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ECSE-6963 Biological Image Analysis

ECSE-6963 Biological Image Analysis

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ECSE-6963 Biological Image Analysis

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  1. ECSE-6963Biological Image Analysis Lecture #7: Common Medical Imaging Instrument: MRI & PET Scanner Badri Roysam Rensselaer Polytechnic Institute, Troy, New York 12180. Center for Sub-Surface Imaging & Sensing

  2. Recap • Probes, media, and objects • Basic types of microscopes • The Radon Transform, back projection algorithm, & the X-Ray CT Scanner

  3. Magnetic Resonance Imaging (MRI) • Biggest advance since X-Rays & X-Ray CT • Was called “Nuclear Magnetic Resonance” in the early days • The word “Nuclear” made people uncomfortable during the 70’s, was not needed. • MRI has advanced to a point of becoming the method of choice for most parts of the body 1952 Nobel Prize: Felix Bloch and Edward Purcell 1991 Nobel Prize: Richard R. Ernst

  4. Basic Principle • Use radio waves instead of X-Rays to probe • Problem: Wavelength is too long! • Get around this limitation by producing images based on spatial variations in the phase and frequency of the radio frequency energy being absorbed and emitted by the imaged object. • Exploit magnetic properties of abundant particles such as protons in tissue • When protons are placed in a magnetic field, they become capable of receiving and then transmitting electromagnetic energy. • The strength of the transmitted energy is proportional to the number of protons in the tissue. • Signal strength is modified by properties of each proton's microenvironment, such as its mobility and the local homogeneity of the magnetic field. • MR signal can be "weighted" to accentuate some properties over others.

  5. Physics Background - Spin • All physical particles (electrons, protons, neutrons….) possess a fundamental property known as spin • Spin is always a multiple of +/- ½ . • Think of spin as a tiny spinning magnet with a north pole and a south pole • When an external magnetic field B is applied along the z axis, the tiny magnets line up along the same axis • It can absorb/emit energy at a characteristic frequency • These frequencies are in the radio frequency range Nuclei with highest biological abundance

  6. Behavior when a field is applied • Each mini-magnet has two states: • Lined up along B, and against • There is an energy difference between these two states • Change in state involves absorbing or emitting radio-frequency energy

  7. Behavior when radio excitation is applied • At equilibrium, net magnetization vector is along B (z axis) • When we apply radio frequency energy at frequency , the magnetization can turn slowly away from the equilibrium angle • The change in angle depends on how long the RF energy is applied • A 90o turn can make the z component of the magnetization Mz zero (takes several milliseconds) • If we apply a long enough RF pulse, the magnetization can even turn by 180o

  8. Behavior when radio excitation is stopped • If we stop this RF excitation, it returns to equilibrium T1 = spin-lattice relaxation time

  9. Behavior when radio excitation is stopped • Excitation in the x-y plane makes the magnetization “precess” (wobble, like a spinning top) around the z axis. • Rate of precession is called the “Larmor frequency” • The transverse magnetization Mxy returns to equilibrium according to T2 = spin-spin relaxation time

  10. T1 and T2 • T1 is applicable along the magnetic field (axial/longitudinal) • Can’t be detected directly • Measures how quickly equilibrium is achieved with external field • T2 is applicable in a transverse direction to the magnetic field • Can be detected directly • Measures how long the precession persists after excitation is turned off • Useful to know T1 and T2 because they are characteristically different for each kind of tissue • These equations are for one proton

  11. The Free Induction Decay signal • A rotating magnetization will induce a current in a coil perpendicular to the z axis (say, along x axis) • This signal decays as T2 • This signal is called the “free induction decay” or FID.

  12. The 90o FID • The RF pulse is long enough to flip the net magnetization by 90o • The magnetization vector’s decay can be measured with a coil. • To get a strong signal: • Increase B0 • Reduce temperature T • Material with high  • Material with more protons • In general, more spins • More abundant material

  13. The Overall MRI Signal • There’s not enough time to establish full equilibrium in practice. If TR is the time available for recovery after the previous pulse, the longitudinal magnetization actually available is: •  = proton density • This magnetization produces the transverse magnetization in response to a 90o pulse. We measure this at time TE

  14. Weighted Signals • By choosing TR and TE suitably, we can make one of the factors T1, T2, or  dominate

  15. Imaging Process: Basic Idea • If the external field is constant, B, then for the 3 points in the brain, the resonance frequency is the same, so they can’t be distinguished • We just see the sum of 3 signals • One way to distinguish the points is to change B, i.e., a small gradient field (about 0.01 T/m). • Only the points whose resonant frequency matches will respond

  16. Selecting a Slice thru the Patient • Apply a linear magnetic field gradient Gzduring the time that the RF pulse is applied • Only the small window of z values for which the resonance frequency is matched will resonate

  17. Back-Projection Imaging • Apply 1-D field gradient at multiple angles in the x-y plane • Record MR spectra at multiple angles and use the back projection algorithm • Gx, Gy, and Gz are components of a 3-D field gradient

  18. Pulse Sequence for Back-Projection Imaging • Apply a linear magnetic field gradient Gzduring the time that the RF pulse is applied to excite a slice through the patient • Use Gx, Gy, to set the angle while recording the signal

  19. Better Technology Briefly, • Use sinc-shaped pulse • GS selects the slice along the z axis • G sets the phase encoding w.r.t the y axis within the plane at the selected z value • Gf sets the frequency encoding w.r.t the x axis within the plane at the selected z value

  20. Image Reconstruction Fourier Transform the FID signal to obtain a frequency spectrum for each angle Backproject the frequency spectra to reconstruct image!

  21. The Instrument • The magnets are extremely strong (1 – 3 Tesla) • Enough to hurl a trashcan across a room! • Extremely noisy & claustrophobic inside the machine • Optimal design of coils, pulse sequences, and reconstruction algorithms is big business • Current instruments have progressed way beyond the back-projection scheme outlined here

  22. MRI Images • Pixel sizes approx. 3 mm3 • By collecting a series of images, it is possible to calculate 3 values at each pixel: T1,T2 , Proton density  • Different tissues show up differently on each of these “channels” • Basis of image segmentation! T1 T2 Proton Density

  23. MRI Images Axial (trans-axial, horizontal) Coronal Sagittal

  24. CT Cheap & Fast Good resolution with bone Hard to distinguish soft tissues without contrast agent Can’t distinguish atoms beyond their X-Ray cross-section X-Rays harmful to body MRI Expensive & Slow Can distinguish bone and various soft tissues Can distinguish specific atoms No known health hazards to MR imaging CT vs. MRI

  25. Main advantages of MRI • Structural & Functional Imaging Possible • Differentiation between various kinds of soft tissue. • X-rays pass through soft tissue without much absorption • High sensitivity to early pathological changes makes early detection possible. • Studies of blood vessels and flow without use of contrast • just oxygen level of blood gives contrast • 3-D, allowing Multi-planar display • i.e. axial, sagittal, coronal, and oblique. • Multi-channel output • Enables better segmentation • No known biological hazards • Magnetic fields don’t ionize, unlike X-rays • Exceptions: people with pacemakers and/or implanted metallic objects can’t be imaged safely

  26. Recent Developments • Faster imaging (about 5 images/sec) • “Echo Planar MR” can image the brain in seconds instead of minutes • Of late, the importance of MRI in diagnosis is also greatly enhanced by its ability to do • Functional mapping of the brain • Exploit the fact that oxygen level differences in blood show up on MRI’s. • Spectroscopy and molecular imaging

  27. Nuclear Medicine • Basic Idea: • Inject patient with radio-isotope labeled substance (tracer) • Chemically the same, but physically different • Detect the radioactive emissions (gamma rays) • Super-short wavelength • But, can’t achieve the implied high resolution • Detection technology limitations • Not enough photons! • Use filtered back-projection to reconstruct the 3-D image • Like fluorescence microscopy, except we don’t need excitation

  28. SPECT & PET • Major Functional imaging tools • SPECT: Single-photon Emission Computed Tomography • cheap and low-resolution • Tells us where blood is flowing • PET: Positron Emission Tomography • expensive and higher-resolution PET image Showing a tumor

  29. SPECT Instrument • The “gamma camera” is a 2-D array of detectors • One or more gamma cameras are used to capture 2-D projections at multiple angles • Use filtered back-projection to reconstruct 3-D image! • Actual sinograms appear “noisy” due to the fact that we don’t have enough photons • Quantum-limited imaging 3-camera SPECT instrument

  30. PET Idea Gamma Photon #1 Nucleus (protons+neutrons) Basic Idea: • Nucleus emits a positron • A short-lived particle • Same mass as electron, but opposite charge • Positron collides with a nearby electron and annihilates • Two 511 keV gamma rays are produced • They fly in opposite directions (to conserve momentum) BANG electrons Gamma Photon #2

  31. B A Emission Detection Ring of detectors • If detectors A & B receive gamma rays at the approx. same time, we have a detection • Hard sensor and electronics design problem, expensive

  32. Image Reconstruction • We can organize our set of detections as a set of angular views • Use filtered back-projection algorithm!

  33. PET Images • Single-channel images • Noisy, and blurry • Not ideal for segmentation • Segment MRI/CT for defining anatomy • Register the images • Measure activity

  34. Better Algorithms • Filtered back-projection algorithm • produces a background artifact, discussed earlier • Noisy reconstruction • The Maximum Likelihood algorithm produces a better reconstruction for the same data Filtered Back-Projection Maximum Likelihood

  35. References on MRI • Main MRI Reference: • http://www.cis.rit.edu/htbooks/mri/inside.htm • Other MRI References • http://www.spincore.com/nmrinfo/mri_s.html • http://dmoz.org/Science/Chemistry/Nuclear_Magnetic_Resonance/Theory_of_NMR_and_MRI/Basic_NMR_and_MRI_Theory/

  36. References on SPECT & PET • PET • http://www.crump.ucla.edu/lpp/lpphome.html • SPECT Imaging: • http://www.physics.ubc.ca/~mirg/intro.html • SPECT Image Atlas • http://brighamrad.harvard.edu/education/online/BrainSPECT/BrSPECT.html

  37. Summary • Discussion of major medical instruments • Structure imaging • Function imaging • Next Class: • Image Pre-processing methods Image Acquisition Image Reconstruction & Pre-processing Image Segmentation Morphometry & Higher-Level Analysis

  38. Assignment #2 1. Create a simple 2-D phantom like the one shown in Lecture 6, and use the “radon” and “iradon” functions in MATLAB to simulate a CT scanner with 1, 2, 4, 48, and 96 angles. In other words, generate an example like the one shown in class. http://www.mathworks.com/access/helpdesk/help/toolbox/images/transfo9.shtml. Note: You should be able to do the above exercise simply by following the instructions in the tutorials on the mathworks website. They do not need specialized mathematics knowledge. 2. Search the Internet for a sample MRI image of the human brain. Plot a histogram of T1, T2 and Proton density values from this image. 3. Search the Internet for a sample PET image, and an MRI image for the same patient. Generate an overlay of the PET image over the MRI image using MATLAB or an image viewer such as PaintShop Pro or Adobe Photoshop.

  39. Instructor Contact Information Badri Roysam Professor of Electrical, Computer, & Systems Engineering Office: JEC 6046 Rensselaer Polytechnic Institute 110, 8th Street, Troy, New York 12180 Phone: (518) 276-8067 Fax: (518) 276-6261/2433 Email: roysam@ecse.rpi.edu Website: http://www.rpi.edu/~roysab NetMeeting ID (for off-campus students): 128.113.61.80 Secretary: Jeanne Denue, JEC 6049, (518) 276 –6313, denuej@ecse.rpi.edu