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

Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis

Recent advances in label-free histology promise a new era for real-time diagnosis in neurosurgery. Deep learning using autofluorescence is promising for tumor classification without histochemical staining process. The high image resolution and minimally invasive diagnostics with negligible tissue damage is of great importance. The state of the art is raster scanning endoscopes, but the distal lens optics limits the size. Lensless fiber bundle endoscopy offers both small diameters of a few 100 microns and the suitability as single-use probes, which is beneficial in sterilization. The problem is the inherent honeycomb artifacts of coherent fiber bundles (CFB). For the first time, we demonstrate an end-to-end lensless fiber imaging with exploiting the near-field. The framework includes resolution enhancement and classification networks that use single-shot CFB images to provide both high-resolution imaging and tumor diagnosis. The well-trained resolution enhancement network not only recovers high-resolution features beyond the physical limitations of CFB, but also helps improving tumor recognition rate. Especially for glioblastoma, the resolution enhancement network helps increasing the classification accuracy from 90.8 to 95.6%. The novel technique enables histological real-time imaging with lensless fiber endoscopy and is promising for a quick and minimally invasive intraoperative treatment and cancer diagnosis in neurosurgery.

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.

A 25 micron-thin microscope for imaging upconverting nanoparticles with NIR-I and NIR-II illumination

Rationale: Intraoperative visualization in small surgical cavities and hard-to-access areas are essential requirements for modern, minimally invasive surgeries and demand significant miniaturization. However, current optical imagers require multiple hard-to-miniaturize components including lenses, filters and optical fibers. These components restrict both the form-factor and maneuverability of these imagers, and imagers largely remain stand-alone devices with centimeter-scale dimensions. Methods: We have engineered INSITE (Immunotargeted Nanoparticle Single-Chip Imaging Technology), which integrates the unique optical properties of lanthanide-based alloyed upconverting nanoparticles (aUCNPs) with the time-resolved imaging of a 25-micron thin CMOS-based (complementary metal oxide semiconductor) imager. We have synthesized core/shell aUCNPs of different compositions and imaged their visible emission with INSITE under either NIR-I and NIR-II photoexcitation. We characterized aUCNP imaging with INSITE across both varying aUCNP composition and 980 nm and 1550 nm excitation wavelengths. To demonstrate clinical experimental validity, we also conducted an intratumoral injection into LNCaP prostate tumors in a male nude mouse that was subsequently excised and imaged with INSITE. Results: Under the low illumination fluences compatible with live animal imaging, we measure aUCNP radiative lifetimes of 600 μs - 1.3 ms, which provides strong signal for time-resolved INSITE imaging. Core/shell NaEr0.6Yb0.4F4 aUCNPs show the highest INSITE signal when illuminated at either 980 nm or 1550 nm, with signal from NIR-I excitation about an order of magnitude brighter than from NIR-II excitation. The 55 μm spatial resolution achievable with this approach is demonstrated through imaging of aUCNPs in PDMS (polydimethylsiloxane) micro-wells, showing resolution of micrometer-scale targets with single-pixel precision. INSITE imaging of intratumoral NaEr0.8Yb0.2F4 aUCNPs shows a signal-to-background ratio of 9, limited only by photodiode dark current and electronic noise. Conclusion: This work demonstrates INSITE imaging of aUCNPs in tumors, achieving an imaging platform that is thinned to just a 25 μm-thin, planar form-factor, with both NIR-I and NIR-II excitation. Based on a highly paralleled array structure INSITE is scalable, enabling direct coupling with a wide array of surgical and robotic tools for seamless integration with tissue actuation, resection or ablation.