Improving flat fluorescence microscopy in scattering tissue through deep learning strategies.

Intravital microscopy in small animals growingly contributes to the visualization of short- and long-term mammalian biological processes. Miniaturized fluorescence microscopy has revolutionized the observation of live animals' neural circuits. The technology's ability to further miniaturize to improve freely moving experimental settings is limited by its standard lens-based layout. Typical miniature microscope designs contain a stack of heavy and bulky optical components adjusted at relatively long distances. Computational lensless microscopy can overcome this limitation by replacing the lenses with a simple thin mask. Among other critical applications, Flat Fluorescence Microscope (FFM) holds promise to allow for real-time brain circuits imaging in freely moving animals, but recent research reports show that the quality needs to be improved, compared with imaging in clear tissue, for instance. Although promising results were reported with mask-based fluorescence microscopes in clear tissues, the impact of light scattering in biological tissue remains a major challenge. The outstanding performance of deep learning (DL) networks in computational flat cameras and imaging through scattering media studies motivates the development of deep learning models for FFMs. Our holistic ray-tracing and Monte Carlo FFM computational model assisted us in evaluating deep scattering medium imaging with DL techniques. We demonstrate that physics-based DL models combined with the classical reconstruction technique of the alternating direction method of multipliers (ADMM) perform a fast and robust image reconstruction, particularly in the scattering medium. The structural similarity indexes of the reconstructed images in scattering media recordings were increased by up to 20% compared with the prevalent iterative models. We also introduce and discuss the challenges of DL approaches for FFMs under physics-informed supervised and unsupervised learning.

Deep Learning Empowered Fresnel-based Lensless Fluorescence Microscopy.

Miniaturized fluorescence microscopy has revolutionized the way neuroscientists study the brain in-vivo. Recent developments in computational lensless imaging promise a next generation of miniaturized microscopes in lensless fluorescence microscopy. We developed a microscope prototype using an optimized Fresnel amplitude mask. While many lensless imaging modalities have reported excellent performance using Deep Learning (DL) approaches, DL application in fluorescence imaging has been left untouched. We generated a computational dataset based on experimental system calibration to evaluate DL capabilities on biological cell morphologies. We show that our DL-assisted microscope can provide high-quality imaging with a structural similarity index of 89%. The least absolute error was decreased by 63% using the DL-assisted method compared with the classical models. The state-of-the-art performance of this prototype enhances the expected potential of amplitude masks in lensless microscopy applications, which are critical for robust in-vivo flat microscopy with engineered image sensors.Clinical Relevance- This study aids in advancing miniaturized fluorescence microscopy, which greatly impacts long-term brain circuit and disease studies in freely moving animal models.

Development of a Dual-Wavelength Isosbestic Wireless Fiber Photometry Platform for Live Animals Studies.

Measuring neural activity from well-defined neural populations deep inside the brain has an important value in neuroscience. Fiber photometry is an important technique for evaluating neuron activity inside the brain. Besides, miniature wireless systems to record neuronal activity in a fully untethered experimental setting have recently become extremely interesting for experimenters. Still, a noise-robust wireless fiber photometry system for this purpose does not exist. Using an isosbestic excitation wavelength for recording with GCaMP6 has recently been suggested to reduce the different types of noises. We present the design of a wireless fiber photometry system at 470 nm for calcium-dependent fluorescence emission of GCaMP6 using a calcium-independent isosbestic excitation wavelength of 410 nm. Synthetic emission fluorescence light was played from a function generator to drive an LED at 530 nm at test the photometry platform. The setup has been fixed at 4.18 mW light power after linearity assessment while the analog circuit has THD of 0.35%. Then, the recorded synthetic neuronal activity was transmitted wirelessly to the base station. Finally, the isosbestic response has been aligned and removed from the calcium-dependent fluorescence signal to have a noiseless neuronal activity.