Deep-learning-augmented Computational Miniature Mesoscope

Fluorescence microscopy is essential to study biological structures and dynamics. However, existing systems suffer from a tradeoff between field-of-view (FOV), resolution, and complexity, and thus cannot fulfill the emerging need of miniaturized platforms providing micron-scale resolution across centimeter-scale FOVs. To overcome this challenge, we developed Computational Miniature Mesoscope (CM\(^2\)) that exploits a computational imaging strategy to enable single-shot 3D high-resolution imaging across a wide FOV in a miniaturized platform. Here, we present CM\(^2\) V2 that significantly advances both the hardware and computation. We complement the 3\(\times\)3 microlens array with a new hybrid emission filter that improves the imaging contrast by 5\(\times\), and design a 3D-printed freeform collimator for the LED illuminator that improves the excitation efficiency by 3\(\times\). To enable high-resolution reconstruction across the large imaging volume, we develop an accurate and efficient 3D linear shift-variant (LSV) model that characterizes the spatially varying aberrations. We then train a multi-module deep learning model, CM\(^2\)Net, using only the 3D-LSV simulator. We show that CM\(^2\)Net generalizes well to experiments and achieves accurate 3D reconstruction across a \(\sim\)7-mm FOV and 800-\(\mu\)m depth, and provides \(\sim\)6-\(\mu\)m lateral and \(\sim\)25-\(\mu\)m axial resolution. This provides \(\sim\)8\(\times\) better axial localization and \(\sim\)1400\(\times\) faster speed as compared to the previous model-based algorithm. We anticipate this simple and low-cost computational miniature imaging system will be impactful to many large-scale 3D fluorescence imaging applications.