Measuring fluorescence lifetimes of fast-moving cells or particles have broad applications in biomedical sciences. This paper presents a dynamic fluorescence lifetime sensing (DFLS) system based on the time-correlated single-photon counting (TCSPC) principle. It integrates a CMOS 192 × 128 single-photon avalanche diode (SPAD) array, offering an enormous photon-counting throughput without pile-up effects. We also proposed a quantized convolutional neural network (QCNN) algorithm and designed a field-programmable gate array embedded processor for fluorescence lifetime determinations. The processor uses a simple architecture, showing unparallel advantages in accuracy, analysis speed, and power consumption. It can resolve fluorescence lifetimes against disturbing noise. We evaluated the DFLS system using fluorescence dyes and fluorophore-tagged microspheres. The system can effectively measure fluorescence lifetimes within a single exposure period of the SPAD sensor, paving the way for portable time-resolved devices and shows potential in various applications.
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.
We present a deep learning approach to obtain high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images acquired from fluorescence lifetime imaging (FLIM) systems. We first proposed a theoretical method for training neural networks to generate massive semi-synthetic FLIM data with various cellular morphologies, a sizeable dynamic lifetime range, and complex decay components. We then developed a degrading model to obtain LR-HR pairs and created a hybrid neural network, the spatial resolution improved FLIM net (SRI-FLIMnet) to simultaneously estimate fluorescence lifetimes and realize the nonlinear transformation from LR to HR images. The evaluative results demonstrate SRI-FLIMnet’s superior performance in reconstructing spatial information from limited pixel resolution. We also verified SRI-FLIMnet using experimental images of bacterial infected mouse raw macrophage cells. Results show that the proposed data generation method and SRI-FLIMnet efficiently achieve superior spatial resolution for FLIM applications. Our study provides a solution for fast obtaining HR FLIM images.
This paper proposes a new calibration method, the mixed-binning method, to pursue a TDC with high linearity in field-programmable gate arrays (FPGAs). This method can reduce the nonlinearity caused by large clock skews in FPGAs efficiently. Therefore, a wide dynamic range tapped delay line (TDL) TDC has been developed with maintained linearity. We evaluated this method in Xilinx 20nm UltraScale FPGAs and Xilinx 28nm Virtex-7 FPGAs. Results conduct that this method is perfectly suitable for driverless vehicle applications which require high linearity with an acceptable resolution. The proposed method also has great potentials for multi-channel applications, due to the low logic resource consumption. For a quick proof-of-concept demonstration, an 8-channel solution has also been implemented. It can be further extended to a 64-channel version soon.Index Terms-Carry chains, field-programmable gate array (FPGA), time-of-flight, time-to-digital converter (TDC), Automatic Vehicle.
Single-photon avalanche diodes (SPAD) are powerful sensors for 3D light detection and ranging (LiDAR) in low light scenarios due to their single-photon sensitivity. However, accurately retrieving ranging information from noisy time-of-arrival (ToA) point clouds remains a challenge. This paper proposes a photon-efficient, non-fusion neural network architecture that can directly reconstruct high-fidelity depth images from ToA data without relying on other guiding images. Besides, the neural network architecture was compressed via a low-bit quantization scheme so that it is suitable to be implemented on embedded hardware platforms. The proposed quantized neural network architecture achieves superior reconstruction accuracy and fewer parameters than previously reported networks.
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