An Approach for Characterizing and Comparing Hyperspectral Microscopy Systems

Abstract

Abstract:Hyperspectral imaging and analysis approaches offer accurate detection and quantification of fluorescently-labeled proteins and cells in highly autofluorescent tissues. However, selecting optimum acquisition settings for hyperspectral imaging is often a daunting task. In this study, we compared two hyperspectral systems-a widefield system with acoustic optical tunable filter (AOTF) and charge coupled device (CCD) camera, and a confocal system with diffraction gratings and photomultiplier tube (PMT) array. We measured the effects of system parameters on hyperspectral image quality and linear unmixing results. Parameters that were assessed for the confocal system included pinhole diameter, laser power, PMT gain and for the widefield system included arc lamp intensity, and camera gain. The signal-to-noise ratio (SNR) and the root-mean-square error (RMS error) were measured to assess system performance. Photobleaching dynamics were studied. Finally, theoretical sensitivity studies were performed to estimate the incremental OPEN ACCESSSensors 2013, 13 9268 response (sensitivity) and false-positive detection rates (specificity). Results indicate that hyperspectral imaging assays are highly dependent on system parameters and experimental conditions. For detection of green fluorescent protein (GFP)-expressing cells in fixed lung tissues, a confocal pinhole of five airy disk units, high excitation intensity and low detector gain were optimal. The theoretical sensitivity studies revealed that widefield hyperspectral microscopy was able to detect GFP with fewer false positive occurrences than confocal microscopy, even though confocal microscopy offered improved signal and noise characteristics. These studies provide a framework for optimization that can be applied to a variety of hyperspectral imaging systems.

Excitation-scanning hyperspectral imaging microscope

“…Results from imaging GFP-labeled cells within autofluorescent lung tissues indicate that the excitation and emission spectra for both GFP 2,9,15 and Hoechst 15,36,37 are consistent with previous values reported in the literature. The emission spectrum from lung autofluorescence is also consistent with our previous studies using AOTF-based and TFTF-based emission-scanning hyperspectral imaging systems.…”

“…9,15,36 This is likely because the lung contains very high concentrations of elastin and collagen, when compared with other tissues. However, autofluorescence is present in many tissue types, and may be attributed to other endogenous fluorophores, such as flavin adenine dinucleotide (FAD), nicotinamide adenine dinucleotide (NADH), and collagen.…”

Hyperspectral imaging fluorescence excitation scanning for colon cancer detection

“…We have previously shown that hyperspectral imaging using fluorescence emission scanning can be used to accurately detect discrete molecular signals in cells and tissues. 41,[56][57][58][59][60] However, we have found that fluorescence excitation scanning provides 10-to 30-fold higher signal sensitivity than traditional (emission-based) spectral imaging approaches. 55 This technology allows fluorescence and absorbance image data to be acquired across a range of narrow-wavelength illumination bands, spanning the ultraviolet (UV) through visible spectrum and can easily be adapted for endoscopic use.…”

A theoretical‐experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods

“…Second, the variability of biomedical spectral imaging applications and hardware has made it difficult to quantitatively compare the effectiveness of different analysis approaches. Hence, it may be difficult to assess which approach is best for a given biomedical spectral imaging scenario [65].…”

“…However, only a limited amount of research has been performed to apply many of the spectral image processing algorithms developed by the remote sensing community to biomedical applications [10]. Even less work has been done to quantitatively assess the effectiveness of spectral image analysis algorithms as applied to biological image data or to compare algorithms or spectral imaging systems to each other [10,[65][66][67]. Hence, it is not always clear which spectral imaging platform or analysis algorithm should be used for a particular biomedical imaging application.…”