Recently, the international high-impact optics journal "Advanced Imaging" published a research paper titled "Photonic Timestamped Confocal Microscopy (PT-Confocal)" by Professor Xianmin Jin's team from Shanghai Jiao Tong University. The study reports the experimental achievement of confocal microscopy imaging using the first ten photons. They combined maximum likelihood estimation, discrete wavelet transform, and deep learning optimization to achieve high-quality reconstruction. Compared to traditional methods, the exposure intensity for samples was reduced by two orders of magnitude. The paper also demonstrates the application capabilities of PT-Confocal in multi-channel and three-dimensional imaging.

I. Background

Confocal microscopy, an advanced imaging technology used to improve optical resolution and contrast, can be applied to various scenarios ranging from biomedical imaging to industrial inspection. However, sufficiently high illumination intensity is required to form an energy concentration pattern in the focal area, which can lead to biological sample damage and nonlinear effects of fluorescent groups. Additionally, intense light exposure may cause phototoxicity and photobleaching, resulting in irreversible damage to biological samples. Achieving high-precision confocal imaging under low photon flux conditions has become a major challenge for researchers.

II. Work Introduction

Professor Xian-Min Jin's team proposed the Photonic Timestamped Confocal Microscopy (PT-Confocal) scheme. By treating light as a discrete photon stream, they utilized a time-correlated single-photon counting module to record the arrival time distribution information of photons, statistically estimated the average photon number intensity, and then applied maximum likelihood estimation, discrete wavelet transform, and deep learning algorithms to denoise and enhance the image. In experiments, PT-Confocal could restore fluorescent images containing information from ten photons, while traditional cumulative imaging required a hundredfold more photons to achieve the same reconstruction effect. This demonstrates its enormous application potential under ultra-low photon budgets, significantly advancing the application of confocal imaging technology in low-light imaging fields.

Figure 1: Experimental Scheme of Low-Exposure Confocal Microscopy Based on Photonic Timestamps

III. Evaluation

Team member Si-Yuan Yin commented: "We independently built a confocal microscopic imaging system with nanosecond-level single-photon time resolution capabilities. By exploiting the temporal information freedom of photons, we achieved confocal fluorescence imaging with the first ten photons. Compared to traditional cumulative imaging, the photon budget was reduced by two orders of magnitude. Our technology can be applied to various sparse and weak signal imaging scenarios, such as metabolite imaging and neuronal imaging, as well as imaging of materials and cells with low reflectivity. The current system can be upgraded through hardware and algorithm enhancements to improve imaging speed, catering to the needs of real-time live-cell imaging."

IV. Future Work Outlook

Currently, the imaging speed of PT-Confocal is limited by the mechanical frequency of the scanning galvanometer, making it insufficient for live-cell imaging. In the future, we will enhance imaging speed by incorporating multi-pinhole or spinning disk pinhole arrays combined with single-photon detector arrays. Additionally, we will select stable and fast scanning galvanometers to shorten the time required for mechanical laser scanning. These improvements are crucial for detecting samples where radiation damage or sample characteristics prevent the collection of sufficient photons.

The research team expresses gratitude for the significant support from the Major Projects of the Shanghai Science and Technology Committee, the National Natural Science Foundation of China, the National Key Research and Development Program, and the Shanghai Municipal Education Commission. Si-Yuan Yin, (master's student) and co-first authors Shi-Bao Wu, Zhan-Ming Li (PhD candidates, Grade 2019), and Hao-Ran Lu (master's student), at the Center for Integrated Quantum Information Technology Research, School of Physics and Astronomy, Shanghai Jiao Tong University, contributed to the paper, with Professor Xian-Min Jin serving as the corresponding author.