The overarching goal of HÈRMES ( High-speed timE Resolved fluorescence iMaging with no pilE-up diStortion) is to establish a new methodology for time-resolved imaging by means of Time-Correlated Single Photon Counting (TCSPC).
Since its debut in the literature, the potential of TCSPC as non-invasive, ultra-sensitive and extremely precise imaging tool was manifest. Numerous applications have benefited from it so far, but a key limitation still prevents its wide use in many other crucial applications: the speed of a TCSPC acquisition chain must be kept low to avoid distortion due to pile-up of events. Up to now researchers in this field have tried to work around this limit mainly by posing multiple channels in parallel, but still facing severe limitations mainly due to efficiency, fill factor, precision, linearity and readout complexity.
The HÈRMES project aims at changing the paradigm of how TCSPC systems are conceived and how time-resolved measurements are carried out. The root of the project relies on the development of a comprehensive mathematical model showing that pile-up distortion can be avoided with any combination of single photon detector and laser excitation power provided that additional picosecond-precision information on the status of system in each time bin is acquired at run-time. Pushing TCSPC speed well beyond current limits open the way to the development of ultrafast electronics for single photon detectors to move from theory to the real experimental world.
The full potential of ultrafast TCSPC systems will be finally unlocked by an innovative computational imaging framework, opening the way to the real-time acquisition of 4D images. Next-generation TCSPC systems based on the HÈRMES methodology will allow the exploitation of this powerful tool in crucial applications such as, for example, intraoperative and neuron imaging, where an ultrafast but still linear acquisition is necessary to enable complex operations like image-assisted brain surgery and spike analysis of neurons.
The project is funded by the European Research Council, ERC-STG 2023.
