Spectral Imaging: Methods, Design, and Applications - Biomedical Optical Imaging Technologies: Design and Applications - page 120

extended the excitation method to multiphoton excitation [ 9 ] and have shown
how it can be used to eliminate auto-fluorescence and distinguish dyes with
similar emission spectra.

4.3.2 Spectral Imaging Challenge: Information Versus Time

A spectral image contains significantly more information and data than a single-
color (gray-level) image and, thus, takes longer to acquire. When comparing the
total acquisition time of different methods, the common ground should be the time
it takes to acquire a spectral image of the same quality (or SNR).
Theoretically, the ideal system includes many shot-noise-limited detectors that
can measure simultaneously the entire spectral image. If, in addition, we could tune
the spectral-range sensitivity of each one of these detectors, it would provide the
ideal system. Such a detector may look like a cubical structure (Fig. 4.2 ) where each
box is a detector.
To date, no such detector is available (unless the field of view is compromised).
The total acquisition time continues to depend on the sequential number of times
that data has to be collected. It is clear, however, that the larger the number of
detector elements used in parallel, the shorter the measurement time will be.
As an example, assume that it is only required to measure an image with three
spectral components which are in the blue, green, and red spectral ranges. If a
gray-level CCD is used, it requires an acquisition of three images. There are also
sensors that enable to capture three color ranges simultaneously either by having a
three-layer array detector [ 10 , 11 ] or with a three-chip color CCD that uses dichroic
mirrors to capture the three colors simultaneously. Such devices require only a single
acquisition relative to a gray-level CCD that entails three filters sequentially and will

lead to an improvement of p 3 in the SNR (assuming the same quality of the detector
elements).
Such basic method would typically be in use only in cases where the samples'
colors and associated wavelengths looked for are known a priori, so that an image
would provide an answer to existence or nonexistence in the observed sample (e.g.,
measuring aneuploidy in cancerous cells where a specific gene is labeled with a
known fluorescent tag).
A fair comparison of different acquisition methods should be based on the time
it takes each method to obtain information that would suffice to satisfy the pre-
requisites of the measurement. The problem can be formulated as follows: In a given
amount of time, what is the optimal set of spectral-component images that enables
one to interpret the data with a given level of accuracy?
Time limitation can be critical for different reasons: high throughput that is
required in many biomedical applications, photobleaching in fluorescence, and
phototoxicity or image motion in applications such as retinal imaging.
Methods satisfying the question formulated above have been treated before. They
depend on the specificity of the spectra that have to be separated, and in the case