Understanding Fluorescence Depth Sensitivity

Fluorescence imaging systems face a fundamental challenge: detecting signals through tissue, which alters both the path and intensity of light. This depth sensitivity determines what we can visualize during procedures like fluorescence-guided surgery, with detection limits governed by both tissue properties and imaging system characteristics. Here we present core concepts, practical considerations, and testing methods for evaluating fluorescence imaging performance through tissue.

The Challenge of Depth Detection

Imagine photographing the moon on a cloudy night. Just as a thin layer of clouds obscures the moon's surface features, a small amount of tissue can blur fluorescent signals. When cloud cover thickens, even locating the bright moon becomes challenging - similar to how deeper tissue layers increasingly mask fluorescent targets. This effect becomes even more pronounced with dimmer and smaller objects like stars, which become virtually invisible through clouds, much like how small or dim fluorescent signals become undetectable through tissue.

When light enters tissue, it encounters two main types of interactions that affect its path and intensity: absorption and scatter.

• Absorption: Think of absorption as light being converted into other forms of energy, typically heat. Different components in tissue, called chromophores, absorb light in distinct ways. For example, blood strongly absorbs visible light, which is why we see it as red - it absorbs most other colors! The absorption coefficient (μ_a), which tells us how likely light is to be absorbed over a given distance (units: 1/mm or 1/cm).
• Scattering: Scattering occurs when light changes direction as it bounces off tissue structures. In tissue, we describe scattering using the scattering coefficient (μ_s), but we often use the reduced scattering coefficient (μ_s') which accounts for the tendency of tissue to scatter light mostly forward rather than in random directions. The way tissue scatters light forward rather than randomly is described by a property called anisotropy (g). This value ranges from -1 (complete backward scattering) to 1 (complete forward scattering). Most tissues have g values between 0.80 and 0.95, indicating that light tends to maintain its general forward direction even after multiple scattering events. 

In fluorescence imaging, excitation light must travel through the superficial tissue layers to reach the fluorescent material or contrast agent in order to excite it. Once excited, the material or contrast agent emits fluorescence in all directions. Only some of the light will reach the surface and be detected by the imaging system. Note that the probability of light interactions depends on the wavelength of light. This is why near-infrared (NIR) fluorophores often perform better at depth – NIR light experiences less absorption and scattering in tissue as compared to UV/visible light.

Optical Phantoms

Optical phantoms made with well-characterized materials and fluorescent contrast agents serve as reference targets for evaluating imaging system performance. By incorporating an obscuring layer of tissue-mimicking material above a uniform fluorescent layer, these phantoms help quantify how well systems can detect fluorescent signals through different types of tissue.

Consider our example below, which demonstrates fluorescence detection through three different materials: highly scattering, clear, and highly absorbing. The uniform fluorescent layer underneath, which could represent a large tumor, appears distinctly different when viewed through each material due to their unique optical properties.

This is the same challenge observed during surgery, where various tissue types interact with light in different ways. Also, the size and shape of the fluorescent object affects how big and bright it appears on the camera; this affects how easily detected the target is. Regions that are large or brightly fluorescent are much easier to detect than dim or small regions.

Returning to the moon and stars example, the large moon is more easily seen even when veiled by clouds, whereas small or dim stars may only appear when the sky is clear with little light pollution. As fluorescence-guided surgery moves towards enhancing cancer resections, large primary tumors may be more easily visualized through thick layers of tissue, whereas small residual nodes are more difficult to identify, requiring an imaging system with better depth sensitivity.

Characterizing Your System’s Depth Sensitivity

Imaging system characterization using QUEL’s tissue-mimicking phantoms offers consistency and reliability: with known fluorophore concentrations, precisely controlled depths, and tissue-equivalent optical properties, QUEL products can provide reproducible measurements, shelf stability, and consistency between different testing sessions.

1. Using your fluorescent imaging system, take images of a tissue-equivalent depth target under typical conditions (working distance, ambient lighting, system settings).
   Example images are provided below, where the well with the shallowest depth of scattering or absorbing “tissue” above the fluorescent material appears brightest and the well with the thickest “tissue” will appear dimmest.
2. For each of the circular wells, measure the pixel intensity. The TG311 consensus recommends using a region of interest, "within the interior of the region to encompass about half the diameter." This can be automated by using the QUEL-QAL Python library.
3. Plot the mean pixel intensity vs. tissue-equivalent depth. This is plotted in the example below. A fit of the data to an exponential equation in order to estimate fluorescence signal at any depth. The contrast-to-noise ratio (CNR) can also be used in place of mean pixel intensity, where a CNR of 3 is considered the limit of detection. 

Best Practices for Development

The goal isn't always maximum depth penetration, but rather reliable detection at clinically relevant depths with appropriate sensitivity. A stronger fluorescent signal doesn't always mean better depth detection. It is important to use a phantom that best represents the clinical context:

• What are you trying to detect? The size and brightness of your target matters. Large, bright tumors are easier to detect than small metastases, just as it's easier to see the moon than distant stars on a cloudy night.
• What tissue types will you encounter? Different tissues have different optical properties. Imaging through muscle is different from imaging through fat or blood.
• What depth sensitivity do you need? Not every application requires maximum depth penetration. Sometimes, reliable detection at a specific, clinically relevant depth is more important.
• What are your system characteristics? What are the wavelengths of the system’s excitation source and of the fluorescence imaging camera?

Test your system using the same settings (camera exposure time, camera gain, working distance, ambient lighting conditions, etc.) that will be used in clinical practice. If your system has different operating modes, characterize each one separately. By understanding these principles and following proper testing procedures, you'll be better equipped to develop and validate your fluorescence imaging system's depth sensitivity capabilities.

For more detailed guidance on system characterization and standardization, refer to the AAPM TG311 guidelines. Implementation tools and reference targets are available to help you meet these standards effectively.

Interested in characterizing your imaging system or developing a customized fluorescence reference target? Contact QUEL Imaging!