The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working on this field, that has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead.

Measurements of three-photon action cross-sections for fluorescein (dissolved in water, pH ∼11.5) are presented in the excitation wavelength range from 1154 to 1500 nm in ∼50 nm steps. The excitation source is a femtosecond wavelength tunable non-collinear optical parametric amplifier, which has been spectrally filtered with 50 nm full width at half maximum band pass filters. Cube-law power dependance is confirmed at the measurement wavelengths. The three-photon excitation spectrum is found to differ from both the one- and two-photon excitation spectra. The three-photon action cross-section at 1154 nm is more than an order of magnitude larger than those at 1450 and 1500 nm (approximately three times the wavelength of the one-photon excitation peak), which possibly indicates the presence of resonance enhancement.

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Much of fluorescence-based microscopy involves detection of if an object is present or absent (i.e., binary detection). The imaging depth of three-dimensionally resolved imaging, such as multiphoton imaging, is fundamentally limited by out-of-focus background fluorescence, which when compared to the in-focus fluorescence makes detecting objects in the presence of noise difficult. Here, we use detection theory to present a statistical framework and metric to quantify the quality of an image when binary detection is of interest. Our treatment does not require acquired or reference images, and thus allows for a theoretical comparison of different imaging modalities and systems.

Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whereby less measurements are collected during sensing and reconstruction is performed to recover the image. Much work has gone into optimizing the sensing and reconstruction portions separately. We propose a method of jointly optimizing both sensing and reconstruction end-to-end under a total measurement constraint, enabling learning of the optimal sensing scheme concurrently with the parameters of a neural network-based reconstruction network. We train our model on a rich dataset of confocal, two-photon, and wide-field microscopy images comprising of a variety of biological samples. We show that our method outperforms several baseline sensing schemes and a regularized regression reconstruction algorithm.