De-scattering with Excitation Patterning enables rapid wide-field imaging through scattering media

Abstract

Nonlinear optical microscopy has enabled in vivo deep tissue imaging on the millimeter scale. A key unmet challenge is its limited throughput especially compared to rapid wide-field modalities that are used ubiquitously in thin specimens. Wide-field imaging methods in tissue specimens have found successes in optically cleared tissues and at shallower depths, but the scattering of emission photons in thick turbid samples severely degrades image quality at the camera. To address this challenge, we introduce a novel technique called De-scattering with Excitation Patterning or “DEEP,” which uses patterned nonlinear excitation followed by computational imaging–assisted wide-field detection. Multiphoton temporal focusing allows high-resolution excitation patterns to be projected deep inside specimen at multiple scattering lengths due to the use of long wavelength light. Computational reconstruction allows high-resolution structural features to be reconstructed from tens to hundreds of DEEP images instead of millions of point-scanning measurements.

DEEP2: Deep Learning Powered De-scattering with Excitation Patterning

“…In this section we present numerical results on synthetic test datasets to validate our trained inverse model. All experiments used only 32 patterned illuminations which is nearly an order of magnitude less than the current state of the art 8 .…”

“…They used coded excitations and single-pixel detection. We proposed 'De-scattering by Excitation Patterning (DEEP)' 8 . Instead of single-pixel detection, we used wide-field detection.…”

“…An inverse model should be constructed to map the observed (y) to the ideal expected image (x). In our original work 8 , we used an analytical inverse model that utilized wavelet sparsity priors. However, recently, for such inverse problems, deep learning has proven impressively capable.…”

Computational optics for high-throughput imaging of neural activity

“…Selective illumination can be implemented with either one-photon microscopy 4 – 8 or multiphoton microscopy. 9 – 17 Multiphoton microscopy is more robust to tissue scattering than one-photon microscopy because the excitation light of multiphoton microscopy has a longer wavelength. Therefore, selective illumination generated by multiphoton microscopy has higher precision and less cross-talk when imaging at a few hundred microns deep in the mouse brain.…”

HiLo Based Line Scanning Temporal Focusing Microscopy for High-Speed, Deep Tissue Imaging

“…For the latter, we proposed the techniques of focal modulation [ 22 ] and extended detection followed by computation reconstructions [ 18 ] for LSTFM. Zheng et al also demonstrated a de-scattering method by taking advantage of patterned nonlinear excitation and computational-assisted wide-field detection [ 23 ]. All these methods are effective in enhancing axial resolution and background rejection.…”