signal processing for medical applications frequency ...1.basics of brain – i) brain signals -...
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Muthuraman Muthuraman Christian-Albrechts-Universität zu Kiel Department of Neurology / Faculty of Engineering Digital Signal Processing and System Theory
Signal Processing for Medical Applications –
Frequency Domain Analyses
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-2
1.Basics of Brain –
i) Brain signals - EEG/ MEG;
ii) Muscle signals - EMG;
iii) Magnetic resonance imaging – MRI
iv) Tremor disorders
2. Quantities measured from time series in frequency domain
i) Power spectrum
ii) Modelling time series using AR2 processes
ii) Coherence spectrum
- Different windows used for the estimation
iii) Phase spectrum
iv) Delay between signals
3. Source analysis in the frequency domain
- Forward problem
- Inverse problem
- Different Solutions
Lecture 1 & 2
Lecture 3
Lecture 4
Lecture 5
Lecture 6-10
Contents
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-3
• Basics of MRI
> Magnets
> Hydrogen atoms
• Creating a image
• Visualization
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-4
• Magnets The biggest and the most important part component in a
MRI system is the magnet, unit-tesla. There is horizontal tube running
through the magnet from front to back, this tube is the bore of the magnet.
• Superconducting Magnet Principle(Superconductivity) Metals and
ceramic materials cooled to temp. near absolute zero no electrical
resistance electrons can travel through them freely carry large amounts
of current long periods of time without losing energy as heat.
• Gradient Magnet There are 3 gradient magnets inside the MRI
machines. These magnets are very, very low strength compared to the
main magnetic field, range-18 to 27 millitesla.
• The main magnet immerses the patient in a stable and very intense
magnetic field, and the gradient magnets create a variable field.
Basics Of MRI(Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-5
Magnets
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-6
• Hydrogen atoms- It is an ideal atom for MRI because its nucleus
has a single proton and a large magnetic moment
• When placed in a magnetic field, the hydrogen atom has a strong
tendency to line up with the direction of the magnetic field
Hydrogen atoms
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-7
Creating a Image
• Inside the bore of the scanner, the magnetic field runs straight down the center of
the tube in which we place the patient. The hydrogen protons in the body will
lineup in the direction of either the feet or the head.
• The vast majority of protons will cancel each other only a couple remains which is
used to create images.
• The MRI machine applies an RF pulse that is specific only to hydrogen, the
system directs the pulse towards the area of the body we want to examine. The
RF pulse causes the protons in that area to absorb the energy required to make
them spin at a particular frequency in a particular direction. The specific frequency
of resonance is the larmour frequency and is calculated based on the
particular tissue being imaged and the strength of the magnetic
field.
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-8
• The three gradient magnets are arranged in such a manner inside the main
magnet that when they are turned on and off very rapidly in a specific manner,
they alter the main magnetic field on a very local level, which means we can
pick exactly which area we want a picture of the brain.
• The RF pulse is turned off, the hydrogen protons begin to slowly return to their
natural alignment within the magnetic field and release there excess stored
energy. They give off a signal that the coil picks up and sends it to the
computer system. With the Fourier transform the mathematical data is
converted into a picture to put on film.
Creating a Image
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-9
Visualization
• MRI works by altering the local magnetic field in the tissue being
examined.
• Normal and abnormal tissue will respond slightly altered, giving us
different signals.
• These varied signals are transfered to the images, allowing us to
visualize many different types of tissue abnormalities.
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-10
Review: Image Formation
•Data gathered in k-space (Fourier domain of image)
•Gradients change position in k-space during data acquisition (location in k-space is integral of gradients)
•Image is Fourier transform of acquired data
k-space image space
Fourier transform
ky
kx
Visualization
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-11
Sagittal Coronal Axial
Magentic Resonance Imaging (MRI)
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-12
Magentic Resonance Imaging (MRI)
Lecture 2 – Magnetic resonance imaging (MRI)
Sagittal
Axial / Horizontal
Coronal / Frontal
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-13
Magentic Resonance Imaging (MRI)
Lecture 2 – Magnetic resonance imaging (MRI)
Sagittal Axial / Horizontal Coronal / Frontal
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-14
Magentic Resonance Imaging (MRI)
Lecture 2 – Magnetic resonance imaging (MRI)
Susceptibility and Susceptibility Artifacts
Adding a nonuniform object (like a person) to B0 will make the total magnetic field B nonuniform
This is due to susceptibility: generation of extra magnetic fields in materials that are immersed in an external field
For large scale (10+ cm) inhomogeneities, scanner-supplied nonuniform magnetic fields can be adjusted to “even out” the ripples in B — this is called shimming
Susceptibility Artifact
-occurs near junctions between air and tissue
• sinuses, ear canals
sinuses
ear
canals
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-15
How Susceptibility Affects Signal
Susceptibility nonuniform precession frequencies
RF signals from different regions that are at different frequencies will
get out of phase and thus tend to cancel out
Sum of 500 Cosines with
Random Frequencies Starts off large when all phases are about equal
Decays away as different
components get different phases
Magentic Resonance Imaging (MRI)
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-16
FMRI (Functional Magnetic Resonance Imaging)
• FMRI measures brain activity indirectly through changes in blood
vasculature that accompany neural activity
An intial increase in oxygen consumption owing to increased
metabolic demand
After a delay of 2 secs, a large increase in local blood flow, which
overcompensates for the amount of oxygen being extracted
Local increase in cereberal blood volume
• The increase in blood oxyhaemoglobin is what we measure in FMRI.
This is so called the BOLD (Blood oxygen level dependent) response.
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-17
FMRI (Functional Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
The Bold effect
BOLD: Blood Oxygenation Level Dependent
Deoxyhemoglobin (dHb) has different resonance frequency than water
dHb acts as endogenous contrast agent
dHb in blood vessel creates frequency offset in surrounding tissue (approx as dipole pattern)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-18
FMRI (Functional Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
Frequency spread causes signal loss over time
BOLD contrast: Amount of signal loss reflects [dHb]
Contrast increases with delay (TE = echo time)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-19
FMRI (Functional Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
Vascular
Response
to Activation O2 metabolism
dHb
dHb
HbO2
HbO2
dHb HbO2
HbO2
dHb dHb
HbO2
blood flow [dHb]
dHb = deoxyhemoglobin HbO2 = oxyhemoglobin
capillary
blood volume
neuron
HbO2
HbO2
HbO2
HbO2
dHb
dHb
dHb
dHb dHb
dHb
HbO2
HbO2
dHb HbO2
HbO2
dHb dHb
HbO2
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-20
FMRI (Functional Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
Very indirect measure of activity (via hemodynamic response to neural activity)!
Complicated dynamics lead to reduction in [dHb] during activation (active research area)
Neuronal activity Metabolism
Blood flow
Blood volume
[dHb] BOLD signal
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-21
FMRI (Functional Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
Hemodynamic Response Function
% signal change
= (point – baseline)/baseline
usually 0.5-3%
initial dip
-more focal
-somewhat elusive so far
time to rise
signal begins to rise soon after stimulus begins
time to peak
signal peaks 4-6 sec after stimulus begins
post stimulus undershoot
signal suppressed after stimulation ends
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-22
FMRI (Functional Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
The Canonical FMRI Experiment
Subject is given sensory stimulation or task, interleaved with control or rest condition
Acquire timeseries of BOLD-sensitive images during stimulation
Analyse image timeseries to determine where signal changed in response to stimulation
Predicted BOLD signal
time
Stimulus pattern
on
off
on
off
on
off
on
off off
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-23
FMRI (Functional Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
What is required of the scanner?
Must resolve temporal dynamics of stimulus (typically, stimulus lasts 1-30 s)
Requires rapid imaging: one image every few seconds (typically, 2–4 s)
Anatomical images take minutes to acquire!
Acquire images in single shot (or a small number of shots)
1 2 3 … image
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-24
High-resolution FMRI at 7T High-res 7T: 0.58 x 0.58 x 0.58 mm3 = 0.2 mm3
High-res 3T: 1 x 1 x 1 mm3 = 1 mm3
Conventional 3T: 3 x 3 x 3 mm3 = 27 mm3
FMRI (Functional Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-25
FMRI (Functional Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
Diffusion
Tensor
Imaging (DTI)
Water diffusion restricted along white matter
Sensitize signal to diffusion in different directions
Measure along all directions, infer tracts
Diffusion direction
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-26
FMRI (Functional Magnetic Resonance Imaging)
Lecture 2 – Magnetic resonance imaging (MRI)
Complementary information to FMRI
FMRI: gray matter, information processing
DTI: white matter, information pathways
Tractography: tracing white matter pathways between gray matter regions
Tract-based connectivity
Color-coded directions x
y
z
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-27
Tremor Disorders
Tremor:
Tremor is defined as rhythmic non-voluntary oscillatory activity of body parts. The
body parts affected by this disorder are the hands, arms, head, face, vocal cords,
trunk and legs.
Parkinsonian tremor:
The classical form of Parkinsonian tremor is the rest tremor which is present when
the limb is at rest. But pure rest tremor is not so common; it is usually in combination of
both rest and postural or kinetic tremors.
- 1% of the population above 50 years
Essential tremor:
This tremor occurs while doing voluntary actions and remains constant till the action is
performed, it usually disappears at rest.
- 4% of the population above 65 years
Lecture 2 – Tremor Disorders
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-28
PD ET
PD and ET Tremor Patients
Lecture 2 – Tremor Disorders
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-29
Lecture 2 – Tremor Disorders
PD and ET Tremor Patients
STIM OFF STIM ON
Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-30
Deep Brain Stimulation
Lecture 2 – Tremor Disorders