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

• 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

dyyxtyfxf

xtxfxf

inputoutput

inputoutput

)()()(

)()()(

Transfer Functions

• In Fourier Space this representation is simplified

)()()( sTsXsX inputoutput

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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

• 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

• 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

Quantum Efficiency (%)

Dynamic Range

Linearity

CCD 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

Silver Emulsion

Film

Incident electrons

Silver Grain Emulsion At Various Magnification

How Film Is Scanned

Developed Silver Emulsion

Film

Incident Light

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

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Calculating The Fourier Transform Of an Image

Also called the power spectrum of the image

Image Of Carbon Film• amorphous (non crystalline) specimen• not beam sensitive• common

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 Film

Linearity

Quantum Efficiency

Dynamic Range

MTF

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

ComplexNumber Of Particles

Nominal Microscope Magnification

Expected Resolution (Å) at

2/5 Nyquist

Final Resolution (0.5 FSC cutoff, Å)

Software Package For Reconstructi

on

CPV 5,000 60,000 9 9 SAVR

GroEL 8,000 80,000 6.8 7-8 EMAN

Ryr1 29,000 60,000 9 9.5 EMAN

Epsilon Phage 15,000 40,000 13.6 13 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

)(

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sNoise

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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

• Quadrant Normalization

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xI

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referencegain

referencedarkacquiredfinal

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!