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Signal to Noise Ratio as a Cross-Platform Metric for Intraoperative Fluorescence Imaging
Asmaysinh Gharia1,2, Efthymios P Papageorgiou1, Simeon Giverts2
1Department of Electrical Engineering and Computer Sciences, University of California Berkeley, CA, USA.
Abstract:
Real-time molecular imaging to guide curative cancer surgeries is critical to ensure removal of all tumor cells; however, visualization of microscopic tumor foci remains challenging. Wide variation in both imager instrumentation and molecular labeling agents demands a common metric conveying the ability of a system to identify tumor cells. Microscopic disease, comprised of a small number of tumor cells, has a signal on par with the background, making the use of signal (or tumor) to background ratio inapplicable in this critical regime. Therefore, a metric that incorporates the ability to subtract out background, evaluating the signal itself relative to the sources of uncertainty, or noise is required. Here we introduce the signal to noise ratio (SNR) to characterize the ultimate sensitivity of an imaging system and optimize factors such as pixel size. Variation in the background (noise) is due to electronic sources, optical sources, and spatial sources (heterogeneity in tumor marker expression, fluorophore binding, and diffusion). Here, we investigate the impact of these noise sources and ways to limit its effect on SNR. We use empirical tumor and noise measurements to procedurally generate tumor images and run a Monte Carlo simulation of microscopic disease imaging to optimize parameters such as pixel size.
Insights
A new signal-to-noise ratio (SNR) metric accurately measures microscopic cancer detection in real-time molecular imaging. This approach optimizes imaging systems to improve tumor visualization and surgical guidance for better cancer treatment outcomes.
Area of Science:
- Medical Imaging
- Oncology
- Biomedical Engineering
Background:
- Real-time molecular imaging is crucial for guiding curative cancer surgeries to ensure complete tumor removal.
- Visualizing microscopic tumor foci remains a significant challenge in current imaging technologies.
- Existing metrics like signal-to-background ratio are inadequate for detecting small tumor cell clusters with low signal.
Purpose of the Study:
- To introduce a novel metric, signal-to-noise ratio (SNR), for characterizing the ultimate sensitivity of molecular imaging systems.
- To address the limitations of current metrics in visualizing microscopic disease.
- To optimize imaging system parameters, such as pixel size, for improved detection of minimal residual disease.
Main Methods:
- Investigated various sources of noise (electronic, optical, spatial) impacting imaging sensitivity.
- Developed a method to procedurally generate tumor images using empirical measurements of tumor and noise.
- Employed Monte Carlo simulations of microscopic disease imaging to optimize imaging parameters.
Main Results:
- Introduced signal-to-noise ratio (SNR) as a robust metric for evaluating molecular imaging system sensitivity.
- Demonstrated that SNR is applicable in the critical regime of microscopic disease detection where signal-to-background ratio fails.
- Identified key noise sources and strategies to mitigate their impact on SNR.
Conclusions:
- The signal-to-noise ratio (SNR) provides a superior metric for assessing molecular imaging system performance in detecting microscopic cancer.
- Optimizing SNR can lead to enhanced visualization of residual tumor cells, improving surgical guidance and patient outcomes.
- This work lays the foundation for developing more sensitive and reliable molecular imaging tools for cancer surgery.
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