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Detecting Electrons: CCD vs Film. Practical CryoEM Course July 26, 2005 Christopher Booth. Overview. Basic Concepts Detector Quality Concepts How Do Detectors Work? Practical Evaluation Of Data Quality Final Practical Things To Remember. Basic Concepts.

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detecting electrons ccd vs film

Detecting Electrons: CCD vs Film

Practical CryoEM Course

July 26, 2005

Christopher Booth

overview
Overview
  • Basic Concepts
  • Detector Quality Concepts
  • How Do Detectors Work?
  • Practical Evaluation Of Data Quality
  • Final Practical Things To Remember
basic concepts
Basic Concepts
  • Fourier Transform and Fourier Space
  • Convolution
  • Transfer Functions
    • Point Spread Function
    • Modulation Transfer Function
  • Low Pass Filter
fourier transform
Fourier Transform

The co-ordinate (ω) in Fourier space is often referred to as spatial frequency or just frequency

convolution in fourier space
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
Low Pass Filter
  • Reducing or removing the high frequency components
  • Only the low frequency components are able to “pass” the filter

x

=

transfer functions
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
transfer functions1
Transfer Functions
  • In Fourier Space this representation is simplified

=

x

point spread function psf
Point Spread Function (PSF)
  • The blurring of an imaginary point as it passes through an optical system
  • Convolution of the input function with a
modulation transfer function mtf
Modulation Transfer Function (MTF)
  • A representation of the point spread function in Fourier space

=

x

summarize basic concepts
Summarize Basic Concepts
  • 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
Detector Specific Concepts
  • Nyquist Frequency
  • Dynamic Range
  • Linearity
  • Dark Noise
nyquist frequency
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
quantum efficiency
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
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
  • 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
Summarize CCD Specific Terms
  • Nyquist Frequency, must sample image at 2x the highest frequency you want to recover
so why does anyone use film
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 film detects electrons
How Film Detects Electrons

Incident electrons

Silver Emulsion

Film

how film is scanned
How Film Is Scanned

Incident Light

Developed Silver Emulsion

Film

Scanner CCD Array

summary of detection methods
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
calculating spectral signal to noise ratio
Calculating Spectral Signal To Noise Ratio
  • Signal To Noise Ratio is more meaningful if we think in Fourier Space
calculating the fourier transform of an image
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

relating snr s to resolution
Relating SNR(s) To Resolution

2/5 Nyquist Frequency

further experimental confirmation of 2 5 nyquist
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

evaluate your data to estimate the quality of your imaging
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
final practical things to remember
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
Normalization
  • Standard Normalization
  • Quadrant Normalization
magnification of ccd relative to film
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
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
Conclusion
  • Understand what you are trying to achieve and use the detector that will make your job the easiest
  • Check Your Own Data!
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