Key points are not available for this paper at this time.
In this paper we describe properties of a video imaging system used to acquire voltage-sensitive dye fluorescence signals from the salamander olfactory bulb. Sources of noise in these signals were evaluated in preparations stained with the potentiometric probe RH-414. These were compared with noise levels in signals obtained from a light-emitting diode array designed to stimulate the experimental conditions with light levels similar to those seen in the salamander bulb recordings. These experiments define a number of determinants of video image quality to standardize optical voltage measurements in the salamander olfactory bulb. 2. Images were acquired at video rates using a Newvicon camera in a standard upright microscope and digitized with an eight-bit video frame grabber. 3. Sources of noise related to camera sensitivity, stability of illumination, and mechanical vibration were characterized. Camera dark noise was less than the pixel variability due to photon noise at the camera faceplate. This pixel noise was the limiting factor for discriminating the spatial and temporal properties of the optical responses. 4. No significant noise was found to be related to image digitization, transmission, or readout by the eight-bit frame grabber. Mechanical vibration, light stability, and other sources of noise could be controlled in vitro. In this condition, voltage-sensitive dye signal noise was similar to that in stimulated experiments using the light-emitting diode array. Higher levels of noise were found in vivo; some of this was reduced by sychronizing frame acquisition to the heartbeat. 5. On the basis of photodiode and video measurements, voltage-sensitive dye responses in the salamander olfactory bulb typically fell between 0.75% and 2.5% fractional change of background fluorescence. By appropriately adjusting the video signals before analog-to-digital conversion, we could detect fractional changes of < 0.5%. 6. Both response averaging and low-bandpass spatial filtering improved the signal-to-noise ratios of the images. For small numbers of averaged runs, the best improvement was obtained by low-bandpass spatial filtering. 7. Acquisition of high-spatial resolution video images permitted the use of low-bandpass spatial filters to suppress pixel noise. The degree of spatial enhancement depended on the relationship between the size of the structures of interest, pixel density, and the properties of the convolution filter kernel. This method avoided exposure of the preparation to prolonged illumination and the necessity of applying the large numbers of repeated stimuli required for averaging.
Cinelli et al. (1995) studied this question.