Transcript Document 7353448

Detecting Electrons: CCD vs Film
Practical CryoEM Course
July 26, 2005
Christopher Booth
Overview
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Basic Concepts
Detector Quality Concepts
How Do Detectors Work?
Practical Evaluation Of Data Quality
Final Practical Things To Remember
Basic Concepts
• Fourier Transform and Fourier Space
• Convolution
• Transfer Functions
– Point Spread Function
– Modulation Transfer Function
• Low Pass Filter
Fourier Transform
The co-ordinate (ω) in Fourier space is often
referred to as spatial frequency or just
frequency
Graphical Representation Of The Fourier
Transform
Convolution
Convolution In Fourier Space
• Convolution in Real Space is
Multiplication in Fourier Space
• It is a big advantage to think in Fourier
Space
Low Pass Filter
• Reducing or removing the high frequency
components
• Only the low frequency components are able to
“pass” the filter
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Transfer Functions
• A transfer function is a representation of the
relation between the input and output of a linear
time-invariant system
• Represented as a convolution between an
input and a transfer function
f output ( x)  f input ( x)  t ( x)
f output ( x)   f input ( y )  t ( x  y )dy
Transfer Functions
• In Fourier Space this representation is simplified
X output ( s )  X input ( s )  T ( s )
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Point Spread Function (PSF)
• The blurring of an imaginary point as it
passes through an optical system
• Convolution of the input function with a
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Modulation Transfer Function (MTF)
• A representation of the point spread
function in Fourier space
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Summarize Basic Concepts
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Fourier Transform and Fourier Space
Convolution describes many real processes
Convolution is intuitive in Fourier Space
Transfer Functions are multiplication in Fourier
Space
• MTF is the Fourier Transform Of the PSF
• MTF is a Transfer Function
• Some Filters are easiest to think about in Fourier
Space
Detector Specific Concepts
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Nyquist Frequency
Dynamic Range
Linearity
Dark Noise
Nyquist Frequency
• Nyquist-Shannon Sampling Theorem
• You must sample at a minimum of 2 times
the highest frequency of the image
• This is very important when digitizing
continuous functions such as images
Example Of Sampling Below Nyquist
Frequency
Quantum Efficiency
• The Quantum Efficiency of a detector is
the ratio of the number of photons
detected to the number of photons incident
Dynamic Range
• The ratio between the smallest and largest
possible detectable values.
• Very important for imaging diffraction
patterns to detect weak spots and very
intense spots in the same image
Linearity
• Linearity is a measure of how consistently
the CCD responds to light over its well
depth.
• For example, if a 1-second exposure to a
stable light source produces 1000
electrons of charge, 10 seconds should
produce 10,000 electrons of charge
Summarize CCD Specific Terms
• Nyquist Frequency, must sample image at 2x
the highest frequency you want to recover
Dynamic
Range
Linearity
CCD
Quantum
Efficiency
(%)
50 – 90
10,000
Very linear
Film
5 – 20
100
Limited linearity
So Why Does Anyone Use Film?
• For High Voltage Electron Microscopes,
the MTF of Film is in general better than
that of CCD at high spatial frequencies.
• If you have an MTF that acts like a low
pass filter, you may not be able to recover
the high resolution information
How a CCD Detects electrons
Electron Path After Striking The Scintillator
100 kV
200 kV
300 kV
400 kV
How Readout Of the CCD Occurs
How Film Detects Electrons
Incident electrons
Silver Emulsion
Film
Silver Grain Emulsion At Various
Magnification
How Film Is Scanned
Incident Light
Developed Silver Emulsion
Film
Scanner CCD Array
Options For Digitizing Film
Summary Of Detection Methods
• Scintillator and fiber optics introduce some
degredation in high resolution signal in
CCD cameras
• Film + scanner optics introduce a
negligible amount of degredation of high
resolution signal
Practical
Evaluation Of The
CCD Camera
Decomposing Graphite Signal
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Calculating Spectral Signal To Noise Ratio
• Signal To Noise Ratio is more meaningful
if we think in Fourier Space
PowerSpectrum( s)  Noise ( s)
SNR( s) 
Noise( s)
Calculating The Fourier Transform Of an
Image
Image Of Carbon Film
• amorphous (non crystalline) specimen
• not beam sensitive
• common
Also called the power
spectrum of the image
Power Spectrum Of Amorphous Carbon On
Film and CCD
Comparing The Signal To Noise Ratio From
Film and CCD
Film Vs CCD Head-To-Head
CCD
Linearity
Quantum
Efficiency
Dynamic Range
MTF
Film
Calculating
SNR for Ice
Embedded
Cytoplasmic
Polyhedrosis
Virus
Reconstruction To 9 Å Resolution
Confirming A 9 Å Structure
Relating SNR(s) To Resolution
2/5 Nyquist Frequency
Further Experimental Confirmation Of 2/5
Nyquist
Table 2: Comparison of Reconstruction Statistics between Several Different Ice Embedded Single Particles
Collected On the Gatan 4kx4k CCD at 200 kV at the Indicated Nominal Magnification
Final Resolution
(0.5 FSC cutoff, Å)
Software
Package For
Reconstructi
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Complex
Number Of
Particles
Nominal Microscope
Magnification
Expected
Resolution (Å) at
2/5 Nyquist
CPV
5,000
60,000
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SAVR
GroEL
8,000
80,000
6.8
7-8
EMAN
Ryr1
29,000
60,000
9
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EMAN
Epsilon Phage
15,000
40,000
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EMAN/SAVR
Evaluate Your Data To Estimate The Quality
Of Your Imaging
• You can use ctfit from EMAN to calculate a
spectral signal to noise ratio
– Built In Method
– Alternate Method Presented Here
PowerSpectrum( s)  Noise ( s)
SNR( s) 
Noise( s)
Final Practical Things to Remember…
• Good Normalization Means Good Data
– Dark Reference
– Gain Normalization
– Quadrant Normalization
• Magnification Of CCD relative to Film
• Angstroms/Pixel
Normalization
• Standard Normalization
I final ( x) 
I acquired ( x)  I dark _ reference ( x)
I gain_ reference ( x)
• Quadrant Normalization
Quadrant Normalization
Dark Reference
Gain Normalization
How Do I Tell If Something Is Wrong?
Magnification Of CCD relative to Film
• 2010F Mag x 1.38 = 2010F CCD Mag
• 3000SFF Mag x 1.41 = 3000SFF CCD Mag
• This has to be calibrated for each microscope
detector.
How Do I Calculate Angstroms/Pixel?
• Å/pixel = Detector Step-Size/Magnification
• For a microscope magnification of 60,000 on the
3000SFF:
• Å /pixel = 150,000 Å / (microscope magnification x 1.41)
• Å /pixel = 150,000 Å / (60,000 x 1.41)
Å /pixel = 1.77
Conclusion
• Understand what you are trying to achieve
and use the detector that will make your
job the easiest
• Check Your Own Data!