We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM), using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared these algorithms using synthetic datasets. The results indicate that ELM can obtain higher fidelity, even in low-photon conditions. Afterwards, we used ELM to retrieve lifetime components from human prostate cancer cells loaded with gold nanosensors, showing that ELM also outperforms the iterative fitting and non-fitting algorithms. By comparing ELM with a computational efficient neural network, ELM achieves comparable accuracy with less training and inference time. As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches. The proposed strategy is promising for edge computing with online training.

Fluorescence lifetime imaging (FLIM) is a powerful tool that provides unique quantitative information for biomedical research. In this study, we propose a multi-layer-perceptron-based mixer (MLP-Mixer) deep learning (DL) algorithm named FLIM-MLP-Mixer for fast and robust FLIM analysis. The FLIM-MLP-Mixer has a simple network architecture yet a powerful learning ability from data. Compared with the traditional fitting and previously reported DL methods, the FLIM-MLP-Mixer shows superior performance in terms of accuracy and calculation speed, which has been validated using both synthetic and experimental data. All results indicate that our proposed method is well suited for accurately estimating lifetime parameters from measured fluorescence histograms, and it has great potential in various real-time FLIM applications.

The polarization multiplexing technique is a well-established method that improves the communication capacity of an optical system. In this paper, we designed orthogonal linear and circular polarization multiplexing metalens using a library of rectangle TiO₂ nanostructures. The former can independently focus x- and y-linearly polarized incident lights to designed positions with a focusing efficiency of 53.81% and 51.56%, respectively, whereas the latter with two preset focal points can independently control left and right circularly polarized incident lights with a focusing efficiency of 42.45% and 42.46%, respectively. We also show that both metalenses can produce diffraction-limited focal spots for four polarization states with no obvious distortion, which opens up new applications in polarization imaging and polarization detection.

SignificanceDiffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient μa and reduced scattering coefficient μs′) and scalp and skull thicknesses.AimWe aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light’s temporal ACFs. Moreover, the proposed method can be applied to a range of source–detector distances instead of being limited to a specific source–detector distance.ApproachWe present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural network (DCS-NET), for BFi and coherent factor (β) estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source–detector distances (5, 10, 15, 20, 25, and 30 mm) on BFi and β estimations among DCS-NET, semi-infinite, and three-layer fitting models.ResultsDCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of μa and μs′ at a source–detector separation of 30 mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively.ConclusionsDCS-NET can robustly quantify blood flow measurements at considerable source–detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements.

Cerebral blood flow (CBF) is a critical biomarker for brain health and function, supporting cognitive and neurological processes.¹ Existing technique, such as transcranial Doppler ultrasound, is noninvasive but relies on experienced operators and is limited to larger arteries.² Functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), on the other hand, are bulky and provide only “snapshot” observations; they are not suitable for bedside applications.^3,4 By contrast, diffuse correlation spectroscopy (DCS) offers noninvasive, continuous, high temporal resolution CBF index (CBFi) measurements at the bedside.⁵ DCS analyzes light intensity fluctuations caused by red blood cell movement using the autocorrelation function (ACF), given by $g_2=⟨I(t)I(t+\tau )⟩/{⟨I(t)⟩}^2$, to assess blood flow dynamics.^6,7