A Single-Shot Autofocus Approach for Surface Plasmon Resonance Microscopy

Design of image intelligent focusing system based on improved SMD function and RBF algorithm

The utilization of digital statistical processes in images and videos can effectively tackle numerous challenges encountered in optical sensors. This research endeavors to overcome the limitations inherent in traditional focus models, particularly their inadequate accuracy. It aims to bolster the precision of real-time perception and dynamic control by employing enhanced data fusion methodologies. The ultimate objective is to facilitate information services that enable seamless interaction and profound integration between computational and physical processes within an open environment. To achieve this, an enhanced sum-modulus difference (SMD) evaluation function has been proposed. This innovation is founded on the concept of threshold value evaluation, aimed at rectifying the accuracy shortcomings of traditional focusing models. Through the computation of each gray value after threshold segmentation, the method identifies the most suitable threshold for image segmentation. This identified threshold is then applied to the focus search strategy employing the radial basis function (RBF) algorithm. Furthermore, an intelligent focusing system has been developed on the Zynq development platform, encompassing both hardware design and software program development. The test results affirm that the focusing model based on the improved SMD evaluation function rapidly identifies the peak point of the gray variance curve, ascertains the optimal focal plane position, and notably enhances the sensitivity of the focusing model.

Surface Plasmon Resonance Biosensors: A Review of Molecular Imaging with High Spatial Resolution

Surface plasmon resonance (SPR) is a powerful tool for determining molecular interactions quantitatively. SPR imaging (SPRi) further improves the throughput of SPR technology and provides the spatially resolved capability for observing the molecular interaction dynamics in detail. SPRi is becoming more and more popular in biological and chemical sensing and imaging. However, SPRi suffers from low spatial resolution due to the imperfect optical components and delocalized features of propagating surface plasmonic waves along the surface. Diverse kinds of approaches have been developed to improve the spatial resolution of SPRi, which have enormously impelled the development of the methodology and further extended its possible applications. In this minireview, we introduce the mechanisms for building a high-spatial-resolution SPRi system and present its experimental schemes from prism-coupled SPRi and SPR microscopy (SPRM) to surface plasmonic scattering microscopy (SPSM); summarize its exciting applications, including molecular interaction analysis, molecular imaging and profiling, tracking of single entities, and analysis of single cells; and discuss its challenges in recent decade as well as the promising future.

Raman signal optimization based on residual network adaptive focusing

Due to its high sensitivity and specificity, Micro-Raman spectroscopy has emerged as a vital technique for molecular recognition and identification. As a weakly scattered signal, ensuring the accurate focus of the sample is essential for acquiring high quality Raman spectral signal and its analysis, especially in some complex microenvironments such as intracellular settings. Traditional autofocus methods are often time consuming or necessitate additional hardware, limiting real-time sample observation and device compatibility. Here, we propose an adaptive focusing method based on residual network to realize rapid and accurate focusing on Micro-Raman measurements. Using only a bright field image of the sample acquired on any image plane, we can predict the defocus distance with a residual network trained by Resnet50, in which the focus position is determined by combining the gradient and discrete cosine transform. Further, detailed regional division of the bright field map used for characterizing the height variation of actual sample surface is performed. As a result, a focus prediction map with 1μm accuracy is obtained from a bright field image in 120 ms. Based on this method, we successfully realize Raman signal optimization and the necessary correction of spectral information. This adaptive focusing method based on residual network is beneficial to further enhance the sensitivity and accuracy of Micro-Raman spectroscopy technology, which is of great significance in promoting the wide application of Raman spectroscopy.

High-precision microscopic autofocus with a single natural image

In industrial microscopic detection, learning-based autofocus methods have empowered operators to acquire high-quality images quickly. However, there are two parts of errors in Learning-based methods: the fitting error of the network model and the making error of the prior dataset, which limits the potential for further improvements in focusing accuracy. In this paper, a high-precision autofocus pipeline was introduced, which predicts the defocus distance from a single natural image. A new method for making datasets was proposed, which overcomes the limitations of the sharpness metric itself and improves the overall accuracy of the dataset. Furthermore, a lightweight regression network was built, namely Natural-image Defocus Prediction Model (NDPM), to improve the focusing accuracy. A realistic dataset of sufficient size was made to train all models. The experiment shows NDPM has better focusing performance compared with other models, with a mean focusing error of 0.422µm.

Recent advances in surface plasmon resonance imaging and biological applications

Surface Plasmon Resonance Imaging (SPRI) is a robust technique for visualizing refractive index changes, which enables researchers to observe interactions between nanoscale objects in an imaging manner. In the past period, scholars have been attracted by the Prism-Coupled and Non-prism Coupled configurations of SPRI and have published numerous experimental results. This review describes the principle of SPRI and discusses recent developments in Prism-Coupled and Non-prism Coupled SPRI techniques in detail, respectively. And then, major advances in biological applications of SPRI are reviewed, including four sub-fields (cells, viruses, bacteria, exosomes, and biomolecules). The purpose is to briefly summarize the recent advances of SPRI and provide an outlook on the development of SPRI in various fields.

Quantitative understanding of HepaRG cells during drug-induced intrahepatic cholestasis through changes in bile canaliculi dynamics

An understanding of the quantitative relationship between bile canaliculus (BC) dynamics and the disruption of tight junctions (TJs) during drug-induced intrahepatic cholestasis may lead to new strategies aimed at drug development and toxicity testing. To investigate the relationship between BC dynamics and TJ disruption, we retrospectively analyzed the extent of TJ disruption in response to changes in the dynamics of BCs cultured with entacapone (ENT). Three hours after adding ENT, the ZO-1-negative BC surface area ratio became significantly higher (4.1-fold) than those of ZO-1-positive BCs. Based on these data, we calculated slopes of surface area changes, m, of each ZO-1-positive and ZO-1-negative BC. BCs with m ≤ 15 that fell within the 95% confidence interval of ZO-1-positive BCs were defined as ZO-1-positive. To validate this method, we compared the frequency of ZO-1-positive BCs, F_Z , with that of BCs with m ≤ 15, F_T , in culture using drugs that regulate TJ, or induce intrahepatic cholestasis. F_T values were correlated with F_Z under all culture conditions (R²  = .99). Our results indicate that the magnitude of BC surface area changes is a factor affecting TJ disruption, suggesting that maintaining TJ integrity by slowing BC dilation inhibits cell death.

Automated Nanoparticle Analysis in Surface Plasmon Resonance Microscopy

The unique capability of surface plasmon resonance microscopy (SPRM) in single nanoparticle analysis has found use in various chemical and biological applications. While SPRM offers exceptional sensitivity, the statistical analysis of numerous nanoparticles has been extremely laborious and time-consuming. Herein, we presented an image processing software package for nanoparticle analysis in SPRM, which is empowered by a deep learning algorithm. This package enabled fully automated nanoparticle identification, digital counting, three-dimensional tracking of particle locations, and quantification of dwell time and Brownian motion properties. With a built-in image filtering process to improve the contrast, robust identification and analysis have been achieved from SPRM images of low refractive index nanoparticles. This software tool would largely promote the translation of SPRM technology into the digital sensing platform for high throughput sample screening.