Biomedical Imaging

1
Biomedical Imaging

• 100 million Americans undergo diagnostic imaging
  procedures every year. Lately functional imaging
  is added as a new routine clinical imaging
  procedure.
• There will be a bigger expansion in biomedical
  imaging technology in the 21st century.
• There is a growing demand in developing new
  imaging instruments, novel imaging procedures,
  and data processing.
• The Decade of the Brain signed by President
  Bush, 1990-1999.
• The National Institute for Biomedical Imaging
  and Bioengineering (NIBIB) signed by President
  Clinton, 2000.

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Medical Imaging

• Anatomical Imaging
• Vs.
• Functional Imaging

3
Brain Imaging Techniques

• Michasel I. Posner and Marcus E. Raichle, Images
  of Mind, New York, Scientific American Library,
  1997

4
Functional Brain Imaging

• Anatomical Imaging Ever since imaging
• Functional Imaging last 10 years
• 3 Requirements for Functional Imaging
• Scanner (Camera) Scanner, Coils etc
• Data Analysis Signal Image Processing
• Experimental Protocol (Neuroscience)

Primary Motor Area
First-order motion
V5
MEG
fMRI
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How the Brain Works

• Functional Segregation
• Functional Integration

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Functional Brain Imaging
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PET SPECT

• Positron Emission Tomography (PET) and SPECT
• Regional Cerebral Blood Flow
• Regional Cerebral Glucose Metabolism

PET Language Functions
8
Functional MRI

• Principles
• Processing Methods
• Problems and Solutions
• Applications

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fMRI Experimental Setup
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Spatiotemporal Resolution

• Spatial Resolution
• Speed of image acquisition
• Image signal/noise (SNR)
• Magnetic field gradient strength
• Static magnetic field
• Effective spatial resolution of the BOLD effect
  3mm
• Temporal Resolution
• Number of slices acquired
• Slow blood flow changes
• Effective temporal resolution a few seconds

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Echo Planar Imaging (EPI)

• High-speed imaging
• First proposed by Mansfield.
• After one or multiple rf excitation (single-shot
  EPI or multi-shot EPI), a series of gradient
  pulse is followed, thus MR signals are created by
  a series of gradient reversals or oscillations.
• Allows imaging of multiple slices in reasonable
  short time.
• fMR images are acquired with gradient echo EPI
  sequences.

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Effective Transverse Relaxation Time, T2

• T2 spin-spin relaxation time
• The time to reduce the transverse magnetization
• Pure T2 is a molecular effect
• Effective decay of transverse magnetization
• Molecular interactions (pure T2)
• Magnetic inhomogeneity (T2,inhomogeneity)
• 1/T21/T21/T2,inhomogeneity
• 1/T21/T2??H/2

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T2 Difference in BOLD fMRI
Signal intensity
MR Signal Intensity ? Tesla
activated
resting
13 at 1.5T
Time
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Neuro-Physiology
EEG
fMRI
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fMRI BOLD

• Sensory, motor, or cognitive task ? Localized
  increase in neural activity ? Increased metabolic
  rate ? Local vasodilation ? Increased blood
  volume ltlt Increased blood flow (50) gtgt metabolic
  rate increase (5) ? Decreased ratio
  deoxy-Hb/oxy-Hb ? Less spin dephasing from
  magnetic inhomogeneities caused by deoxy-Hb ?
  Increase in T2 ? Increased MR signal.
• Deoxy-Hb paramagnetic
• Oxy-Hb diamagnetic

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Anatomical MRI vs. Functional MRI
Spin-echo Gradient-echo
Spin-echo Gradient-echo
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fMR Data Analysis

• Movement Correction
• Activaty Detection
• Model-driven Processing
• Data-driven Processing

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Time-series Images
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Motion Correction

• Subjects always move in the scanner.
• If correlated with stimulation, artifacts show up
  as activated regions.
• Registration
• Software AIR by Wood et al.
• Motion models
• translation-rotation, polynomials, etc
• Coregistration costs
• Transformation

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fMRI Data Processing

• Head/Brain Motion Correction
• Image Registration Software
• N-th Order Polynomials
• Voxel Similarity Measures
• Mean Squared Difference (MSD)
• Pearson product-moment cross correlation (NCC)
• Mutual Information (MI)
• Normalized Mutual Information (NMI)
• Entropy of Difference Image (EDI)
• Modified Pattern Intensity (MPP)
• Ratio Image Uniformity (RIU)
• Modified Ratio Image Uniformity (MRIU)
• Brain Activity Detection
• Model-Driven Processing
• Data-Driven Processing

t1 sec
t2 sec
T.-S. Kim et al., IEEE TNS Nucl. Med. Img.
Sci. (2000) Mag. Res. Med (2002)
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fMRI Model-driven Processing

• Reference Function

LCC
SPM
(cross correlation)
(general linear model)
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Cross-Correlation Thresholding

• Linear Correlation Coefficient (LCC)

Activation Map
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Statistical Parametric Mapping

• SPM
• A voxel by voxel hypothesis testing approach
• Developed at The Wellcome Department of Cognitive
  Neurology Institute of Neurology, University
  College London
• http//www.fil.ion.ucl.ac.uk/spm/

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The Basics of SPM
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Data-Driven Processing

• No Reference Function ? No assumption of brain
  activity
• Why
• May reveal brain activity that cannot be detected
  with model-driven methods
• Clustering
• Principle Component Analysis (PCA)
• Independent Component Analysis (ICA)
• Blind Source Separation Problem
• BS Infomax (Bell Sejnowski, 1995)
• FastICA (Hyvärinen, 1999)
• Mixture Density ICA (J. Jeong T.-S. Kim, 2002
  Xu. L, 1997)

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Typical Experimental Designs

• Block (or Steady-State) fMRI
• Measure evoked hemodynamic responses due to
  multiple stimuli or events.

• Event fMRI
• Measure an evoked hemodynamic response due to a
  single stimulus or event.

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Block fMRI

• Voluntary self-paced right index finger flex
• Linear cross correlation (cc) analysis
• Threshold ccgt0.33

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Event fMRI

• Finger Flexing

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ApplicationSpatiotemporal Localization of Alpha
Activity Source in fMRI and EEG Using Mixture
Density ICA
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Independent Component Analysis (ICA)

• The goal of blind source separation in signal
  processing is to recover independent source
  signals (e.g., different people speaking, music
  etc.) after they are linearly mixed by an unknown
  medium, and recorded at N sensors (e.g.,
  microphones).
• The concept of independent component analysis
  (ICA) as maximizing the degree of statistical
  independence among outputs using contrast
  functions. In contrast with decorrelation
  techniques such as Principal Component Analysis
  (PCA), which ensures that output pairs are
  uncorrelated. ICA imposes the much stronger
  criterion that the multivariate probability
  density function (p.d.f.) of output variables
• Finding such a factorization requires that the
  mutual information between all variable pairs go
  to zero. Mutual information depends on all
  higher-order statistics of the output variables
  while decorrelation only takes account of
  2nd-order statistics.

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Signal Mixing Unmixing Example
Mixed Sources
?
Unmixed Sources
Sources
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Basics of ICA

• Blind Source Separation Problem
• Data Model

• Xobserved data, Amixing matrix,
  Ssources
• Assumptions
• Source density is NOT Gaussian
• Linear Mixing
• Find an unmixing matrix W by making components of
  S sparse, nongaussian and independent, such that

• 
  AW-1
• Study Sources, S and mixing matrix, A to find
  hidden components in measurements

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ICA Estimation Principles

• Two ICA Estimation Principles
• Nonlinear Decorrelation Find W such that
  components of S and their non-linear transformed
  components are uncorrelated.
• Maximum Nongaussianity Find W such that
  components are the maximally nongaussian.
• ICA Limitations
• Logistic BS Infomax handles only super-gaussian
• Extended Infomax handles both sub- and
  super-gaussian
• FastICA handles both sub- and super-gaussian
• Mixture Density handles any different types of
  density with parameterized nonlinearity
  functions.

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Independent Component Analysis
x As

• Original ICA(Bell,1996) preselected gi( ) for
  supergaussian
• Extended ICA(Lee,1998) preselected gi( ) for
  super or subgaussian
• ICA with mixture density model Adjustable gi( )
  for any density

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What is Alpha Activity?

• Rhythmic (8-10 Hz in EEG)
• Produced during awake and relaxed state
• Intermittent burst
• Unknown hemodynamics response in fMRI
• Sources ?

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Experimental Protocol

• Conditions
• - With closed eyes
• - Relaxation awake and relaxed, thus
  producing Alpha
• - Mathematical Tasks Perform pre-assigned
  math operation, thus suppressing Alpha
• Protocol for fMRI EEG

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Results of ICA in EEG

Measured EEG Signals
Temporal ICs
x1(t)
x19(t)
EEG measurements, xi(n)
Alpha power
index of electrode
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Mixture Density ICA for fMRI

• Assumption
• fMR Images as weighted sum of unknown spatial
  maps (ICs)

• ICA decompose fMRI data into spatially
  independent components maps

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Localization in fMRI and EEG

• Spatial Scalp Maps of Temporally Independent
  Sources in EEG

• 2 Spatially Independent Maps of
  fMRI

ICA only
Model- and data- driven methods
J. Jeong T.-S. Kim, submitted.
40
Mixture Density ICA for EEG

• Assumption
• EEG channel data as weighted sum of temporal ICs

• ICA decompose EEG data into temporally
  independent components maps

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Selection of alpha components in EEG
Ratio of alpha power to background power
SNR of components
Selected alpha dominant components
Spatial distribution of alpha comps
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Results of ICA in fMRI
cc 0.48
cc 0.44
Spatially independent maps
Associated Time courses
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Results of ICA in fMRI
72
cc .57
6
cc .46
7
cc .42
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Result of ME Localization
w/o ICA (left) and w/ ICA (right)
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fMRI Noise and Artifacts

• Problem negative and positive false activation
  in functional images
• Periodic Sources (physiological fluctuations)
• Cardiac and respiratory pulsation
• CSF pulsation
• Nonperiodic Sources
• MR system instability
• Magnetic susceptibility change
• Gross head motion
• Signal drifts and shifts

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Summary

• Image local changes in blood oxygen concentration
• Blood Oxygenation Level Dependant (BOLD) contrast
• Superior spatial resolution 2 mm
• Low temporal resolution 12 sec due to slow
  blood hemodynamics
• Provides both anatomical and functional
  information
• No radioactive material injected
• Artifacts due to physiological noise components
• Movement-related artifacts
• Signals originates from tissue and draining veins