This Time. fmri Data analysis

Size: px
Start display at page:

Download "This Time. fmri Data analysis"

Transcription

1 This Time Reslice example Spatial Normalization Noise in fmri Methods for estimating and correcting for physiologic noise SPM Example Spatial Normalization: Remind ourselves what a typical functional image volume looks like: fmri Data analysis fmri Data analysis fmri time-series Reorient Slice order Unwarp Realignment Slice timing fmri time-series Realignment Normalisation fmri Data analysis Reorient Slice order Unwarp Slice timing Talairach Coordinate System to provide mechanism for comparing data across different labs/studies Talairach coordinate system (space) Atlas provided by Talairach and Tournoux Based on a French woman s brain Center (0,0,0 is on the anterior commissure) Line passes through the Anterior commissure Posterior commissure line (a.k.a., AC-PC line) Template 1

2 Talairach Coordinate System Individual brains are different shapes and sizes How can we compare or average brains? Rotate brain into ACPC plane Corpus Callosum Find anterior commisure (AC) Talairach & Tournoux, 1988 squish or stretch brain into shoe box extract 3D coordinate (x, y, z) for each activation focus Pineal Body bent asparagus Fornix Find posterior commisure (PC) ACPC line = horizontal axis Note: official Tal sez use top of AC and bottom of PC Source: Duvernoy, 1999 Note: That s TalAIRach, not TAILarach! Deform brain into Talairach space Mark 8 points in the brain: anterior commisure posterior commisure front back top bottom (of temporal lobe) left right Squish or stretch brain to fit in shoebox of Tal system y y<0 AC=0 y>0 z y>0 Extract 3 coordinates ACPC=0 y<0 x Talairach Atlas 2

3 Talairach Pros and Cons Left is what?!!! Advantages widespread system allows averaging of fmri data between subjects allows researchers to compare activation foci easy to use Disadvantages based on the squished brain of an elderly alcoholic woman (how representative is that?!) not appropriate for all brains (e.g., Japanese brains don t fit well) activation foci can vary considerably other landmarks like sulci may be more reliable Ignores left/right differences Neurologic (i.e. sensible) convention left is left, right is right L R - + x = 0 Radiologic (i.e. stupid) convention left is right, right is left R L Note: Make sure you know what your magnet and software are doing before publishing left/right info! Note: If you re really unsure which side is which, tape a vitamin E capsule to the one side of the subject s head. It will show up on the anatomical image. + x = 0 - Talairach vs MNI Talairach space Revised in spm96 using MNI brain Montreal Neurological Institute Based on 304 normal subjects More representative of population 10-15% larger than Talairach space Much confusion in the literature Must convert to Talairach to MNI space Meta-analyses Planning studies, regions of interest Different groups/software use different methods Manuscripts should specify the actual space used 3

4 Spatial Transformations: Warping Strategies Label-based (identifiable features) Identification of homologous structures (features, landmarks) between 2 images Find transformation that best superposes labeled points Non-label-based (no corresponding feature) Spatial transformation minimizing index of difference between images Spatial Normalization fmri time-series Mean functional EPI image Slice order Unwarp Realignment Slice timing Normalisation EPI template Structural image (T1) T1 template Template Spatial Normalization Spatial Normalization Mean functional image EPI template Coregister subject s mean EPI to structural T1 image Problems: - coreg is linear operation - EPI warping and - EPI distortion not accounted -Unless you have: field map correction -issues slow acquisition Structural image (T1) Normalize T1 to T1 Template T1 template Mean functional image EPI template Normalize mean EPI to EPI Template Structural image (T1) Good: - normalization has linear and nonlinear components - EPI warping and - EPI distortion accounted for -Even better if you have: field map correction -issues slow acquisition Bad If mean EPI is different from EPI template T1 template normalization can go wrong - tune normalization - customize basis functions - create site template Spatial Normalization Spatial Normalization Data must be resliced into new MNI (Talairach space) Data must be interpolated sinc most likely best Must choose voxel sizes to reslice into: Inputs were 3.44 x 3.44 x 5.00mm Default is 2.00 x 2.00 x 2.00 We use 3.00 x 3.00 x

5 Spatial Normalisation - Non-linear Deformations consist of a linear combination of smooth basis functions Deformation Field These are the lowest frequencies of a 3D discrete cosine transform (DCT) Algorithm simultaneously minimises * Mean squared difference between template and source image * Squared distance between parameters and their known expectation Original Deformation field Warped Template Jacobians Spatial Normalisation - Procedure Begin with affine registration Non-linear registration (about 1000 parameters) Jacobian Matrix (or just Jacobian ) Jacobian Determinant (or just Jacobian ) - relative volumes Affine registration Non-linear registration Affine vs Non-Rigid A Look at the transformation Affine vs Non-Rigid Affine Non-Rigid Affine 12 parameters Non-Rigid ~ 2000 parameters Average Anatomical Images from 10 Subjects displayed at 1.5x1.5x1.5 mm 5

6 What are typical SNRs for fmri data? Signal amplitude MR units: 5-10 units (baseline: ~700) Percent signal change: 0.5-2% Noise amplitude MR units: Percent signal change: 0.5-5% SNR range Total range: 0.1 to 4.0 Typical: Measured Effects of Field Strength SNR usually increases by less than theoretical prediction Sub-linear increases in SNR; large vessel effects may be independent of field strength Where tested, clear advantages of higher field have been demonstrated But, physiological noise may counteract gains at high field ( > ~4.0T) Spatial extent increases with field strength Increased susceptibility artifacts 6

7 Types of Noise Thermal noise Responsible for variation in background Eddy currents, scanner heating Power fluctuations Typically caused by scanner problems Variation in subject cognition Timing of processes Head motion effects Physiological changes Differences across brain regions Functional differences Large vessel effects Artifact-induced problems Variability in Subject Behavior: Issues Cognitive processes are not static May take time to engage Often variable across trials Subjects attention/arousal wax and wane Subjects adopt different strategies Feedback- or sequence-based Problem-solving methods Subjects engage in non-task cognition Non-task periods do not have the absence of thinking Signal Size in fmri Contrast-to-Noise-Ratio (SNR) A 45 B 50 E Task-Related Variability C D (50-45)/45 Non-task-related Variability Filtering Approaches Identify unwanted frequency variation Drift (low-frequency) Physiology (high-frequency) Task overlap (high-frequency) Reduce power around those frequencies through application of filters Potential problem: removal of frequencies composing response of interest Standard Deviation Image 7

8 Drift Drift (low freq) CSF flow, spontaneous fluctations Field (gradient!) Receive Freq Linear/Cubic/Spline correction Drift Data Acquisition Need high temporal sampling rate to avoid aliasing cardiac and respiratory-rate effects BOLD fluctuations near middle cerebral arteries respiration Cardiac Pulse oximeter Data Acquisition need enough images to have good spectral resolution (i.e. enough d.o.f. for statistical power) Example: Acquire 512 images with TR=200ms Data Analysis: Functional Connectivity Physiological Noise Nyquist Frequency=2.5Hz Cardiac Respiratory Movement Spectral resolution=~0.01hz Only 10 spectral d.o.f after filtering > 0.1Hz Periodic Hz Periodic Hz Transitory / unknown 8

9 Physiological noise: Examples Cardiac noise Data Analysis: Data-Driven Functional Connectivity analysis Physiologic Signals Cardiac ( Hz) Gating Physiologic monitoring Bandpass filter Respiratory (.1-.5 Hz) Navigator echo Respiratory noise Chuang et al. (2001) MRM 46: RETROICOR Method RETROICOR method, (Glover et al. (2000)) models the physiological noise as a basis set of sines and cosines. 9

10 10

11 11

12 12

13 Spatial Distribution of Noise A: Anatomical Image B: Noise image C: Physiological noise D: Motion-related noise E: Phantom (all noise) F: Phantom (Physiological) - Kruger & Glover (2001) 13

14 SPM Example Spatial Normalization 14

Last Time. This Time. Thru-plane dephasing: worse at long TE. Local susceptibility gradients: thru-plane dephasing

Last Time. This Time. Thru-plane dephasing: worse at long TE. Local susceptibility gradients: thru-plane dephasing Motion Correction Last Time Mutual Information Optimiation Decoupling Translation & Rotation Interpolation SPM Example (Least Squares & MI) A Simple Derivation This Time Reslice example SPM Example : Remind

More information

Functional MRI in Clinical Research and Practice Preprocessing

Functional MRI in Clinical Research and Practice Preprocessing Functional MRI in Clinical Research and Practice Preprocessing fmri Preprocessing Slice timing correction Geometric distortion correction Head motion correction Temporal filtering Intensity normalization

More information

SPM8 for Basic and Clinical Investigators. Preprocessing. fmri Preprocessing

SPM8 for Basic and Clinical Investigators. Preprocessing. fmri Preprocessing SPM8 for Basic and Clinical Investigators Preprocessing fmri Preprocessing Slice timing correction Geometric distortion correction Head motion correction Temporal filtering Intensity normalization Spatial

More information

EPI Data Are Acquired Serially. EPI Data Are Acquired Serially 10/23/2011. Functional Connectivity Preprocessing. fmri Preprocessing

EPI Data Are Acquired Serially. EPI Data Are Acquired Serially 10/23/2011. Functional Connectivity Preprocessing. fmri Preprocessing Functional Connectivity Preprocessing Geometric distortion Head motion Geometric distortion Head motion EPI Data Are Acquired Serially EPI Data Are Acquired Serially descending 1 EPI Data Are Acquired

More information

SPM8 for Basic and Clinical Investigators. Preprocessing

SPM8 for Basic and Clinical Investigators. Preprocessing SPM8 for Basic and Clinical Investigators Preprocessing fmri Preprocessing Slice timing correction Geometric distortion correction Head motion correction Temporal filtering Intensity normalization Spatial

More information

Basic fmri Design and Analysis. Preprocessing

Basic fmri Design and Analysis. Preprocessing Basic fmri Design and Analysis Preprocessing fmri Preprocessing Slice timing correction Geometric distortion correction Head motion correction Temporal filtering Intensity normalization Spatial filtering

More information

Introduction to fmri. Pre-processing

Introduction to fmri. Pre-processing Introduction to fmri Pre-processing Tibor Auer Department of Psychology Research Fellow in MRI Data Types Anatomical data: T 1 -weighted, 3D, 1/subject or session - (ME)MPRAGE/FLASH sequence, undistorted

More information

Journal of Articles in Support of The Null Hypothesis

Journal of Articles in Support of The Null Hypothesis Data Preprocessing Martin M. Monti, PhD UCLA Psychology NITP 2016 Typical (task-based) fmri analysis sequence Image Pre-processing Single Subject Analysis Group Analysis Journal of Articles in Support

More information

Functional MRI data preprocessing. Cyril Pernet, PhD

Functional MRI data preprocessing. Cyril Pernet, PhD Functional MRI data preprocessing Cyril Pernet, PhD Data have been acquired, what s s next? time No matter the design, multiple volumes (made from multiple slices) have been acquired in time. Before getting

More information

White Pixel Artifact. Caused by a noise spike during acquisition Spike in K-space <--> sinusoid in image space

White Pixel Artifact. Caused by a noise spike during acquisition Spike in K-space <--> sinusoid in image space White Pixel Artifact Caused by a noise spike during acquisition Spike in K-space sinusoid in image space Susceptibility Artifacts Off-resonance artifacts caused by adjacent regions with different

More information

fmri pre-processing Juergen Dukart

fmri pre-processing Juergen Dukart fmri pre-processing Juergen Dukart Outline Why do we need pre-processing? fmri pre-processing Slice time correction Realignment Unwarping Coregistration Spatial normalisation Smoothing Overview fmri time-series

More information

Fmri Spatial Processing

Fmri Spatial Processing Educational Course: Fmri Spatial Processing Ray Razlighi Jun. 8, 2014 Spatial Processing Spatial Re-alignment Geometric distortion correction Spatial Normalization Smoothing Why, When, How, Which Why is

More information

Computational Neuroanatomy

Computational Neuroanatomy Computational Neuroanatomy John Ashburner john@fil.ion.ucl.ac.uk Smoothing Motion Correction Between Modality Co-registration Spatial Normalisation Segmentation Morphometry Overview fmri time-series kernel

More information

Image Registration + Other Stuff

Image Registration + Other Stuff Image Registration + Other Stuff John Ashburner Pre-processing Overview fmri time-series Motion Correct Anatomical MRI Coregister m11 m 21 m 31 m12 m13 m14 m 22 m 23 m 24 m 32 m 33 m 34 1 Template Estimate

More information

Brain Extraction, Registration & EPI Distortion Correction

Brain Extraction, Registration & EPI Distortion Correction Brain Extraction, Registration & EPI Distortion Correction What use is Registration? Some common uses of registration: Combining across individuals in group studies: including fmri & diffusion Quantifying

More information

MRI Physics II: Gradients, Imaging

MRI Physics II: Gradients, Imaging MRI Physics II: Gradients, Imaging Douglas C., Ph.D. Dept. of Biomedical Engineering University of Michigan, Ann Arbor Magnetic Fields in MRI B 0 The main magnetic field. Always on (0.5-7 T) Magnetizes

More information

FMRI Pre-Processing and Model- Based Statistics

FMRI Pre-Processing and Model- Based Statistics FMRI Pre-Processing and Model- Based Statistics Brief intro to FMRI experiments and analysis FMRI pre-stats image processing Simple Single-Subject Statistics Multi-Level FMRI Analysis Advanced FMRI Analysis

More information

Transforming Datasets to Talairach-Tournoux Coordinates

Transforming Datasets to Talairach-Tournoux Coordinates -1- Transforming Datasets to Talairach-Tournoux Coordinates The original purpose of AFNI was to perform the transformation of datasets to Talairach-Tournoux (stereotaxic) coordinates The transformation

More information

fmri Image Preprocessing

fmri Image Preprocessing fmri Image Preprocessing Rick Hoge, Ph.D. Laboratoire de neuroimagerie vasculaire (LINeV) Centre de recherche de l institut universitaire de gériatrie de Montréal, Université de Montréal Outline Motion

More information

AFNI Preprocessing: Outline, Recommendations, and New(ish) Stuff. Robert W Cox SSCC / NIMH & NINDS / NIH / DHHS / USA / EARTH

AFNI Preprocessing: Outline, Recommendations, and New(ish) Stuff. Robert W Cox SSCC / NIMH & NINDS / NIH / DHHS / USA / EARTH AFNI Preprocessing: Outline, Recommendations, and New(ish) Stuff Robert W Cox SSCC / NIMH & NINDS / NIH / DHHS / USA / EARTH HBM 2016 As a work of a US Government official, this presentation is not copyrighted

More information

2. Creating Field Maps Using the Field Map GUI (Version 2.0) in SPM5

2. Creating Field Maps Using the Field Map GUI (Version 2.0) in SPM5 1. Introduction This manual describes how to use the Field Map Toolbox Version 2.0 for creating unwrapped field maps that can be used to do geometric distortion correction of EPI images in SPM5. 1. 1.

More information

fmri Preprocessing & Noise Modeling

fmri Preprocessing & Noise Modeling Translational Neuromodeling Unit fmri Preprocessing & Noise Modeling Lars Kasper September 25 th / October 17 th, 2015 MR-Technology Group & Translational Neuromodeling Unit An SPM Tutorial Institute for

More information

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008 MIT OpenCourseWare http://ocw.mit.edu HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

Spatial Preprocessing

Spatial Preprocessing Spatial Preprocessing Overview of SPM Analysis fmri time-series Design matrix Statistical Parametric Map John Ashburner john@fil.ion.ucl.ac.uk Motion Correction Smoothing General Linear Model Smoothing

More information

Supplementary methods

Supplementary methods Supplementary methods This section provides additional technical details on the sample, the applied imaging and analysis steps and methods. Structural imaging Trained radiographers placed all participants

More information

Statistical Analysis of Neuroimaging Data. Phebe Kemmer BIOS 516 Sept 24, 2015

Statistical Analysis of Neuroimaging Data. Phebe Kemmer BIOS 516 Sept 24, 2015 Statistical Analysis of Neuroimaging Data Phebe Kemmer BIOS 516 Sept 24, 2015 Review from last time Structural Imaging modalities MRI, CAT, DTI (diffusion tensor imaging) Functional Imaging modalities

More information

Preprocessing of fmri Data in SPM 12 - Lab 1

Preprocessing of fmri Data in SPM 12 - Lab 1 Preprocessing of fmri Data in SPM 12 - Lab 1 Index Goals of this Lab Preprocessing Overview MATLAB, SPM, Data Setup Preprocessing I: Checking Motion Correction Preprocessing II: Coregistration Preprocessing

More information

Diffusion MRI Acquisition. Karla Miller FMRIB Centre, University of Oxford

Diffusion MRI Acquisition. Karla Miller FMRIB Centre, University of Oxford Diffusion MRI Acquisition Karla Miller FMRIB Centre, University of Oxford karla@fmrib.ox.ac.uk Diffusion Imaging How is diffusion weighting achieved? How is the image acquired? What are the limitations,

More information

Surface-based Analysis: Inter-subject Registration and Smoothing

Surface-based Analysis: Inter-subject Registration and Smoothing Surface-based Analysis: Inter-subject Registration and Smoothing Outline Exploratory Spatial Analysis Coordinate Systems 3D (Volumetric) 2D (Surface-based) Inter-subject registration Volume-based Surface-based

More information

SPM Introduction. SPM : Overview. SPM: Preprocessing SPM! SPM: Preprocessing. Scott Peltier. FMRI Laboratory University of Michigan

SPM Introduction. SPM : Overview. SPM: Preprocessing SPM! SPM: Preprocessing. Scott Peltier. FMRI Laboratory University of Michigan SPM Introduction Scott Peltier FMRI Laboratory University of Michigan! Slides adapted from T. Nichols SPM! SPM : Overview Library of MATLAB and C functions Graphical user interface Four main components:

More information

Noise and Artifacts in FMRI

Noise and Artifacts in FMRI Noise and Artifacts in FMRI Instructor: Luis Hernandez-Garcia, Ph.D. Associate Research Professor FMRI Laboratory, Biomedical Engineering FMRI analysis - synopsis of what you ll do next week 1. Formulate

More information

Functional MRI. Jerry Allison, Ph. D. Medical College of Georgia

Functional MRI. Jerry Allison, Ph. D. Medical College of Georgia Functional MRI Jerry Allison, Ph. D. Medical College of Georgia BOLD Imaging Technique Blood Oxygen Level Dependent contrast can be used to map brain function Right Hand Motor Task Outline fmri BOLD Contrast

More information

Analysis of fmri data within Brainvisa Example with the Saccades database

Analysis of fmri data within Brainvisa Example with the Saccades database Analysis of fmri data within Brainvisa Example with the Saccades database 18/11/2009 Note : All the sentences in italic correspond to informations relative to the specific dataset under study TP participants

More information

SPM Introduction SPM! Scott Peltier. FMRI Laboratory University of Michigan. Software to perform computation, manipulation and display of imaging data

SPM Introduction SPM! Scott Peltier. FMRI Laboratory University of Michigan. Software to perform computation, manipulation and display of imaging data SPM Introduction Scott Peltier FMRI Laboratory University of Michigan Slides adapted from T. Nichols SPM! Software to perform computation, manipulation and display of imaging data 1 1 SPM : Overview Library

More information

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

The organization of the human cerebral cortex estimated by intrinsic functional connectivity 1 The organization of the human cerebral cortex estimated by intrinsic functional connectivity Journal: Journal of Neurophysiology Author: B. T. Thomas Yeo, et al Link: https://www.ncbi.nlm.nih.gov/pubmed/21653723

More information

FROM IMAGE RECONSTRUCTION TO CONNECTIVITY ANALYSIS: A JOURNEY THROUGH THE BRAIN'S WIRING. Francesca Pizzorni Ferrarese

FROM IMAGE RECONSTRUCTION TO CONNECTIVITY ANALYSIS: A JOURNEY THROUGH THE BRAIN'S WIRING. Francesca Pizzorni Ferrarese FROM IMAGE RECONSTRUCTION TO CONNECTIVITY ANALYSIS: A JOURNEY THROUGH THE BRAIN'S WIRING Francesca Pizzorni Ferrarese Pipeline overview WM and GM Segmentation Registration Data reconstruction Tractography

More information

SPM99 fmri Data Analysis Workbook

SPM99 fmri Data Analysis Workbook SPM99 fmri Data Analysis Workbook This file is a description of the steps needed to use SPM99 analyze a fmri data set from a single subject using a simple on/off activation paradigm. There are two parts

More information

FSL Pre-Processing Pipeline

FSL Pre-Processing Pipeline The Art and Pitfalls of fmri Preprocessing FSL Pre-Processing Pipeline Mark Jenkinson FMRIB Centre, University of Oxford FSL Pre-Processing Pipeline Standard pre-processing: Task fmri Resting-state fmri

More information

Function-Structure Integration in FreeSurfer

Function-Structure Integration in FreeSurfer Function-Structure Integration in FreeSurfer Outline Function-Structure Integration Function-Structure Registration in FreeSurfer fmri Analysis Preprocessing First-Level Analysis Higher-Level (Group) Analysis

More information

Basic principles of MR image analysis. Basic principles of MR image analysis. Basic principles of MR image analysis

Basic principles of MR image analysis. Basic principles of MR image analysis. Basic principles of MR image analysis Basic principles of MR image analysis Basic principles of MR image analysis Julien Milles Leiden University Medical Center Terminology of fmri Brain extraction Registration Linear registration Non-linear

More information

Learning-based Neuroimage Registration

Learning-based Neuroimage Registration Learning-based Neuroimage Registration Leonid Teverovskiy and Yanxi Liu 1 October 2004 CMU-CALD-04-108, CMU-RI-TR-04-59 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract

More information

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2006

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2006 MIT OpenCourseWare http://ocw.mit.edu HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2006 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

CS/NEUR125 Brains, Minds, and Machines. Due: Wednesday, April 5

CS/NEUR125 Brains, Minds, and Machines. Due: Wednesday, April 5 CS/NEUR125 Brains, Minds, and Machines Lab 8: Using fmri to Discover Language Areas in the Brain Due: Wednesday, April 5 In this lab, you will analyze fmri data from an experiment that was designed to

More information

Role of Parallel Imaging in High Field Functional MRI

Role of Parallel Imaging in High Field Functional MRI Role of Parallel Imaging in High Field Functional MRI Douglas C. Noll & Bradley P. Sutton Department of Biomedical Engineering, University of Michigan Supported by NIH Grant DA15410 & The Whitaker Foundation

More information

Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking

Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking NeuroImage 14, 486 500 (2001) doi:10.1006/nimg.2001.0845, available online at http://www.idealibrary.com on Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking Matthew

More information

ASAP_2.0 (Automatic Software for ASL Processing) USER S MANUAL

ASAP_2.0 (Automatic Software for ASL Processing) USER S MANUAL ASAP_2.0 (Automatic Software for ASL Processing) USER S MANUAL ASAP was developed as part of the COST Action "Arterial Spin Labelling Initiative in Dementia (AID)" by: Department of Neuroimaging, Institute

More information

Introduction to Neuroimaging Janaina Mourao-Miranda

Introduction to Neuroimaging Janaina Mourao-Miranda Introduction to Neuroimaging Janaina Mourao-Miranda Neuroimaging techniques have changed the way neuroscientists address questions about functional anatomy, especially in relation to behavior and clinical

More information

Image Processing for fmri John Ashburner. Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK.

Image Processing for fmri John Ashburner. Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK. Iage Processing for fmri John Ashburner Wellcoe Trust Centre for Neuroiaging, 12 Queen Square, London, UK. Contents * Preliinaries * Rigid-Body and Affine Transforations * Optiisation and Objective Functions

More information

Table of Contents. IntroLab < SPMLabs < Dynevor TWiki

Table of Contents. IntroLab < SPMLabs < Dynevor TWiki Table of Contents Lab 1: Introduction to SPM and data checking...1 Goals of this Lab...1 Prerequisites...1 An SPM Installation...1 SPM Defaults...2 L/R Brain Orientation...2 Memory Use for Data Processing...2

More information

CORRECTION OF IMAGE DISTORTION IN ECHO PLANAR IMAGE SERIES USING PHASE AND INTENSITY. Ning Xu. Dissertation. Submitted to the Faculty of the

CORRECTION OF IMAGE DISTORTION IN ECHO PLANAR IMAGE SERIES USING PHASE AND INTENSITY. Ning Xu. Dissertation. Submitted to the Faculty of the CORRECTION OF IMAGE DISTORTION IN ECHO PLANAR IMAGE SERIES USING PHASE AND INTENSITY By Ning Xu Dissertation Submitted to the Faculty of the Graduate School of Vanderbilt University in partial fulfillment

More information

Slide 1. Technical Aspects of Quality Control in Magnetic Resonance Imaging. Slide 2. Annual Compliance Testing. of MRI Systems.

Slide 1. Technical Aspects of Quality Control in Magnetic Resonance Imaging. Slide 2. Annual Compliance Testing. of MRI Systems. Slide 1 Technical Aspects of Quality Control in Magnetic Resonance Imaging Slide 2 Compliance Testing of MRI Systems, Ph.D. Department of Radiology Henry Ford Hospital, Detroit, MI Slide 3 Compliance Testing

More information

Data pre-processing framework in SPM. Bogdan Draganski

Data pre-processing framework in SPM. Bogdan Draganski Data pre-processing fraework in SPM Bogdan Draganski Outline Why do we need pre-processing? Overview Structural MRI pre-processing fmri pre-processing Why do we need pre-processing? What do we want? Reason

More information

Analysis of Functional MRI Timeseries Data Using Signal Processing Techniques

Analysis of Functional MRI Timeseries Data Using Signal Processing Techniques Analysis of Functional MRI Timeseries Data Using Signal Processing Techniques Sea Chen Department of Biomedical Engineering Advisors: Dr. Charles A. Bouman and Dr. Mark J. Lowe S. Chen Final Exam October

More information

Cocozza S., et al. : ALTERATIONS OF FUNCTIONAL CONNECTIVITY OF THE MOTOR CORTEX IN FABRY'S DISEASE: AN RS-FMRI STUDY

Cocozza S., et al. : ALTERATIONS OF FUNCTIONAL CONNECTIVITY OF THE MOTOR CORTEX IN FABRY'S DISEASE: AN RS-FMRI STUDY ALTERATIONS OF FUNCTIONAL CONNECTIVITY OF THE MOTOR CORTEX IN FABRY'S DISEASE: AN RS-FMRI STUDY SUPPLEMENTARY MATERIALS Sirio Cocozza, MD 1*, Antonio Pisani, MD, PhD 2, Gaia Olivo, MD 1, Francesco Saccà,

More information

Artifact detection and repair in fmri

Artifact detection and repair in fmri Artifact detection and repair in fmri Paul K. Mazaika, Ph.D. Center for Interdisciplinary Brain Sciences Research (CIBSR) Division of Interdisciplinary Behavioral Sciences Stanford University School of

More information

Playing with data from lab

Playing with data from lab Playing with data from lab Getting data off the scanner From the Patient Browser, select the folder for the study you want (or within that study, the set of images you want), and then from the Transfer

More information

FSL Pre-Processing Pipeline

FSL Pre-Processing Pipeline The Art and Pitfalls of fmri Preprocessing FSL Pre-Processing Pipeline Mark Jenkinson FMRIB Centre, University of Oxford FSL Pre-Processing Pipeline Standard pre-processing: Task fmri Resting-state fmri

More information

Preprocessing II: Between Subjects John Ashburner

Preprocessing II: Between Subjects John Ashburner Preprocessing II: Between Subjects John Ashburner Pre-processing Overview Statistics or whatever fmri time-series Anatomical MRI Template Smoothed Estimate Spatial Norm Motion Correct Smooth Coregister

More information

Motion Correction in fmri by Mapping Slice-to-Volume with Concurrent Field-Inhomogeneity Correction

Motion Correction in fmri by Mapping Slice-to-Volume with Concurrent Field-Inhomogeneity Correction Motion Correction in fmri by Mapping Slice-to-Volume with Concurrent Field-Inhomogeneity Correction Desmond T.B. Yeo 1,2, Jeffery A. Fessler 2, and Boklye Kim 1 1 Department of Radiology, University of

More information

fmri Analysis Sackler Ins2tute 2011

fmri Analysis Sackler Ins2tute 2011 fmri Analysis Sackler Ins2tute 2011 How do we get from this to this? How do we get from this to this? And what are those colored blobs we re all trying to see, anyway? Raw fmri data straight from the scanner

More information

Single Subject Demo Data Instructions 1) click "New" and answer "No" to the "spatially preprocess" question.

Single Subject Demo Data Instructions 1) click New and answer No to the spatially preprocess question. (1) conn - Functional connectivity toolbox v1.0 Single Subject Demo Data Instructions 1) click "New" and answer "No" to the "spatially preprocess" question. 2) in "Basic" enter "1" subject, "6" seconds

More information

Locating Motion Artifacts in Parametric fmri Analysis

Locating Motion Artifacts in Parametric fmri Analysis Tina Memo No. 200-002 Presented at MICCAI 999 Locating Motion Artifacts in Parametric fmri Analysis A.J.Lacey, N.A.Thacker, E. Burton, and A.Jackson Last updated 2 / 02 / 2002 Imaging Science and Biomedical

More information

The simulator can be applied in a number of diverse applications which span both

The simulator can be applied in a number of diverse applications which span both Chapter 6 Simulator applications The simulator can be applied in a number of diverse applications which span both MRI and FMRI fields These applications include the simulation and removal of various imaging

More information

Sources of Distortion in Functional MRI Data

Sources of Distortion in Functional MRI Data Human Brain Mapping 8:80 85(1999) Sources of Distortion in Functional MRI Data Peter Jezzard* and Stuart Clare FMRIB Centre, Department of Clinical Neurology, University of Oxford, Oxford, UK Abstract:

More information

Group analysis of fmri data. Why group analysis? What is a population? Reminder: Analysis of data from 1 person. Robert: Roel Willems,

Group analysis of fmri data. Why group analysis? What is a population? Reminder: Analysis of data from 1 person. Robert: Roel Willems, Reminder: Analysis of data from person Group analysis of fmri data Robert: Roel Willems, FC Donders Centre Nijmegen roel.willems@fcdonders.ru.nl Why group analysis? Results from Robert only apply to Robert

More information

Manual image registration in BrainVoyager QX Table of Contents

Manual image registration in BrainVoyager QX Table of Contents Manual image registration in BrainVoyager QX Table of Contents Manual image registration in BrainVoyager QX......1 Performing manual alignment for functional to anatomical images......2 Step 1: preparation......2

More information

Spatial transformations in BrainVoyager

Spatial transformations in BrainVoyager Spatial transformations in BrainVoyager Rainer Goebel 2005 Introduction This technical document aims to provide detailed knowledge about spatial transformations in general and how they are implemented

More information

Nonrigid Registration using Free-Form Deformations

Nonrigid Registration using Free-Form Deformations Nonrigid Registration using Free-Form Deformations Hongchang Peng April 20th Paper Presented: Rueckert et al., TMI 1999: Nonrigid registration using freeform deformations: Application to breast MR images

More information

Methods for data preprocessing

Methods for data preprocessing Methods for data preprocessing John Ashburner Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK. Overview Voxel-Based Morphometry Morphometry in general Volumetrics VBM preprocessing

More information

Module 4. K-Space Symmetry. Review. K-Space Review. K-Space Symmetry. Partial or Fractional Echo. Half or Partial Fourier HASTE

Module 4. K-Space Symmetry. Review. K-Space Review. K-Space Symmetry. Partial or Fractional Echo. Half or Partial Fourier HASTE MRES 7005 - Fast Imaging Techniques Module 4 K-Space Symmetry Review K-Space Review K-Space Symmetry Partial or Fractional Echo Half or Partial Fourier HASTE Conditions for successful reconstruction Interpolation

More information

Head motion in diffusion MRI

Head motion in diffusion MRI Head motion in diffusion MRI Anastasia Yendiki HMS/MGH/MIT Athinoula A. Martinos Center for Biomedical Imaging 11/06/13 Head motion in diffusion MRI 0/33 Diffusion contrast Basic principle of diffusion

More information

Supplementary Figure 1

Supplementary Figure 1 Supplementary Figure 1 BOLD and CBV functional maps showing EPI versus line-scanning FLASH fmri. A. Colored BOLD and CBV functional maps are shown in the highlighted window (green frame) of the raw EPI

More information

Clinical Importance. Aortic Stenosis. Aortic Regurgitation. Ultrasound vs. MRI. Carotid Artery Stenosis

Clinical Importance. Aortic Stenosis. Aortic Regurgitation. Ultrasound vs. MRI. Carotid Artery Stenosis Clinical Importance Rapid cardiovascular flow quantitation using sliceselective Fourier velocity encoding with spiral readouts Valve disease affects 10% of patients with heart disease in the U.S. Most

More information

K-Space Trajectories and Spiral Scan

K-Space Trajectories and Spiral Scan K-Space and Spiral Scan Presented by: Novena Rangwala nrangw2@uic.edu 1 Outline K-space Gridding Reconstruction Features of Spiral Sampling Pulse Sequences Mathematical Basis of Spiral Scanning Variations

More information

Preprocessing I: Within Subject John Ashburner

Preprocessing I: Within Subject John Ashburner Preprocessing I: Within Subject John Ashburner Pre-processing Overview Statistics or whatever fmri tie-series Anatoical MRI Teplate Soothed Estiate Spatial Nor Motion Correct Sooth Coregister 11 21 31

More information

Evaluation of multiple voxel-based morphometry approaches and applications in the analysis of white matter changes in temporal lobe epilepsy

Evaluation of multiple voxel-based morphometry approaches and applications in the analysis of white matter changes in temporal lobe epilepsy Evaluation of multiple voxel-based morphometry approaches and applications in the analysis of white matter changes in temporal lobe epilepsy Wenjing Li a, Huiguang He a, Jingjing Lu b, Bin Lv a, Meng Li

More information

Measuring longitudinal brain changes in humans and small animal models. Christos Davatzikos

Measuring longitudinal brain changes in humans and small animal models. Christos Davatzikos Measuring longitudinal brain changes in humans and small animal models Christos Davatzikos Section of Biomedical Image Analysis University of Pennsylvania (Radiology) http://www.rad.upenn.edu/sbia Computational

More information

Following on from the two previous chapters, which considered the model of the

Following on from the two previous chapters, which considered the model of the Chapter 5 Simulator validation Following on from the two previous chapters, which considered the model of the simulation process and how this model was implemented in software, this chapter is concerned

More information

An Evaluation of the Use of Magnetic Field Maps to Undistort Echo-Planar Images

An Evaluation of the Use of Magnetic Field Maps to Undistort Echo-Planar Images NeuroImage 18, 127 142 (2003) doi:10.1006/nimg.2002.1281 An Evaluation of the Use of Magnetic Field Maps to Undistort Echo-Planar Images Rhodri Cusack, Matthew Brett, and Katja Osswald MRC Cognition and

More information

Image Acquisition Systems

Image Acquisition Systems Image Acquisition Systems Goals and Terminology Conventional Radiography Axial Tomography Computer Axial Tomography (CAT) Magnetic Resonance Imaging (MRI) PET, SPECT Ultrasound Microscopy Imaging ITCS

More information

Lab Location: MRI, B2, Cardinal Carter Wing, St. Michael s Hospital, 30 Bond Street

Lab Location: MRI, B2, Cardinal Carter Wing, St. Michael s Hospital, 30 Bond Street Lab Location: MRI, B2, Cardinal Carter Wing, St. Michael s Hospital, 30 Bond Street MRI is located in the sub basement of CC wing. From Queen or Victoria, follow the baby blue arrows and ride the CC south

More information

A Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction

A Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction Tina Memo No. 2007-003 Published in Proc. MIUA 2007 A Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction P. A. Bromiley and N.A. Thacker Last updated 13 / 4 / 2007 Imaging Science and

More information

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008 MIT OpenCourseWare http://ocw.mit.edu HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

1 Introduction Motivation and Aims Functional Imaging Computational Neuroanatomy... 12

1 Introduction Motivation and Aims Functional Imaging Computational Neuroanatomy... 12 Contents 1 Introduction 10 1.1 Motivation and Aims....... 10 1.1.1 Functional Imaging.... 10 1.1.2 Computational Neuroanatomy... 12 1.2 Overview of Chapters... 14 2 Rigid Body Registration 18 2.1 Introduction.....

More information

NORTHEASTERN UNIVERSITY

NORTHEASTERN UNIVERSITY 1 NORTHEASTERN UNIVERSITY Graduate School of Engineering Project Title: Investigation of a Volumetric Technique for Registration of Magnetic Resonance Images of the Human Brain. Author: Patrick K. Rennich.

More information

Preprocessing of fmri data

Preprocessing of fmri data Preprocessing of fmri data Pierre Bellec CRIUGM, DIRO, UdM Flowchart of the NIAK fmri preprocessing pipeline fmri run 1 fmri run N individual datasets CIVET NUC, segmentation, spatial normalization slice

More information

Normalization for clinical data

Normalization for clinical data Normalization for clinical data Christopher Rorden, Leonardo Bonilha, Julius Fridriksson, Benjamin Bender, Hans-Otto Karnath (2012) Agespecific CT and MRI templates for spatial normalization. NeuroImage

More information

Human Connectom Project : The minimal processing Pipeline

Human Connectom Project : The minimal processing Pipeline Human Connectom Project : The minimal processing Pipeline Human Connectom Project : The minimal processing Pipeline Van Essen DC, The WU-Minn Human Connectome Project: an overview. Neuroimage. 2013 Marcus

More information

Chapter 3 Set Redundancy in Magnetic Resonance Brain Images

Chapter 3 Set Redundancy in Magnetic Resonance Brain Images 16 Chapter 3 Set Redundancy in Magnetic Resonance Brain Images 3.1 MRI (magnetic resonance imaging) MRI is a technique of measuring physical structure within the human anatomy. Our proposed research focuses

More information

GLM for fmri data analysis Lab Exercise 1

GLM for fmri data analysis Lab Exercise 1 GLM for fmri data analysis Lab Exercise 1 March 15, 2013 Medical Image Processing Lab Medical Image Processing Lab GLM for fmri data analysis Outline 1 Getting Started 2 AUDIO 1 st level Preprocessing

More information

XI Signal-to-Noise (SNR)

XI Signal-to-Noise (SNR) XI Signal-to-Noise (SNR) Lecture notes by Assaf Tal n(t) t. Noise. Characterizing Noise Noise is a random signal that gets added to all of our measurements. In D it looks like this: while in D

More information

Morphological Analysis of Brain Structures Using Spatial Normalization

Morphological Analysis of Brain Structures Using Spatial Normalization Morphological Analysis of Brain Structures Using Spatial Normalization C. Davatzikos 1, M. Vaillant 1, S. Resnick 2, J.L. Prince 3;1, S. Letovsky 1, and R.N. Bryan 1 1 Department of Radiology, Johns Hopkins

More information

Automatic segmentation of the cortical grey and white matter in MRI using a Region Growing approach based on anatomical knowledge

Automatic segmentation of the cortical grey and white matter in MRI using a Region Growing approach based on anatomical knowledge Automatic segmentation of the cortical grey and white matter in MRI using a Region Growing approach based on anatomical knowledge Christian Wasserthal 1, Karin Engel 1, Karsten Rink 1 und André Brechmann

More information

Noise Models and Correction for fmri

Noise Models and Correction for fmri Translational Neuromodeling Unit Noise Models and Correction for fmri Lars Kasper Nov 21 th, 2017 - an Introduction to the PhysIO Toolbox MR-Technology and Methods Group & Translational Neuromodeling Unit

More information

Lucy Phantom MR Grid Evaluation

Lucy Phantom MR Grid Evaluation Lucy Phantom MR Grid Evaluation Anil Sethi, PhD Loyola University Medical Center, Maywood, IL 60153 November 2015 I. Introduction: The MR distortion grid, used as an insert with Lucy 3D QA phantom, is

More information

CHAPTER 9: Magnetic Susceptibility Effects in High Field MRI

CHAPTER 9: Magnetic Susceptibility Effects in High Field MRI Figure 1. In the brain, the gray matter has substantially more blood vessels and capillaries than white matter. The magnified image on the right displays the rich vasculature in gray matter forming porous,

More information

Appendix E1. Supplementary Methods. MR Image Acquisition. MR Image Analysis

Appendix E1. Supplementary Methods. MR Image Acquisition. MR Image Analysis RSNA, 2015 10.1148/radiol.2015150532 Appendix E1 Supplementary Methods MR Image Acquisition By using a 1.5-T system (Avanto, Siemens Medical, Erlangen, Germany) under a program of regular maintenance (no

More information

8/11/2009. Common Areas of Motion Problem. Motion Compensation Techniques and Applications. Type of Motion. What s your problem

8/11/2009. Common Areas of Motion Problem. Motion Compensation Techniques and Applications. Type of Motion. What s your problem Common Areas of Motion Problem Motion Compensation Techniques and Applications Abdominal and cardiac imaging. Uncooperative patient, such as pediatric. Dynamic imaging and time series. Chen Lin, PhD Indiana

More information

ANALYSIS OF PULMONARY FIBROSIS IN MRI, USING AN ELASTIC REGISTRATION TECHNIQUE IN A MODEL OF FIBROSIS: Scleroderma

ANALYSIS OF PULMONARY FIBROSIS IN MRI, USING AN ELASTIC REGISTRATION TECHNIQUE IN A MODEL OF FIBROSIS: Scleroderma ANALYSIS OF PULMONARY FIBROSIS IN MRI, USING AN ELASTIC REGISTRATION TECHNIQUE IN A MODEL OF FIBROSIS: Scleroderma ORAL DEFENSE 8 th of September 2017 Charlotte MARTIN Supervisor: Pr. MP REVEL M2 Bio Medical

More information

An Introduction to Image Reconstruction, Processing, and their Effects in FMRI

An Introduction to Image Reconstruction, Processing, and their Effects in FMRI An Introduction to Image Reconstruction, Processing, and their Effects in FMRI Daniel B. Rowe Program in Computational Sciences Department of Mathematics, Statistics, and Computer Science Marquette University

More information