Signal Processing for Medical Applications – Frequency Domain Analyses Muthuraman Muthuraman Christian-Albrechts-Universität zu Kiel Department of Neurology / Faculty of Engineering Digital Signal Processing and System Theory
Contents 1.Basics of Brain – i) Brain signals - EEG/ MEG; ii) Muscle signals - EMG; Lecture 1 & 2 iii) Magnetic resonance imaging – MRI iv) Tremor disorders 2. Quantities measured from time series in frequency domain Lecture 3 i) Power spectrum ii) Modelling time series using AR2 processes ii) Coherence spectrum Lecture 4 - Different windows used for the estimation iii) Phase spectrum Lecture 5 iv) Delay between signals 3. Source analysis in the frequency domain - Forward problem Lecture 6-10 - Inverse problem - Different Solutions Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-2
Lecture 2 – Magnetic resonance imaging (MRI) • Basics of MRI > Magnets > Hydrogen atoms • Creating a image • Visualization Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-3
Lecture 2 – Magnetic resonance imaging (MRI) Basics Of MRI(Magnetic Resonance Imaging) • 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. Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-4
Lecture 2 – Magnetic resonance imaging (MRI) Magnets Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-5
Lecture 2 – Magnetic resonance imaging (MRI) Hydrogen atoms • 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 Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-6
Lecture 2 – Magnetic resonance imaging (MRI) 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. Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-7
Lecture 2 – Magnetic resonance imaging (MRI) Creating a Image • 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. Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-8
Lecture 2 – Magnetic resonance imaging (MRI) 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. Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-9
Lecture 2 – Magnetic resonance imaging (MRI) Visualization Fourier transform k y image space k -space k x 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 Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-10
Lecture 2 – Magnetic resonance imaging (MRI) Magentic Resonance Imaging (MRI) Axial Coronal Sagittal Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-11
Lecture 2 – Magnetic resonance imaging (MRI) Magentic Resonance Imaging (MRI) Sagittal Axial / Horizontal Coronal / Frontal Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-12
Lecture 2 – Magnetic resonance imaging (MRI) Magentic Resonance Imaging (MRI) Axial / Horizontal Coronal / Frontal Sagittal Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-13
Lecture 2 – Magnetic resonance imaging (MRI) Magentic Resonance Imaging (MRI) Susceptibility and Susceptibility Artifacts Adding a nonuniform object (like a person) to B 0 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 sinuses -occurs near junctions between air and tissue • sinuses, ear canals ear canals Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-14
Lecture 2 – Magnetic resonance imaging (MRI) Magentic Resonance Imaging (MRI) 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 Starts off large when all phases are about equal Random Frequencies Decays away as different components get different phases Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-15
Lecture 2 – Magnetic resonance imaging (MRI) 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. Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-16
Lecture 2 – Magnetic resonance imaging (MRI) FMRI (Functional Magnetic Resonance Imaging) 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-17
Lecture 2 – Magnetic resonance imaging (MRI) FMRI (Functional Magnetic Resonance Imaging) Frequency spread causes signal loss over time BOLD contrast: Amount of signal loss reflects [dHb] Contrast increases with delay (T E = echo time) Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-18
Lecture 2 – Magnetic resonance imaging (MRI) FMRI (Functional Magnetic Resonance Imaging) neuron capillary HbO 2 dHb HbO 2 dHb dHb dHb dHb HbO 2 dHb HbO 2 dHb dHb HbO 2 HbO 2 HbO 2 HbO 2 dHb dHb HbO 2 dHb HbO 2 HbO 2 HbO 2 dHb dHb dHb HbO 2 HbO 2 Vascular HbO 2 = oxyhemoglobin Response O 2 metabolism dHb = deoxyhemoglobin to Activation [dHb] blood flow blood volume Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-19
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