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Data Acquisition, Representation and Reconstruction of medical images

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Presentation on theme: "Data Acquisition, Representation and Reconstruction of medical images"— Presentation transcript:

1 Data Acquisition, Representation and Reconstruction of medical images
Application of Advanced Spectral Methods

2 Acquisition Methods for medical images
X-Rays Computer Tomography (CT or CAT) MRI (or NMR) PET / SPECT (Positron Emission Tomography, Single Photon Emission Computerized Tomography Ultrasound Computational What about microscopic scanners! What about electron microscopes? What about voxelization / discretization? What about synthetic methods? Provide Links to the numerous online tutorials!

3 X-Rays

4 X-Rays - Physics cheap and relatively easy to use
X-Rays are associated with inner shell electrons As the electrons decelerate in the target through interaction, they emit electromagnetic radiation in the form of x-rays. patient is located between an x-ray source and a film -> radiograph cheap and relatively easy to use potentially damaging to biological tissue

5 X-Rays X-Rays similar to visible light, but higher energy!

6 X-Rays - Visibility bones contain heavy atoms -> with many electrons, which act as an absorber of x-rays commonly used to image gross bone structure and lungs excellent for detecting foreign metal objects main disadvantage -> lack of anatomical structure all other tissue has very similar absorption coefficient for x-rays

7 X-Rays - Images X-Rays can be used in computerized tomography
Add images!!! X-Rays can be used in computerized tomography

8 Computerized (Axial) Tomography

9 CT (CAT) scanners and relevant mathematics

10 Non-Intrusive Medical Diagnosis based on Computerized Tomography
Computer tomography CT (From Jain’s Fig.10.1) An X-ray CT scanning system

11 Non-Intrusive Medical Diagnosis based on Transmission Tomography
Source and Detector are rotating around human’s body (From Bovik’s Handbook Fig )

12 Non-Intrusive Medical Diagnosis based on projections
Observe a set of projections (integrations) along different angles of a cross-section Each projection itself loses the resolution of inner structure Types of measurements transmission (X-ray), emission, magnetic resonance (MRI) Want to recover inner structure from the projections “Computerized Tomography” (CT)

13 Non-Intrusive Medical Diagnosis based on Emission Tomography
Emission tomography: ET measure emitted gamma rays by the decay of isotopes from radioactive nuclei of certain chemical compounds affixed to body parts. MRI: based on that protons possess a magnetic moment and spin. In magnetic field => align to parallel or antiparallel. Apply RF => align to antiparallel. Remove RF => absorbed energy is remitted and detected by Rfdetector. f(x,y) is 2D image as before

14 Radon Transform Principles
A linear transform f(x,y)  g(s,) Line integral or “ray-sum” Along a line inclined at angle  from y-axis and s away from origin Fix  to get a 1-D signal g(s) We have now a set of images g(s) which represent g(s,) (From Jain’s Fig.10.2) This is a transform from 2D to 2D spaces

15 Tomography and Reconstruction
Lecture Overview Applications Background/history of tomography Radon Transform Fourier Slice Theorem Filtered Back Projection Algebraic techniques Measurement of Projection data Example of flame tomography

16 Applications & Types of Tomography
Medical Applications Type of Tomography Full body scan X-ray Respiratory, digestive systems, brain scanning PET Positron Emission Tomography Respiratory, digestive systems. Radio-isotopes Mammography Ultrasound Whole Body Magnetic Resonance (MRI, NMR) MRI and PET showing lesions in the brain. PET scan on the brain showing Parkinson’s Disease

17 Applications & Types of Tomography – non medical
Non Medical Applications Type of Tomography Oil Pipe Flow Turbine Plumes Resistive/Capacitance Tomography Flame Analysis Optical Tomography                                                                       ECT on industrial pipe flows

18 CT or CAT - Principles Computerized (Axial) Tomography Radon again!
introduced in 1972 by Hounsfield and Cormack natural progression from X-rays based on the principle that a three-dimensional object can be reconstructed from its two dimensional projections based on the Radon transform (a map from an n-dimensional space to an (n-1)-dimensional space) Radon again! From 2D to 3D !

19 CT or CAT - Methods measures the attenuation of X-rays from many different angles a computer reconstructs the organ under study in a series of cross sections or planes combine X-ray pictures from various angles to reconstruct 3D structures

20 The History of CAT Johan Radon (1917) showed how a reconstruction from projections was possible. Cormack (1963,1964) introduced Fourier transforms into the reconstruction algorithms. Hounsfield (1972) invented the X-ray Computer scanner for medical work, (which Cormack and Hounsfield shared a Nobel prize). EMI Ltd (1971) announced development of the EMI scanner which combined X-ray measurements and sophisticated algorithms solved by digital computers.

21 Backpropagation Principles

22 Backpropagation We know that objects are somewhere here in black stripes, but where?


24 Example of Simple Backprojection Reconstruction
Given are sums, we have to reconstruct values of pixels A, B, C and D

25 Image Reconstruction: ART or Algebraic Reconstruction Technique

26 CT - Reconstruction: ART or Algebraic Reconstruction Technique
METHOD 1: Algebraic Reconstruction Technique iterative technique attributed to Gordon Initial Guess Reconstructed model Back- Projection Projection Actual Data Slices 26

27 CT - Reconstruction: FBP Filtered Back Propagation
METHOD 2 : Filtered Back Projection common method uses Radon transform and Fourier Slice Theorem y F(u,v) f(x,y) Gf(r) x s u gf(s) f Spatial Domain Frequency Domain

Algebraic Reconstruction Technique Still slow better quality for fewer projections better quality for non-uniform project. “guided” reconstruct. (initial guess!) Filtered Back Projection Computationally cheap Clinically usually 500 projections per slice problematic for noisy projections 28

29 Fourier Slice Theorem and FFT review
Patient’s body is described by spatial distribution of attenuation coefficient

30 Properties of attenuation coefficient
Our transform: f(x,y)  p(r,)

31 attenuation coefficient is used in CT_number of various tissues
These numbers are represented in HU = Hounsfield Units CT_number uses attenuation coefficients

REMEMBER: f(x,y)  p(r,)

33 Radon Transform is available in Matlab
Radon and its inverse easy to use You can do your own projects with CT reconstruction Data are available on internet sinogram

34 Inverse Radon Transform

35 Matlab examples

36 Sinogram versus Hough

37 The object An aside The sinogram AN ASIDE

38 Review and notation – Fourier Transform of Image f(x,y)

39 Matlab example In Matlab there are implemented functions that use Fourier Slice Theorem

40 Matlab example: filtering
High frequency removed Low frequency removed

41 Matlab example - convolution

42 Remainder of main theorem of spectral imaging
Matlab example – filtering by convolution in spectral domain

43 Radon Transform and a Head Phantom


45 Reconstructing with more and more rays

46 Example of Image Radon Transform
[Y-axis] distance, [X-axis] angle (From Matlab Image Processing Toolbox Documentation)

47 Matlab Implementation of Radon Transform



50 big noise No noise

51 The Lung Cancer and the reconstruction

52 The Lung and The CTs 52 [LUNG]
1.Either of the pair of organs occupying the cavity of the thorax that effect the aeration of the blood. 2.Balloon-like structures in the chest that bring oxygen into the body and expel carbon dioxide from the body [TYPES] 1.Small Cell Lung Cancer (SCLC) - 20% of all lung cancers 2.Non Small Cell Lung Cancer (NSCLC) - 80% of all lung cancer [Risks] In the United States alone, it is estimated that 154,900 died from lung cancer in In comparison,is estimated that 126,800 people died from colon, breast and prostate cancer combined, in 2002. [LUNG CANCER] Lung Cancer happens when cells in the lung begin to grow out of control and can than invade nearby tissues or spread throughout the body; Large collections of this out of control tissues are called tumors. 52

53 We want to reconstruct shape of the lungs
Starting Point Border Detection At the moment two approaches are available. Left the algorithm developed at Pisa Right the algorithm developed at Lecce 53

54 Image Interpolation - Theory
[IDEA] In order to provide a richer environment we are thinking of using interpolation methods that will generate “artificial images” thus revealing hidden information. [RADON RECONSTRUCTION] Radon reconstruction is the technique in which the object is reconstructed from its projections. This reconstruction method is based on approximating the inverse Radon Transform. [RADON Transform] The 2-D Radon transform is the mathematical relationship which maps the spatial domain (x,y) to the Radon domain (p,phi). The Radon transform consists of taking a line integral along a line (ray) which passes through the object space. The radon transform is expressed mathematically as: [FILTERED BACK PROJECTION - INVERSE R.T.] It is an approximation of the Inverse Radon Transform. [The principle] Several x-ray images of a real-world volume are acquired [The Data] X-ray images (projections) of known orientation, given by data samples. [The Goal] Reconstruct a numeric representation of the volume from these samples. [The Mean] Obtain each voxel value from its pooled trace on the several projections. [Resampling] At this point one can obtain the “artificial slices” [Reslicing] An advantage of the volume reconstruction is the capability of obtaining new perpendicular slices on the original ones. 54

55 Image Interpolation - Graphical Representation (I)

56 Image Interpolation - Graphical Representation (II)

57 Line Integrals and Projections
We review the principle Discuss various geometries Show the use of filtering

58 Line Integrals and Projections
The function P = Radon transform object function f(x,y). The function is known as the Radon transform of the function f(x,y).

59 Various types of beams can be used
Fan Beams Parallel Beams A fan beam projection is taken if the rays meet in one location Parallel beams projections are taken by measuring a set of parallel rays for a number of different angles Various types of beams can be used

60 Line Integrals and Projections
A projection is formed by combining a set of line integrals. Here the simplest projection, a collection of parallel ray integrals i.e constant θ, is shown. Notation for calculations in these projections

61 Line Integrals and Projections
A simple diagram showing the fan beam projection

62 Fourier Slice Theorem

63 Fourier Slice Theorem The Fourier slice theorem is derived by taking the one-dimensional Fourier transform of a parallel projection and noting that it is equal to a slice of the two-dimensional Fourier transform of the original object. It follows that given the projection data, it should then be possible to estimate the object by simply performing the 2D inverse Fourier transform. Start by defining the 2D Fourier transform of the object function as For simplicity θ=0 which leads to v=0 Define the projection at angle θ = Pθ(t) Define its transform by As the phase factor is no-longer dependent on y, the integral can be split.

64 Fourier Slice Theorem As the phase factor is no-longer dependent on y, the integral can be split. The part in brackets is the equation for a projection along lines of constant x Substituting in Thus the following relationship between the vertical projection and the 2D transform of the object function:

65 Fourier Slice Theorem Stanley and Kak
Full details of derivation, not for now.


67 The Fourier Slice Theorem
The Fourier Slice theorem relates the Fourier transform of the object along a radial line. t Fourier transform v u θ Space Domain Frequency Domain

68 The Fourier Slice Theorem
The Fourier Slice theorem relates the Fourier transform of the object along a radial line. Collection of projections of an object at a number of angles t v u Fourier transform v u θ For the reconstruction to be made it is common to determine the values onto a square grid by linear interpolation from the radial points. But for high frequencies the points are further apart resulting in image degradation. Space Domain Frequency Domain



71 Backprojection of Radon Transform




75 Backprojection of Radon Transform


77 Filtered backpropagation creates crisp edges
Ideal cylinder Blurred edges Crisp edges Filtered backpropagation creates crisp edges


79 Computerized Tomography Equipment


81 CT - 2D vs. 3D Linear advancement (slice by slice) helical movement
typical method tumor might fall between ‘cracks’ takes long time helical movement 5-8 times faster A whole set of trade-offs



84 Evolution of CT technology

85 CT or CAT - Advantages significantly more data is collected
superior to single X-ray scans far easier to separate soft tissues other than bone from one another (e.g. liver, kidney) data exist in digital form -> can be analyzed quantitatively adds enormously to the diagnostic information used in many large hospitals and medical centers throughout the world

86 CT or CAT - Disadvantages
significantly more data is collected soft tissue X-ray absorption still relatively similar still a health risk MRI is used for a detailed imaging of anatomy – no Xrays involved.

87 Nuclear Magnetic Resonance (NMR) Magnetic Resonance Imaging (MRI)

88 MRI Nuclear Magnetic Resonance (NMR) (or Magnetic Resonance Imaging - MRI) most detailed anatomical information high-energy radiation is not used, i.e. this is “safe method” based on the principle of nuclear resonance (medicine) uses resonance properties of protons

89 Magnetic Resonance Imaging MRI - polarized
all atoms (core) with an odd number of protons have a ‘spin’, which leads to a magnetic behavior Hydrogen (H) - very common in human body + very well magnetizing Stimulate to form a macroscopically measurable magnetic field

90 MRI - Signal to Noise Ratio
proton density pictures - measures H MRI is good for tissues, but not for bone signal recorded in Frequency domain!! Noise - the more protons per volume unit, the more accurate the measurements - better signal to noise ratio (SNR) through decreased resolution

91 PET/SPECT Positron Emission Tomography Single Photon Emission Computerized Tomography

92 PET/SPECT Positron Emission Tomography Single Photon Emission Computerized Tomography recent technique involves the emission of particles of antimatter by compounds injected into the body being scanned follow the movements of the injected compound and its metabolism reconstruction techniques similar to CT - Filter Back Projection & iterative schemes

93 Ultrasound

94 Ultrasound the use of high-frequency sound (ultrasonic) waves to produce images of structures within the human body above the range of sound audible to humans (typically above 1MHz) piezoelectric crystal creates sound waves aimed at a specific area of the body change in tissue density reflects waves echoes are recorded

95 Ultrasound (2) Delay of reflected signal and amplitude determines the position of the tissue still images or a moving picture of the inside of the body there are no known examples of tissue damage from conventional ultrasound imaging commonly used to examine fetuses in utero in order to ascertain size, position, or abnormalities also for heart, liver, kidneys, gallbladder, breast, eye, and major blood vessels

96 Ultrasound (3) by far least expensive very safe very noisy
1D, 2D, 3D scanners irregular sampling - reconstruction problems




100 Typical Homework

101 Sources of slides and information
Badri Roysam Jian Huang, Machiraju, Torsten Moeller, Han-Wei Shen Kai Thomenius

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