Transcript Folie 1

Medical Imaging
Dr. Mohammad Dawood
Department of Computer Science
University of Münster
Germany
Medical Imaging, SS-2014
Recap
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Medical Imaging, SS-2014
α decay
Gamma scintigraphy
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β- decay
PET
β+ decay
Sinogram
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Medical Imaging, SS-2014
Ultrasound / Sonography
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Medical Imaging, SS-2014
Sound spectrum
The frequency range of sound above 20kHz is known as ultrasound
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Medical Imaging, SS-2014
Sound wave propagation
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Ultrasound
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Medical Imaging, SS-2014
Ultrasound
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Medical Imaging, SS-2014
Ultrasound
- is produced through the conversion of electrical energy into mechanical energy
- is detected by the reverse process, by converting mechanical energy into
electrical energy.
- The transducer is a device that is both a transmitter and receiver of the
ultrasound signal and it serves a dual role in pulse echo imaging.
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Medical Imaging, SS-2014
Inge Edler und Helmut Hertz
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(1954)
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Ultrasound
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Ultrasound
Reflexion
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Scatter
Transmission
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Ultrasound
Depth
Resolution
Frequency
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Ultrasound
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Ultrasound
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Medium
Speed of sound m/s
Air
331
Water
1483
Tissue
1540-1595
Liver
1549
Blood
1570
Glycerin
1923
Bones (Compact)
3600
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Ultrasound
- A mode (amplitude)
- B mode (brightness)
- M mode (moving)
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Ultrasound
- no functional information
- Bone / gas
- fast
- Obesity
- cheap
- Operator dependence
- no radiation
- portable
- no injection
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Image Reconstruction
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Reconstruction
Tomography
detector
x-ray source
collimator
object
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Medical Imaging, SS-2014
Reconstruction
Law of Attenuation
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Reconstruction
Parallel projections of a plane
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Reconstruction
y
s
Radon Transformation
f
n
r
θ
x
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Medical Imaging, SS-2014
Reconstruction
Radon Transformation (Line Integrals at different angles)
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Reconstruction
Radon Transformations
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Reconstruction
Radon Transformation
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128
512
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Reconstruction
Inverse Radon Transformation
H: Hilbert transform
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Reconstruction
Inverse Radon Transformation
Problems with Missing data and Noise!
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Reconstruction
Filtered Back Projection
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Reconstruction
Projections
A
A+B
B
1
D
C+D
C
2
4
A
B
3
B+D
A+C
D
C
A+D B+C
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Medical Imaging, SS-2014
Reconstruction
Back projections
2A+B
3A+B
4A+B
A+3B
A+4B
A+B+
A+C+
C+2D
C+D
?
C+4D
3D
A+B+
A+2C
C+D
?
3C+D
4C+D
+D
A+2B
A+B
? A+B
?
+C+D
+C
+C+D
+C
A+B
1
C+D
4
2
A
B
B+D
3
C
A+C
D
A+D B+C
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Medical Imaging, SS-2014
Reconstruction
Back projections
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4A+B
+C+D
A+4B
+C+D
A+B+
C+4D
A+B+
4C+D
3A
3B
- (A+B+C+D) =
A
B
D
C
/ (n-1) =
3D
3C
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Reconstruction
Filtered Back Projection
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Reconstruction
FBP: Commonly used filters
1=Ram-Lak (ramp), 2=Shepp-Logan,
3=Cosine, and 4=Hamming
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Reconstruction
Filtered Back Projection
2D/3D filtering is costly
Backproject
Filter 2D
Projections
Image
Filter 1D
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Backproject
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Reconstruction
Fourier slice theorem
Take a two-dimensional function f(r), project it onto a line, and do a
Fourier transform of that projection
Take that same function, but do a two-dimensional Fourier transform
first, and then slice it through its origin parallel to the projection line
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Medical Imaging, SS-2014
Reconstruction
Iterative Reconstruction
Kaczmarz Method (=ART: Algebraic Reconstruction Technique)
1. Start by setting x(0) = 0
2. Compute the forward projection
3. Update the current estimate
4. Iterate steps 2,3 until the difference between new forward projection,
computed in 2, and the old one is below tolerance
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