A computational imaging approach for resolution enhancement in fiber bundle endomicroscopy

Confocal, multi-photon, and wide-field endomicroscopy often use coherent fiber-optic bundles to facilitate in vivo imaging. The narrow diameter and flexibility of these bundles allow excellent tissue access, but fabrication processes place a practical limit on fiber packing density, restricting the number of resolvable points in an image. Furthermore, the hexagonal packing of discrete fibers creates inter-fiber gaps that prevent some regions of the object from being imaged. We have combined compressed sensing (CS) principles with dispersive optics to simultaneously address these two fundamental limitations of the fiber bundle architecture. We previously reported a CS approach to improve the spatial resolution of bundle based imaging systems by recovering multiple resolvable points within each fiber (Dumas et al., Proc. SPIE 2018). This manuscript will discuss and integrate approaches for recovering object details that lie behind inter-fiber gaps with our CS-based method for resolving intra-fiber detail. First, we show that modifying our CS model to consider the whole field of view rather than a discrete point for each fiber can partially recover inter-fiber detail. Next, we outline how a dispersive component at the distal end of the bundle can be used to spectrally shift object detail such that information from all locations on the sample are transmitted through the bundle. We then implement image compounding techniques with our CS approach to produce a more continuous image. We demonstrate that our platform can produce images of biological samples with 65,536 resolved pixels using a fiber bundle with only 3,700 fiber cores.