Description of ScanImage
ScanImage controls a laser-scanning microscope (Figure 1A). It is written entirely in MATLAB and makes use of standard multifunction boards (e.g. National Instruments, Austin, TX) for data acquisition and control of scanning. The software generates the analog voltage waveforms to drive the scan mirrors, acquires the raw data from the photomultiplier tubes (PMTs), and processes these signals to produce images. ScanImage controls three input channels (12-bits each) simultaneously, and the software is written to be easily expandable to the maximum number of channels the data acquisition (DAQ) board supports and that the CPU can process efficiently. The computer bus speed dictates the number of samples that can be acquired before an overflow of the input buffer occurs, while the CPU speed and bus speed combine to determine the rate of data processing and ultimately the refresh rate of images on the screen. Virtually no customized data acquisition hardware is required for either scan mirror motion or data acquisition.
Typical pixel times in biological laser scanning microscopy are > 2 μs (the pixel time depends on the pixels per line and the time per line; see Figure 2 for more information on the image construction algorithm). We have benchmarked ScanImage while acquiring three channels simultaneously with ~3 μs pixel times (Figure 1B). Under these demanding conditions, ScanImage updates and saves the images with minimal delay from the time they are actually obtained (Figure 1B). The actual size of the image (in microns) depends on the microscope optics and the angles navigated by the scan mirrors.
We have chosen MATLAB as our development environment for its powerful numerical manipulation abilities and data acquisition (DAQ) engine. The DAQ engine directly communicates with the acquisition board and presents a flexible, object-oriented environment in which to construct DAQ software. In many scientific and engineering communities, MATLAB has become the dominant language for desktop numerical computing [16]. Integration of custom written analysis functions and incorporation of additional software is much more tractable for novice programmers in MATLAB, simplifying much of the experimental design process.
Data Acquisition in ScanImage
The signal from the detector (typically a PMT) is a current that needs to be converted to a voltage before acquisition. This conversion can be done with a standard amplifier [17] (see Stanford Research Systems, Sunnyvale, CA for examples) equipped with a low pass filter. The roll-off frequency of the filter has to be <0.5 f
s, where f
s is the rate at which the detector signal is sampled. In ScanImage we compromise between the degradation in the signal-to-noise ratio due to limited sampling of the signal and the increasing computational load placed on the CPU with higher sample rates.
With current computer hardware we operate ScanImage with f
s = 1.25 MHz. This sampling frequency allows nearly ideal imaging (in terms of apparent quantum efficiency and bleedthrough between pixels, discussed below) for pixel times as short as 3 μs. An appropriate roll-off frequency would be ~300–500 kHz. In the future, faster computer architectures will allow more rapid sampling and number crunching and hence shorter pixel times. The sampled data is streamed to memory. An overview of the image construction algorithm used in ScanImage is shown in Figure 2A.
Sampled data are summed in real-time over the pixel times. Once the data is collected, it is compared with the output signals to the scan mirrors. The position of the scan mirrors dictates where the pixels lie in the final image. The mirror driver electronics (see Cambridge Technology, Cambridge, MA for examples) and the command voltage output to the drivers from the DAQ board determine the mirror position. During a cycle of the mirror motion useful data are collected only when the mirrors are moving at constant velocity (i.e. linear voltage versus time regime; see Figure 2B). Data acquired when the mirrors are turning around or flying back to their starting positions (flyback) are removed (Figure 2B; figure caption for details).
The timing for image formation requires synchronization of the mirror scan forms and the data acquisition engine. This is achieved by triggering data acquisition and data output using a TTL trigger generated by the DAQ board (i.e. under the control of a software panel). This mechanism for triggering also allows ScanImage to be externally triggered by a TTL from another application or hardware device. Appropriate sampling rates and output rates are hard-coded in ScanImage, while many other acquisition parameters can be chosen based on the demands of particular applications.
Quality of the Images Collected by ScanImage
We evaluated the performance of our signal conditioning method (Figures 2 &3). We compared the variability of the integral of a simulated PMT signal to the variability of the signal sum computed at a fixed sample rate as is done in ScanImage (see Figure 2B for details). In simulations the PMT voltage was modeled as a Poisson process with mean photon rate of 0.5 photons per μs. Each single-photon pulse was Gaussian with unit amplitude and 2.35 μs full-width-at-half-maximum (FWHM) [18]. Each simulated pulse was constructed at 500 MHz. A 20 ms waveform was created by summing Poisson-distributed pulses and dividing them into 2000 pixel intervals (duration 10 μs). Each of these intervals constituted a trial in the analysis of variability described below. Trials were sampled at 200 kHz to 5 MHz in 100 kHz steps. A typical trial sampled at different rates is shown in the inset of Figure 3A.
How does the signal to noise ratio vary with sampling frequency? A useful measure of the quality of the signal is the coefficient of variance (CVy). CVy is the ratio of the standard deviation (σy) to the mean (
) of the signal summed over a pixel interval (y). CVy is related to the apparent quantum efficiency (
) of the acquisition system and the mean number of pulses per pixel interval (
) by Equation 1:
is inversely related to the noise introduced by the signal conditioning in addition to the inherent shot noise of the light source and multiplicative noise due to the variable pulse-amplitude distribution [19]. For optimal sampling with uniform pulse amplitudes, will be 1 since 
= = (see Equation 1); this can be accomplished by direct integration of the signal (Figure 3A, red line). Thus we can interpret any reduction in from 1 as a decrease of the signal-to-noise ratio (SNR) of the system.
We verified our simulation by fitting the histogram of the number of events (N) to a Poisson model (Figure 3B). This analysis yielded a mean value for = 4.96 ± .09 (p < .05) photons per pixel interval (10 μs duration) as expected. We then analyzed the trials sampled at different frequencies to determine the effects of finite sample frequency on . Variation of the pulse amplitudes can be modeled directly by convolving the pulse amplitude distribution with our model, but does not alter the optimal signaling criteria; it merely reduces the maximum value of to a value lower than 1 [18].
was computed at each sample rate from the simulated pulses (Figure 3A). Note that the digital integrator shows nearly ideal behavior ( ≈ 1). As expected, for low sampling frequencies is small, reflecting the fact that data (single photon events) are missed by the sampling scheme. increases with sample rate until it approaches 1 around the Nyquist limit of 1 MHz (i.e. since we simulated the PMT voltage with 2.35 μs pulses). We repeated this analysis with data coming from an actual PMT filtered at ~330 KHz (blue points in Figure 3A). The lower filtering frequency broadens the pulses (3–4 μs FWHM) and hence increases the performance of our algorithm at lower sample rates. At 1.25 MHz, the image has an essentially ideal .
We also modeled the expected bleedthrough of photons from adjacent pixels (Figure 3C). A simulated test pulse with a FWHM of 2.5 μs was placed randomly in pixel intervals of widths 2 to 10 μs. The percent bleedthrough was calculated as the percentage of the area of the pulse that fell outside of the pixel interval in which it was placed. We see that the bleedthrough for the shortest pixel times recommended (~3.0 μs) is ~10%. Longer pixel times under normal imaging conditions (Pixel Interval/Pulse Width ~2) yield a bleedthrough of only ~6%.
Currently ScanImage performs well with a 1.25 MHz acquisition rate and a 330 kHz filter frequency (blue points and arrow in Figure 3A) depending on the application. Using sampling rates above 1.25 MHz (together with higher filter frequencies) is currently an issue of bus and CPU speed as mentioned. The sample rate can be adjusted manually in the software to a multiple of 1.25 MHz (e.g. 2.5 MHz) without any corruption of image quality. This yields an effective range of pixel times of 2–3-μs. Pixel times as low as 2 μs are feasible with a 500 kHz filter acquiring at 1 ms per line.
) of the signal (see inset) determined by digital integration. The black trace shows 
) of the signal summed over a pixel interval (y). CVy is related to the apparent quantum efficiency (
) by Equation 1:
= 