Decoupling channel count from field of view and spatial resolution in single-sensor imaging systems for fluorescence image-guided surgery.

Near-infrared fluorescence image-guided surgery is often thought of as a spectral imaging problem where the channel count is the critical parameter, but it should also be thought of as a multiscale imaging problem where the field of view and spatial resolution are similarly important. Conventional imaging systems based on division-of-focal-plane architectures suffer from a strict relationship between the channel count on one hand and the field of view and spatial resolution on the other, but bioinspired imaging systems that combine stacked photodiode image sensors and long-pass/short-pass filter arrays offer a weaker tradeoff. In this paper, we explore how the relevant changes to the image sensor and associated image processing routines affect image fidelity during image-guided surgeries for tumor removal in an animal model of breast cancer and nodal mapping in women with breast cancer. We demonstrate that a transition from a conventional imaging system to a bioinspired one, along with optimization of the image processing routines, yields improvements in multiple measures of spectral and textural rendition relevant to surgical decision-making. These results call for a critical examination of the devices and algorithms that underpin image-guided surgery to ensure that surgeons receive high-quality guidance and patients receive high-quality outcomes as these technologies enter clinical practice.