Signal to Noise Ratio as a Cross-Platform Metric for Intraoperative Fluorescence Imaging.

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.

Real-time cancer detection with an integrated lensless fluorescence contact imager.

Microscopic tumor cell foci left in a patient after surgery significantly increase the chance of cancer recurrence. However, fluorescence microscopes used for intraoperative navigation lack the necessary sensitivity for imaging microscopic disease and are too bulky to maneuver within the resection cavity. We have developed a scalable chip-scale fluorescence contact imager for detecting microscopic cancer in vivo and in real-time. The imager has been characterized under simulated in vivo conditions using ex vivo samples, providing strong evidence that our device can be used in vivo. Angle-selective gratings enhance the resolution of the imager without impacting its physical size. We demonstrate detection of cancer cell clusters containing as few as 25 HCC1569 breast cancer cells and 400 LNCaP prostate cancer cells with integration times of only 50 ms and 70 ms, respectively. A cell cluster recognition algorithm is used to achieve both a sensitivity and specificity of 92 % for HCC1569 cell samples, indicating the reliability of the imager. The signal-to-noise ratio (SNR) degradation with increased separation is only 1.5 dB at 250 µm. Blood scattering and absorption reduce the SNR by less than 2 dB for typical concentrations. Moreover, HER2+ breast cancer tissue taken from a patient is distinguished from normal breast tissue with an integration time of only 75 ms.

Imaging of IR700DX Labeled Mouse Breast Tumor Using a Custom Angle-Selective Fluorescence Contact Imaging System.

Cancer treatment faces the challenge of identifying small clusters of residual tumor cells in the resection cavity after the gross section of tumor is surgically removed. Despite the introduction of targeted fluorescent probes to guide cancer surgeries, large, bulky, optical components restrict the ability of fluorescence imaging devices to detect small clusters of tumor cells in the complex surgical cavity. We have developed a small size-scale contact fluorescence image sensor that incorporates angle-selective gratings and a thin 15 m amorphous silicon optical wavelength filter for detecting residual cancer tissue in vivo. Using a custom fluorescent probe combining a fluorescent dye, IR700DX, with a targeted antibody, Trastuzumab, we label and visualize breast tissue in in vivo mouse models of breast cancer. When imaging tumorbearing mice injected with the probe, HER2+ breast cancer tissue intensity is 3.80.8 times brighter than other tissue. Excised cancer tumors and residual cancer attached to healthy tissue are imaged using the custom image sensor. Residual cancer tissue can be detected in real-time and is imaged with a high SNR of 45 dB using an integration time of only 40 ms.