Highly flexible and compact volumetric endoscope by integrating multiple micro-imaging devices.

A light-field endoscope can simultaneously capture the three-dimensional information of in situ lesions and enables single-shot quantitative depth perception with minimal invasion for improving surgical and diagnostic accuracy. However, due to oversized rigid probes, clinical applications of current techniques are limited by their cumbersome devices. To minimize the size and enhance the flexibility, here we report a highly flexible and compact volumetric endoscope by employing precision-machined multiple micro-imaging devices (MIRDs). To further protect the flexibility, the designed MIRD with a diameter and height of 5 mm is packaged in pliable polyamide, using soft data cables for data transmission. It achieves the optimal lateral resolvability of 31 µm and axial resolvability of 255 µm, with an imaging volume over 2.3 × 2.3 × 10 mm3. Our technique allows easy access to the organism interior through the natural entrance, which has been verified through observational experiments of the stomach and rectum of a rabbit. Together, we expect this device can assist in the removal of tumors and polyps as well as the identification of certain early cancers of the digestive tract.

Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation.

In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). However, the local quality of the images may vary on multiple scales, and these differences can lead to misconceptions. Current mapping methods fail to finely estimate the local quality, challenging to associate the SR scale content. Here, we develop a rolling Fourier ring correlation (rFRC) method to evaluate the reconstruction uncertainties down to SR scale. To visually pinpoint regions with low reliability, a filtered rFRC is combined with a modified resolution-scaled error map (RSM), offering a comprehensive and concise map for further examination. We demonstrate their performances on various SR imaging modalities, and the resulting quantitative maps enable better SR images integrated from different reconstructions. Overall, we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.