ENS, INRIA Sophia-Antipolis,

National Alliance for Medical Image Computing UNC: Quantitative DTI Analysis Guido Gerig, Isabelle Corouge Students: Casey Goodlett,

2004 All Hands Meeting Morphometry BIRN: Milestones for 2005 Jorge Jovicich PhD Steve Pieper, PhD David Kennedy, PhD.

Tract-Based Spatial Statistics of Diffusion Tensor Imaging in Adult Dyslexia Todd Richards 1, Jeff Stevenson 1, James Crouch 2, L. Clark Johnson 3, Kenneth.

Benoit Scherrer, ISBI 2011, Chicago Toward an accurate multi-fiber assessment strategy for clinical practice. Benoit Scherrer, Simon K. Warfield.

Jordan Hamm (BA, BSc) University of Georgia, Athens, Georgia Alexandra Reichenbach (MSc, Dipl-Ing) Max Planck Institute for Biological Cybernetics, Tuebingen,

Instructor Kwan-Jin Jung, Ph.D. (Carnegie Mellon University) Technical Assistant Nidhi Kohli (Carnegie Mellon University) David Schaeffer (University of.

NA-MIC National Alliance for Medical Image Computing Validation of DTI Analysis Guido Gerig, Clement Vachet, Isabelle Corouge, Casey.

DSim: An MR Diffusion Weighted Imaging Simulator Bob Dougherty Shyamsundar Gopalakrishnan Stanford University June 13, 2008.

Bayesian evidence for visualizing model selection uncertainty Gordon L. Kindlmann

Luke Bloy1, Ragini Verma2 The Section of Biomedical Image Analysis

Analytic ODF Reconstruction and Validation in Q-Ball Imaging Maxime Descoteaux 1 Work done with E. Angelino 2, S. Fitzgibbons 2, R. Deriche 1 1. Projet.

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.

Sharpening Improves Clinically Feasible Q-Ball Imaging Reconstructions

Dave Frank & Maggie Mahan

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

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 NA-MIC UNC Guido Gerig, Martin Styner, Isabelle Corouge

Multiplication Facts X 3 = 2. 8 x 4 = 3. 7 x 2 =

NA-MIC National Alliance for Medical Image Computing Mathematical and physical foundations of DTI Anastasia Yendiki, Ph.D. Massachusetts.

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

Subjects are registered to a template using affine transformations. These affine transformations are used to align the tracts passing through the splenium.

Measuring Water Diffusion In Biological Systems Using Nuclear Magnetic Resonance Karl Helmer HST 583, 2006

DTI Acquisition Guide Donald Brien February 2016.

Advanced fMRI Methods John VanMeter, Ph.D. Center for Functional and Molecular Imaging Georgetown University Medical Center.

1 Edge Operators a kind of filtering that leads to useful features.

Data analysis steps Pre-process images to reduce distortions

Diffusion Tensor MRI From Deterministic to Probabilistic Modelling

Mapping White Matter Connections in the Brain using Diffusion-Weighted Imaging and Tractography Andy Alexander Waisman Center Departments of Medical Physics.

resentations/E225/ Go to 51:47-1:06 mark listen for 9 minutes.

Surgical Planning Laboratory -1- Brigham and Women’s Hospital Slicer Tutorial 4 Module: DTMRI Data: Sample Slicer DTI Sonia Pujol,

Introduction to diffusion MRI

Introduction to diffusion MRI

SWI-informed Diffusion Tensor Tractography

Introduction to diffusion MRI

DGIST Department of Brain & Cognitive Sciences Hyeon-Ae Jeon

Human Brain Mapping Conference 2003 # 653

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

a kind of filtering that leads to useful features

a kind of filtering that leads to useful features

CYCLES IN BRAIN Kaushik Majumdar Systems Science and Informatics Unit

DTI Course Bring your computer and you will be guided through the steps to reconstruct the white matter fibers of the brain, inspect fibers reaching gray.

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

Presentation transcript:

High Angular Diffusion Imaging and its Visualization… Limitations of DTI Why HARDI is better?!? Different HARDI models My ideas and current work

Underlying philosophy in DTI S0S0 SiSi

From probability to diffusivity DTI: D(g) = D P(r) = Gaussian ~2µm 2 – 3 orders of magnitude difference 1-2mm 1 r 1 2 r 2 1 r 1 1 r 2

Application - DTI DTITool, BMIA group TU/e

What if… –Measure > 6(20) gradient directions –Give more time to the molecules to do their job What if… –Measure: gradient directions –Use high b-values: <2000s/mm 2 (w.r.t gradient strength and effective time) Different approaches = HARDI

HARDI Many different approaches –DSI, q-ball –High-order tensor models w.r.t. ADC –SH representation –PAS-MRI –Multi-compartment models etc.. All in common: avoid Gaussian model fitting

Reality check… Long (more complicated) acquisition scheme Popular for phantom data and simulations Tricky mathematical models Non-intuitive visualization

Reality check… Scanning time ~0.5h (and much more!) Phantoms like it – people don’t like it! Mathematical models: PDF = mixture of Gaussians

Reality check… Scanning time ~0.5h (and much more!) Phantoms like it – people don’t like it! Mathematical models: m SH representation of ADC

Reality check… Scanning time ~0.5h (and much more!) Phantoms like it – people don’t like it! Mathematical models: HOT representation of ADC

…and of course Visualization issues [Tuch, PhD Thesis 2002] [Ozarslan, MRM 2003] [Liu, MRM 2004 ]

My current work, ideas, struggles… Comparing most promising methods (w.r.t. feasibility on vivo data) and improve it –DOT and q-ball Answer the mysterious 42 question: “How high should be the “high” b-value?” DTI is not dead! Combining with HARDI. Define measure where 2 nd order tensor is sufficient! Segmentation on HARDI data.

More intuitive HARDI visualization => doctors don’t like glyphs Fiber tracking on HARDI Combining different modalities –Use of fMRI activation zones as seeding regions for white matter tractography My current work, ideas, struggles… [Hardenbergh, IEEE Vis 2005] [INRIA-McGill]

Multi-fieldity in HARDI Multiple measurements over same domain High-dimensional data High-order mathematical models (HOT and SH) Combining HARDI+fMRI =>Jorik Sufficient order w.r.t. encapsulated information => Stef