Versatile software and hardware combo enabling photon counting acquisition and real-time display for multiplexing, 2D and continuous 3D two-photon imaging applications.

Significance: rPySight brings a flexible and highly customizable open-software platform built around a powerful multichannel digitizer; combined, it enables performing complex photon counting-based experiments. We exploited advanced programming technology to share the photon counting stream with the graphical processing unit (GPU), making possible real-time display of two-dimensional (2D) and three-dimensional (3D) experiments and paving the road for other real-time applications. Aim: Photon counting improves multiphoton imaging by providing better signal-to-noise ratio in photon-deprived applications and is becoming more widely implemented, as indicated by its increasing presence in many microscopy vendor portfolios. Despite the relatively easy access to this technology offered in commercial systems, these remain limited to one or two channels of data and might not enable highly tailored experiments, forcing most researchers to develop their own electronics and code. We set to develop a flexible and open-source interface to a cutting-edge multichannel fast digitizer that can be easily integrated into existing imaging systems. Approach: We selected an advanced multichannel digitizer capable of generating 70M tags/s and wrote an open software application, based on Rust and Python languages, to share the stream of detected events with the GPU, enabling real-time data processing. Results: rPySight functionality was showcased in real-time monitoring of 2D imaging, improved calcium imaging, multiplexing, and 3D imaging through a varifocal lens. We provide a detailed protocol for implementing out-of-the-box rPySight and its related hardware. Conclusions: Applying photon-counting approaches is becoming a fundamental component in recent technical developments that push well beyond existing acquisition speed limitations of classical multiphoton approaches. Given the performance of rPySight, we foresee its use to capture, among others, the joint dynamics of hundreds (if not thousands) of neuronal and vascular elements across volumes, as is likely required to uncover in a much broader sense the hemodynamic transform function.

Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation.

Recently developed methods for rapid continuous volumetric two-photon microscopy facilitate the observation of neuronal activity in hundreds of individual neurons and changes in blood flow in adjacent blood vessels across a large volume of living brain at unprecedented spatio-temporal resolution. However, the high imaging rate necessitates fully automated image analysis, whereas tissue turbidity and photo-toxicity limitations lead to extremely sparse and noisy imagery. In this work, we extend a recently proposed deep learning volumetric blood vessel segmentation network, such that it supports temporal analysis. With this technology, we are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface. This new capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.

Understanding the neurovascular unit at multiple scales: Advantages and limitations of multi-photon and functional ultrasound imaging.

Developing efficient brain imaging technologies by combining a high spatiotemporal resolution and a large penetration depth is a key step for better understanding the neurovascular interface that emerges as a main pathway to neurodegeneration in many pathologies such as dementia. This review focuses on the advances in two complementary techniques: multi-photon laser scanning microscopy (MPLSM) and functional ultrasound imaging (fUSi). MPLSM has become the gold standard for in vivo imaging of cellular dynamics and morphology, together with cerebral blood flow. fUSi is an innovative imaging modality based on Doppler ultrasound, capable of recording vascular brain activity over large scales (i.e., tens of cubic millimeters) at unprecedented spatial and temporal resolution for such volumes (up to 10µm pixel size at 10kHz). By merging these two technologies, researchers may have access to a more detailed view of the various processes taking place at the neurovascular interface. MPLSM and fUSi are also good candidates for addressing the major challenge of real-time delivery, monitoring, and in vivo evaluation of drugs in neuronal tissue.