1 Seite 1 Uwe Oelfke Computer-Tomography II: Image reconstruction and applications Prof. Dr. U. Oelfke DKFZ Heidelberg Department of Medical Physics (E040) Im Neuenheimer Feld 280 69120 Heidelberg, Germany [email protected]Uwe Oelfke Contents Motivation for CT B i Ph i H fi ld U it Basic Physics – Hounsfield Units Technical aspects: CT scanner CT reconstruction Spiral- and multi-slice CT Image quality Image quality Clinical applications
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Computer-Tomography II · 1 Seite 1 Uwe Oelfke Computer-Tomography II: Image reconstruction and applications Prof. Dr. U. Oelfke DKFZ Heidelberg Department of Medical Physics (E040)
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1
Seite 1
Uwe Oelfke
Computer-Tomography II:
Image reconstruction and applications
Prof. Dr. U. OelfkeDKFZ HeidelbergDepartment of Medical Physics (E040)Im Neuenheimer Feld 28069120 Heidelberg, [email protected]
Uwe OelfkeContents
Motivation for CT
B i Ph i H fi ld U it Basic Physics – Hounsfield Units
Technical aspects: CT scanner
CT reconstruction
Spiral- and multi-slice CT
Image quality Image quality
Clinical applications
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Seite 2
Uwe Oelfke
CT- Reconstruction
Radon-transform
Backprojection
Filtered backprojection
Uwe OelfkeRadon-transform
Measurement: Line-integrals of attenuation coefficient distribution
A
rdnrprdlyxp ²)ˆ()(),()(
Sinogram: All projections as function of Φ,p:
)]([),( rRp
)()( ),(),( pyx
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Seite 3
Uwe OelfkeBackprojection:1st attempt of image reconstruction
)ˆ()( nrrf
)()()(0
rBdrfrfb
Summation of projection values for each voxel over all projection angles projection angles
Uwe OelfkeBackprojection
Object Backprojected of profiles
= reconstructed image )(rfb
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Seite 4
Uwe OelfkeBackprojection = Convolution with PSF
'²|'|
1)'()( rd
rrrrf
A
b Convolution:
1)(
rrhKernel, PSF
)()()( rBRrBrfb
))(()( rhrfb Deconvolution ?
))(()(f b
Uwe OelfkeImage reconstruction via deconvolution
Convolution theorem:
)()())(( 222 rhFrFrhF 222
1)()(
HrhF
1)(
rrhKernel, PSF
2 )()( HrhF
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Seite 5
Uwe OelfkeTheory:Image reconstruction via deconvolution
)()( 21
2 rBRFFrf
1. Backprojection
2. 2D-Fourier-transform
3. Multiplication with filter
4 In erse 2D Fo rier transform4. Inverse 2D-Fourier-transform
Uwe OelfkeCentral Slice-Theorem
Disadvantage of deconvolution approach: alle projections have to be known
Central-Slice-Theorem allows image reconstruction during image acquisition