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.

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.

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 (CV_y). CV_y is the ratio of the standard deviation (σ_y) to the mean () of the signal summed over a pixel interval (y). CV_y 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.

We now briefly describe the software that we have created to turn the data acquisition algorithm into a full image acquisition program. The features presented here can be easily extended through the MATLAB interface. The wiring diagram for a LSM using ScanImage is given in Figure 1A.

Many applications, including quantitative calcium imaging, can involve the use of two dyes with different spectral properties (e.g. green calcium sensitive, Fluo-4, and red calcium insensitive, Alexa Fluor-594) 15. Optics can be easily designed to separate and simultaneously collect both of the emitted wavelengths in separate channels. We have integrated support for processing 3 separate fluorescence channels independently in ScanImage. The data are collected, processed, and displayed simultaneously in real time (Figure 1B).

The microscope focus is under the control of ScanImage allowing automated collection of stacks of images from different focal planes. These stacks can be set and collected easily in ScanImage using any configuration. Kalman filtering can be implemented per focal plane for noise reduction.

ScanImage stores data in TIF files for easy portability and compatibility. TIF files can include multiple frames for storage of three-dimensional, multi-channel, and time series data in the same file. The pixel intensities are saved as 16-bit unsigned integers, exploiting the full dynamic range of the detector and DAQ boards. Different acquisition channels are interleaved. The experimental details are written into a header string that is placed into the ImageDescription field of the generic TIF header, and can be accessed via the standard header reading modules included in all image-processing programs, including MATLAB.

LSM often involves finding the region (or regions) of interest (ROIs) in a complex and large specimen. The ability to select and save ROIs can help in the design of complex imaging experiments. ScanImage allows the user to define an arbitrary number of ROIs for imaging. ScanImage uses its control of the stage and focus to automate collection of focal stacks of data as well as for saving and retrieving ROIs. Through a system of experimental cycles it is possible to select multiple ROIs and image them in succession at a selected repetition rate any number of times.

Before data acquisition the structure to be imaged needs to be located. For this purpose a rapid acquisition mode is necessary where the user can observe the image in realtime as the specimen and focal positions move. ScanImage includes a focus mode to accomplish this. The image seen in focus mode is identical to the one that will be acquired. Multiple channels can be focused simultaneously in realtime. Image zooming and rotation are accomplished in the focus mode by changing the signal output to the mirrors.

One of the key advantages of ScanImage is the user-friendly development environment, MATLAB. Users can easily develop and test data analysis functions off-line and incorporate them into the software without fear of interfering with the data acquisition. A flowchart of ScanImage from a programming perspective is given in Figure 4A and a screenshot of the software is shown in Figure 4B.

Any MATLAB function can be automatically executed after an image is acquired to aid in analysis of the experiment while it is being conducted (see ScanImage manual for details). In addition to MATLAB functions, programmers can compile C and Fortran code and run it with MATLAB calls in the same user function.

From a programming perspective, the code allows the generation of custom graphical interfaces that can be incorporated easily into ScanImage (see Figure 4A). As an example, additional serial ports may need to be integrated to run other optical equipment. A GUI can be constructed with its own initialization file and run in parallel to ScanImage (see the ScanImage manual for details). This GUI can use the same convenient tools for handling GUIs that ScanImage uses while offering the programmer flexibility in both design and development.

ScanImage is freely available as OSI Certified open source software. The license can be found in the ScanImage folder in the ScanImageLicense.txt file. All terms of the license, including details on referencing ScanImage in published reports, must be followed when using ScanImage.

The software and documentation on installation are available from: http://svobodalab.cshl.edu/software_main.html.

Please email any questions or suggestions about the software to scanimage@cshl.edu

All programming and benchmarking was done in MATLAB 6.1 running on a Windows 2000 Professional PC with an 800 MHz Pentium III processor and 512 MB of RAM.