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All will be made clear
Bill Thomson , City hospital Birmingham
Familiar Fourier Facts
a n1
LL
L
xf x( ) cosn xL
d
a 0
21
n
a n cosn x
Lb n sin
n xL
=
b n1
LL
L
xf x( ) sinn xL
d
F [g(s)*h(s)] = F [g(s)] x F [h(s)]
Fundamental convolution theorem
Question?
What is Butterworth?
80p a pound !!? ? ? ?
Filters
• Simple overview only
• No Maths
• What is Butterworth?
• Why Frequency? - Fourier
(Well , only a little!)
Use 1D profile data
0 20 40 60
pixels
counts
profile
Who to blame? Fourier
Jean Baptiste Joseph Fourier Auxerre , 1768 - 1830 nearly became a priest studied with Lagrange , Laplace arrested twice, nearly guillotined scientific adviser to Napoleon in Egypt theory of heat transfer used series of
sines , cosines 15 years before accepted and published
Fourier analysis
Represent a function by sums of sin and cos terms
easier maths
need to consider frequencies
sines and cosines
A
wavelength
A = size (amplitude)
Wavelength = distance (cm , pixels etc)
frequency = 1 / wavelength (cm-1 , pixels-1)
Amplitude = same
wavelength = 1/2
frequency = double
A
wavelength
-40
0
40
80
120
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
pixels
cou
nts
1st harmonic
Count Profile - Fourier fit
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profile data
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pixels
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profile data 1 harmonic
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
pixels
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nts
2nd harmonic
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
pixels
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nts
profile data 2 harmonics
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pixels
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nts
3rd harmonic
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pixels
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nts
profile data 3 harmonics
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pixels
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nts
4th harmonic
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pixels
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nts
profile data 4 harmonics
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
pixels
co
un
ts
8th harmonic
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profile data 8 harmonics
Amplitude - Frequency plot
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1st harmonic
0
0.2
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1
0 0.1 0.2 0.3 0.4 0.5
frequency - pixels-1
amp
litu
de
amp . freq
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2nd harmonic
0
0.2
0.4
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0.8
1
0 0.1 0.2 0.3 0.4 0.5
frequency - pixels-1
amp
litu
de
amp . freq
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0
40
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3rd harmonic
0
0.2
0.4
0.6
0.8
1
0 0.1 0.2 0.3 0.4 0.5
frequency - pixels-1
amp
litu
de
amp . freq
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0
40
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ts
8th harmonic
0
0.2
0.4
0.6
0.8
1
0 0.1 0.2 0.3 0.4 0.5
frequency - pixels-1
amp
litu
de
amp . freq
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pixels
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profile data
What Happens to Noisy Data?
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profile data
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160
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
pixels
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nts
profile data 3 harmonics
0
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pixels
cou
nts
profile data 8 harmonics
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0
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
pixelsc
ou
nts
8th harmonic
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pixels
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nts
profile data
-4
-3
-2
-1
0
1
2
3
4
0 0.1 0.2 0.3 0.4 0.5
frequency - pixels-1am
plit
ud
e
amp . freq
Power Spectrum
Normally plot (Amplitude)2 against frequency , on log scale
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-2
-1
0
1
2
3
4
0 0.1 0.2 0.3 0.4 0.5
frequency - pixels-1am
plit
ud
e
amp . freq
0
40
80
120
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
pixels
cou
nts
profile data
Tomography back projection
Blurring of back projection
Each true count is ‘blurred’ by 1/r function
X Y Z( )
How can we remove the 1/r blurring?
True data ‘blurred’ by 1/r = Back projection data
taking Fourier transform, F
‘blurring’ becomes simple multiplication
F (1/r) becomes 1/
so converting to Fourier , F i.e. in frequency terms
F(true data) x 1/ = F(back projection data)
F(true data) = F(back projection data) x
Ramp Function
F(true data) = F(back projection data) x
0
0.2
0.4
0 0.1 0.2 0.3 0.4 0.5
frequency - pixels-1
am
pli
tud
e
Problem !Amplifies higher frequencies
Noise at higher frequencies
Need to stop at a frequency which contains most signaland little noise
In theory , all done!
Butterworth Filter
Used to ‘cut-off’ the ramp effect
has two components -
order
cut-off
Butterworth Settings
Butterworth Filter
Butterworth cut-off 0.3
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6
Frequency
order 3 order 6 order 9 order 12
Butterworth order 6
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6
Frequency
0.25 0.3 0.35 0.4
Wiener Filter
Restorative filter amplifies mid range
frequencies depends on resolution (MTF)
and noise must have MTF file for
isotope and collimator factor ‘tweaks’ noise
component trust computer selection!
MTF()
MTF() 2 + 1/SNR()
SNR()=Object power
Noise power x multiplier
Maximum cut-off ?
data sample at 2x highest freq in the data pixel is smallest sample so, freqmax = 0.5 pixel-1
highest freq in patient data 1 cm-1
sample at 2cm-1 , pixel size 5mm
64x64 = 8mm 128 x128 = 3.6mm
• Sample = FWHM / 3 • resolution 15 - 18mm• sample at 5 - 6 mm
Automatic Filter
Uses power spectrum
10% noise means ‘rejects’ 90% of noise
based on analysis of four images
suggest use it for most clinical imaging
Software Revision
128 x 128 Butterworth , power spectrum changed
Spatial Frequency (cycles/ pixel) Spatial Frequency (cycles/ 2pixels)
Resolution effects
Resolution depends on
detector resolution
cut-off frequency of filter
if filter cutoff is low , filter determines resolution
Tomographic Noise
Cannot ignore noise in samplingNot simple ‘Poisson’ - complexproportional to
square root of cts per pixel (N) 1/2
fourth root of total pixels (P) 1/4
for the same ‘signal to noise’improve spatial resolution by 2counts must increase by 8
Bone images
Wiener Butterworth
Heart Images
Butterworth filter Wiener filter
2D Fourier
2D Filter of a Duck
2D Fouriertransform
Inverse Fourier
Partial Volume Effect
=FWHM x2x0.5
Cylinder phantom
low pass Wiener
Phantom Study
Two holes separated by diameter
‘building blocks’ join together
fill with Tc99m , tomo scan in water8mm 9mm 10mm 11mm 12mm 13mm
Twin hole phantom
Wiener Best ButterworthCut –off 0.45 pixel-1
Poor ButterworthCut off 0.26 pixel-1
Multi Hole Phantom
Hi-res coll
Butterworth
Gen purpose
Butterworth
Hi-res
Wiener
Heart phantom Study
Conclusions
Filter choice still very user dependent
essentially a balance of noise / detail
frequency needed for the maths behind the scenes
check if cycles/cm , cycles/pixel , cycles/(2 pixels)
higher frequencies needed for detail / resolution
Remember partial volume effect