ScanImage: flexible software for operating laser scanning microscopes

Abstract

Background: Laser scanning microscopy is a powerful tool for analyzing the structure and function of biological specimens. Although numerous commercial laser scanning microscopes exist, some of the more interesting and challenging applications demand custom design. A major impediment to custom design is the difficulty of building custom data acquisition hardware and writing the complex software required to run the laser scanning microscope.

Results: We describe a simple, software-based approach to operating a laser scanning microscope without the need for custom data acquisition hardware. Data acquisition and control of laser scanning are achieved through standard data acquisition boards. The entire burden of signal integration and image processing is placed on the CPU of the computer. We quantitate the effectiveness of our data acquisition and signal conditioning algorithm under a variety of conditions. We implement our approach in an open source software package (ScanImage) and describe its functionality.

Conclusions: We present ScanImage, software to run a flexible laser scanning microscope that allows easy custom design.

Figure 1

Wiring Diagram and Performance Benchmarking for ScanImage A) Typical wiring diagram for a microscope running ScanImage. Scanning, data acquisition, and laser shuttering are accomplished by a single data acquisition board (PCI 6110, National Instruments). Stage and Z-focus motors are controlled through a Sutter 3-axis motor controller (Sutter MP285) that is programmed through a serial communications (COM) port. B) Performance of ScanImage. We computed the realtime fraction as the average time it took ScanImage to process and display the acquired data (i.e. a stripe; see Figure 2) normalized to the acquisition time for that portion of data. Values were averaged over 20 frames acquired at each configuration. The realtime fraction was computed using an 800 MHz Pentium III computer with 512 MB of RAM. A realtime fraction above one indicates the software was able to run in realtime (i.e. ScanImage had completed processing and displaying all of the previously collected data before the next portion of data was fully acquired). For realtime fractions less than one, the display lags the acquisition but no data is lost. The low realtime fraction when acquiring 16 lines per frame on multiple channels indicates that we are approaching the fundamental speed limit for graphics updates.

Figure 2

Image Construction and Signal Processing in ScanImage A) Schematic depicting the algorithm of image creation in ScanImage. Pixels are constructed by summing the PMT voltage over the pixel time T_p. To achieve fast (i.e. realtime) refresh rates we break the acquisition of an image down into a set of stripes acquired and displayed in succession. The stripes are concatenated to form the entire image. B) Further details of image construction in ScanImage. a) Mirror position signal delivered by ScanImage (blue trace) and the actual mirror position (red trace) for two lines of acquisition (PMT voltage shown in gray). b) Definitions of acquisition parameters. The flyback (purple) and line delay (red) regions of the acquired PMT signal are discarded from final image data, which keeps only the fill fraction (blue) of the PMT voltage for image construction. Because the scan mirrors have inertia, their actual position lags the set position by ~7% of the scan period, which is corrected by the cusp delay (green). c) ScanImage retains the data acquired during the linear portions of the scan (blue) and discards the intervening data (red). d) Samples are summed to form the pixels in the final image.

Figure 3

Image Quality in ScanImage A) Apparent quantum efficiency as a function of sampling rate in ScanImage for a single-photon-pulse with a 2.35 μs FWHM and unit amplitude. The red trace shows the apparent quantum efficiency () of the signal (see inset) determined by digital integration. The black trace shows sampled at 200 kHz to 5 MHz. approaches 1 as the sample rate approaches 1 MHz. The blue points are the normalized apparent quantum efficiencies from an actual PMT illuminated by a constant light source (data pre-filtered at 330 kHz). ScanImage uses a sample rate of 1.25 MHz (arrow), which yields nearly ideal signal-to-noise ≈ 1. This analysis is only valid for pulses that are al least 2.35 μs in duration. Inset: 20-μs pulse interval comprised of 2.35-μs events sampled at different rates. B) Histogram of the events in the pixel interval (bars) and Poisson fit (line). C) Model of bleedthrough of photons from adjacent pixels. A single-photon-pulse with a 2.5 μs FWHM was placed randomly in pixel intervals of widths 2 to 10 μs. The number of trials at each pixel interval was 10,000. The percent bleedthrough equaled 100*(1 - IPP/IEP) where IEP is the integral of the entire pulse and IPP is the integral of the region of the pulse that fell in the pixel interval. Shown is the mean value of the percent bleedthrough for each pixel time normalized with respect to the pulse width (Pixel Interval/Pulse Width). The error bars (SEM) were smaller than the marker points and were omitted.

Figure 4

Flowchart and Screenshot of ScanImage A) ScanImage uses a series of text files to remember configurations for different microscopes, users, and experimental configurations. Each aspect of the software and the associated files is detailed in the ScanImage manual. B) A screenshot of ScanImage acquiring data from 2 channels simultaneously.