Fluorescence Uniformity: Anatomy of Accurate Imaging (Part 1)

Imaging performance can be assessed in many ways, but two of the most critical aspects are how accurately an imaging system captures both fluorescence intensity and spatial structure. Imagine looking through a flawed magnifying glass: the center appears bright and sharp, while the edges are dim and distorted. This kind of variation can have serious consequences in fluorescence-guided surgery, where reliable interpretation of labeled tissues depends on both signal consistency and spatial precision. Surgeons rely on high-performance imaging systems to visualize tissue margins and anatomical landmarks — even subtle errors can directly influence clinical outcomes.

Two key parameters define this performance: uniformity, which describes how consistently fluorescence is detected across the field of view, and geometric distortion, which refers to spatial shifts or warping of anatomical features. In this two-part post, we begin with a look at fluorescence uniformity and will explore image distortion in Part 2.

The Challenge of Uniformity

Fluorescence Signal Uniformity refers to how consistently the system captures fluorescence across the entire field of view. In an ideal system, a uniformly fluorescing region would appear equally bright everywhere in the image. In reality, however, most imaging systems show reduced sensitivity toward the edges of the field of view.

Similar to the QUE "E" phantom in the animation at the right, after ICG administration a well-perfused blood vessel could appear brighter in the center of an imaging field but poorly perfused at the edge. Is this a true biological difference or an artifact of the imaging system? This issue becomes more important when fluorescence is used to enhance the contrast of cancerous tissue. 

Introducing the Reference Uniformity and Distortion (RUD) Target

QUEL Imaging's Reference Uniformity and Distortion (RUD) target enables the simultaneous characterization of both fluorescence signal uniformity and image distortion.

The RUD target consists of a grid of equally-spaced wells filled with luminescent material embedded within a non-fluorescent, light-absorbing matrix. Illuminating the target with the appropriate excitation wavelength causes fluorescence to be emitted from the wells. By analyzing how this pattern appears in the imaging system, both uniformity and distortion can be quantified precisely.

Characterizing Your System’s Fluorescence Signal Uniformity

By analyzing how uniform the wells appear in the fluorescence image, signal uniformity across the entire field of view can be assessed in a few simple steps:

1. Using your fluorescent imaging system, take images of the RUD target under typical conditions (working distance, ambient lighting, system settings). Position the RUD target orthogonal to the imaging axis, and multiple images can be stitched together to analyze uniformity over a large field of view.
2. In the fluorescence image, identify the location and mean intensity of each well. This can be done automatically with the QUEL-QAL Python library.
3. The QUEL-QAL library then uses interpolation and fitting techniques to generate a map of fluorescence intensity across the field of view.
4. The intensity profile can be analyzed by characterizing the horizontal and vertical line profiles across the field of view.
5. The intensity map can also generate an isomap showing regions where the fluorescence intensity is uniform within a specific percentage of the maximum intensity.

For detailed step-by-step instructions, follow Use Guide: Uniformity and Distortion Targets.

Best Practices for Development

Understanding and correctly measuring uniformity is crucial for developing effective imaging systems. While the analysis may seem complex, focusing on practical considerations will help ensure your system meets clinical needs:

• What clinical decisions will be based on fluorescence intensity? Consider whether you need high uniformity across the entire field of view or if a smaller region of quantitative measurement is acceptable for your application.
• Will users need to know which regions provide reliable quantitative data? Consider whether to implement on-screen indicators showing the boundaries of reliable display.
• How will tissue topography effects on fluorescence signal uniformity be addressed? Curved or irregular surfaces can significantly impact signal distribution beyond the optical system's inherent performance.
• How will users be informed of fluorescence signal non-uniformity? Consider whether to apply correction factors to normalize fluorescence capture or simply mask out regions with inadequate uniformity for quantitative purposes.
• How frequently will system validation be performed? Consider establishing a regular schedule for RUD target assessment to detect any changes in optical performance over time.

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 uniformity.

The Reference Uniformity and Distortion (RUD) target provides crucial insights into fluorescence imaging fidelity, which directly impacts clinical interpretation. 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!