Microwave-induced Thermoacoustic Imaging Research Articles

Deep-Learning-Based Microwave-Induced Thermoacoustic Tomography Applying Realistic Properties of Ultrasound Transducer

Deep-Learning-Based Microwave-Induced Thermoacoustic Tomography Applying Realistic Properties of Ultrasound Transducer

Design and Implementation of an Ultra-Wideband Water Immersion Antenna for Underwater Ultrasonic Sensing in Microwave-Induced Thermoacoustic Tomography

Microwave-induced thermoacoustic tomography (MITAT) holds significant promise in biomedical applications. It creates images using ultrasonic sensors to detect thermoacoustic signals induced by microwaves. The key to generating thermoacoustic signals that accurately reflect the fact is to achieve sufficient and uniform microwave power absorption of the testing target, which is closely tied to the microwave illumination provided by the antenna. In this article, we introduce a novel design and implementation of an ultra-wideband water immersion antenna for an MITAT system. We analyze and compare the advantages of selecting water as the background medium. Simulations are conducted to analyze the ultra-wideband characteristics in impedance matching, axial ratio, and radiation pattern of the proposed antenna. The measured |S11| shows good agreement with the simulated results. We also simulate the microwave power absorption of tumor and brain tissue, and the uniform microwave power absorption and high contrast between the tumor and brain indicate the excellent performance of the proposed antenna in the MITAT system.

Metal reflector-enhanced thermoacoustic imaging as a guidance for puncture biopsy

Puncture biopsy is an important clinical technique to obtain diseased tissue for pathological diagnosis, where imaging guidance is critical. In this paper, we describe a metal reflector-enhanced microwave-induced thermoacoustic imaging (TAI) approach capable of guiding puncture biopsy for detection of breast cancer and joint diseases. Numerical experimentations simulating puncture guidance in breast cancer and knee gout models were first conducted using (CST STUDIO SUITE) (CST) software, and then ex-vivo experiments were performed followed by qualitative observations and semi-quantitative analysis. The results of both the simulations and ex-vivo experiments showed that our reflector-enhanced TAI could image the puncture needle in high resolution with a large depth of [Formula: see text][Formula: see text]cm.

High-spatiotemporal resolution microwave-induced thermoacoustic tomography for imaging biological dynamics in deep tissue

Biological systems undergo constant dynamic changes across various spatial and temporal scales. To investigate the intricate biological dynamics in living organisms, there is a strong need for high-speed and high-resolution imaging capabilities with significant imaging depth. In this work, we present high-spatiotemporal resolution microwave-induced thermoacoustic tomography (HR-MTAT) as a method for imaging biological dynamics in deep tissues. HR-MTAT utilizes nanosecond pulsed microwave excitation and ultrasound detection, with appropriate spatial configurations, to achieve high coupling of the sample to the microwaves, to produce images in soft tissue with dielectric contrast and sub-millimeter spatial resolution (230 μm), to a depth of a few centimeters. Notably, by employing a 128-channel parallel signal acquisition and digitization strategy, the field programmable gate array module manages data synthesis, and GPU-based parallel pixel reconstruction facilitates HR-MTAT to accomplish single-frame image reconstruction in an impressive 50 μs. The practical feasibility of HR-MTAT was evaluated in live mice. The results show that HR-MTAT can noninvasively image whole-body small animals (up to 60 mm in depth) with clear resolution of internal organ structures at a frame rate of 100 Hz, without the need for labeling. At this high spatiotemporal resolution, HR-MTAT can capture respiration, heartbeat, and arterial pulse propagation without motion artifacts and track bio-nanoprobes in livers and tumors. These findings demonstrate HR-MTAT's ability to perform dynamic imaging with high contrast and resolution in deep tissues.

RPCA-based thermoacoustic imaging for microwave ablation monitoring

Microwave ablation (MWA) is a potent cancer treatment tool, but its effectiveness can be hindered by the lack of visual feedback. This paper validates the feasibility of using microwave-induced thermoacoustic imaging (TAI) technique to monitor the MWA process. A feasibility analysis was conducted at the principle level and a high-performance real-time TAI system was introduced. To address the interference caused by MWA, a robust principal component analysis (RPCA)-based method for TAI was proposed. This method leverages the correlation between multiple signal frames to eliminate interference. RPCA’s effectiveness in TAI was demonstrated through three sets of different experiments. Experiments demonstrated that TAI can effectively monitors the MWA process. This work represents the first application of RPCA-related matrix decomposition methods in TAI, paving the way for the application of TAI in more complex clinical scenarios. By providing rapid and accurate visual feedback, this research advances MWA technology.

Fully dense generative adversarial network for removing artifacts caused by microwave dielectric effect in thermoacoustic imaging

Microwave-induced thermoacoustic (TA) imaging (MTAI) combines pulsed microwave excitation and ultrasound detection to provide high contrast and spatial resolution images through dielectric contrast, which holds great promise for clinical applications. However, artifacts caused by microwave dielectric effect will seriously affect the accuracy of MTAI images that will hinder the clinical translation of MTAI. In this work, we propose a deep learning-based method fully dense generative adversarial network (FD-GAN) for removing artifacts caused by microwave dielectric effect in MTAI. FD-GAN adds the fully dense block to the generative adversarial network (GAN) based on the mutual confrontation between generator and discriminator, which enables it to learn both local and global features related to the removal of artifacts and generate high-quality images. The practical feasibility was tested in simulated, experimental data. The results demonstrate that FD-GAN can effectively remove the artifacts caused by the microwave dielectric effect, and shows superiority in denoising, background suppression, and improvement of image distortion. Our approach is expected to significantly improve the accuracy and quality of MTAI images, thereby enhancing the diagnostic accuracy of this innovative imaging technique.

Investigation of Transducer Distribution in Compressive Thermoacoustic Tomography for Breast Cancer Detection

Microwave-induced thermoacoustic tomography (MITAT) is a novel noninvasive method for biomedical applications like imaging and cancer detection. Recently, compressive sensing has been applied in MITAT, denoted as compressive thermoacoustic tomography (CTAT), can greatly reduce the number of measurements while maintain the image quality. This work reports the study on the effect of distribution of transducer array on image quality in CTAT for the application of breast tumor detection. Different distributions are tested by both simulation and experimental works to compare the quality of imaging. Three breast samples made of pork fat containing tumor-mimicking phantoms are used as the object to evaluate the effect of distribution of transducer array on image quality through the experiment. The results show that the transducer array which distributed evenly on a hemisphere surface is able to achieve an excellent image reconstruction result that is better than other tested types of transducer array distribution. This work gives a faithful guidance on the distribution strategy of transducer array for CTAT in breast tumor detection and other potential applications.

Optimized Excitation in Microwave-induced Thermoacoustic Imaging for Artifact Suppression.

Microwave-induced thermoacoustic imaging (M-TAI) allows the visualization of macroscopic and microscopic structures of bio-tissues. However, it suffers from severe inherent artifacts that might misguide the subsequent diagnostics and treatments of diseases. To overcome this limitation, we propose an optimized excitation strategy. In detail, the strategy integrates dynamically compound specific absorption rate (SAR) and co-planar configuration of polarization state, incident wave vector and imaging plane. Starting from the theoretical analysis, we interpret the underlying mechanism supporting the superiority of the optimized excitation strategy to achieve an effect equivalent to homogenizing the deposited electromagnetic energy in bio-tissues. The following numerical simulations demonstrate that the strategy enables better preservation of the conductivity weighting of samples while increasing Pearson correlation coefficient. Furthermore, the in vitro and in vivo M-TAI experiments validate the effectiveness and robustness of this optimized excitation strategy in artifact suppression, allowing the simultaneous identification of both boundary and inside fine structures within bio-tissues. All the results suggest that the optimized excitation strategy can be expanded to diverse scenarios, inspiring more suitable strategies that remarkably suppress the inherent artifacts in M-TAI.

Performance Evaluation of Focused Microwave Brain Hyperthermia Guided by Microwave-Induced Thermoacoustic Tomography

Performance Evaluation of Focused Microwave Brain Hyperthermia Guided by Microwave-Induced Thermoacoustic Tomography

Real-time thermoacoustic imaging for breast tumor biomarker biopsy navigation basing on a semi-ring ultrasonic transducer

Biopsy is an important means to obtain pathological tissue samples. The traditional imaging technologies have played a great role in clinical biopsy, but they still face some insurmountable problems. Microwave-induced thermoacoustic imaging has been demonstrated to be a powerful technique for visualizing biological tissue structures and functions due to its high resolution, deep imaging depth, and minimal biohazard, which shows great potential for biomarker biopsy navigation. Here, we reported a real-time microwave-pumped thermoacoustic imaging technique for breast tumor intervention biopsy guidance by a fast scanning semi-ring ultrasonic transducer with 128 elements. The system can achieve an imaging speed of about 25 frames per second, and spatial resolution was about 870 μm. The proposed system possesses obvious advantages, such as fast imaging and high resolution, that make it a promising option for breast tumor biomarker biopsy navigation.

Microwave-induced thermoacoustic elastic imaging: A simulation study

Microwave-induced thermoacoustic imaging (MTI) has the advantages of high resolution, high contrast, non-ionization, and non-invasive. Recently, MTI was used in the field of breast cancer screening. In this paper, based on the finite element method (FEM) and COMSOL Multiphysics software, a three-dimensional breast cancer model suitable for exploring the MTI process is proposed to investigate the influence of Young’s modulus (YM) of breast cancer tissue on MTI. It is found that the process of electromagnetic heating and initial pressure generation of the entire breast tissue is earlier in time than the thermal expansion process. Besides, compared with normal breast tissue, tumor tissue has a greater temperature rise, displacement, and pressure rise. In particular, YM of the tumor is related to the speed of thermal expansion. In particular, the larger the YM of the tumor is, the higher the heating and contraction frequency is, and the greater the maximum pressure is. Different Young’s moduli correspond to different thermoacoustic signal spectra. In MTI, this study can be used to judge different degrees of breast cancer based on elastic imaging. In addition, this study is helpful in exploring the possibility of microwave-induced thermoacoustic elastic imaging (MTAE).

Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on ResAttU-Net for Transcranial Brain Hemorrhage Detection.

Hemorrhagic stroke is a leading threat to human's health. The fast-developing microwave-induced thermoacoustic tomography (MITAT) technique holds potential to do brain imaging. However, transcranial brain imaging based on MITAT is still challenging due to the involved huge heterogeneity in speed of sound and acoustic attenuation of human skull. This work aims to address the adverse effect of the acoustic heterogeneity using a deep-learning-based MITAT (DL-MITAT) approach for transcranial brain hemorrhage detection. We establish a new network structure, a residual attention U-Net (ResAttU-Net), for the proposed DL-MITAT technique, which exhibits improved performance as compared to some traditionally used networks. We use simulation method to build training sets and take images obtained by traditional imaging algorithms as the input of the network. We present ex-vivo transcranial brain hemorrhage detection as a proof-of-concept validation. By using an 8.1-mm thick bovine skull and porcine brain tissues to perform ex-vivo experiments, we demonstrate that the trained ResAttU-Net is capable of efficiently eliminating image artifacts and accurately restoring the hemorrhage spot. It is proved that the DL-MITAT method can reliably suppress false positive rate and detect a hemorrhage spot as small as 3 mm. We also study effects of several factors of the DL-MITAT technique to further reveal its robustness and limitations. The proposed ResAttU-Net-based DL-MITAT method is promising for mitigating the acoustic inhomogeneity issue and performing transcranial brain hemorrhage detection. This work provides a novel ResAttU-Net-based DL-MITAT paradigm and paves a compelling route for transcranial brain hemorrhage detection as well as other transcranial brain imaging applications.

Split ring resonator topology based microwave induced thermoacoustic imaging (SRR-MTAI).

Microwave-induced thermoacoustic imaging (MTAI) using low-energy and long-wavelength microwave photons has great potential in detecting deep-seated diseases due to its unique ability of visualizing intrinsic electric properties of tissue in high resolution. However, the low contrast in conductivity between a target (e.g., a tumor) and the surroundings sets a fundamental limit for achieving a high imaging sensitivity, which significantly hinders its biomedical applications. To overcome this limit, we develop a split ring resonator (SRR) topology based MTAI (SRR-MTAI) approach to achieve highly sensitive detection by precise manipulation and efficient delivery of microwave energy. The in vitro experiments show that SRR-MTAI demonstrates an ultrahigh sensitivity of distinguishing a 0.4% difference in saline concentrations and a 2.5-fold enhancement of detecting a tissue target which mimicks a tumor embedded at a depth of 2 cm. The in vivo animal experiments conducted indicate that the imaging sensitivity between a tumor and the surrounding tissue is increased by 3.3-fold using SRR-MTAI. The dramatic enhancement in imaging sensitivity suggests that SRR-MTAI has the potential to open new avenues for MTAI to tackle a variety of biomedical problems that were impossible previously.

Microwave-induced thermoacoustic microscopy based on short-pulse microwave and high-frequency point-focused ultrasonic transducer.

As an emerging hybrid imaging modality, microwave-induced thermoacoustic imaging (MITAI) provides high contrast and deep tissue penetration, and has been extensively applied in cancer diagnosis, arthritis detection, and brain research. However, the previous studies had a limited spatial resolution of about 0.45-1.5mm. Here, we describe a microwave-induced thermoacoustic microscopy (MITAM) system to help overcome the resolution limitation of current MITAI to image more subtle tissue features. On this basis, this paper applies MITAM to the thin skin and to demonstrate the potential of MITAM in detecting scleroderma. To achieve high resolution, short pulse width microwave (pulse width: 70ns) and high-frequency ultrasonic point-focused transducer (center frequency: 25MHz) were used to build the MITAM system. Two parallel copper wires with a diameter of 90μm in the X/Y plane and Y/Z plane were imaged to estimate X/Y/Z resolution. Nine Balb/c mice were randomly divided into three groups and injected with different concentrations of bleomycin to induce scleroderma models. Their ex vivo skins were then imaged by our MITAM system. Visual observations were performed on the 3-dimensional skins MITAM images. And the mean value, Standard deviation, quartile distance, and signal-to-noise ratio were calculated to verify the results of the qualitative observations. Hematoxylin-Eosin (HE) and Masson staining were used to validate the findings of the MITAM. The thickness of each imaged skin was measured to be about 450μm. As an organ composed of multiple layers of tissues, the skin needs to be imaged at high resolution for the detection of related diseases. The results obtained showed that the improved resolution (68μm in the Z-axis and 135μm in the X-axis/Y-axis) of MITAM over conventional MITAI allowed us to differentiate scleroderma skins from normal skins and to identify the severity of scleroderma skins, consistent with the pathological findings of these skins. The preliminary results obtained indicate that the MITAM can relieve the resolution limitation of traditional MITAI and has the potential to detection scleroderma. However, the transmission-type MITAM mentioned in this paper is difficult to image in vivo due to the narrow area between the antenna and the transducer. In the future, a reflective scanning MITAM will be constructed to detect scleroderma in vivo.

A one-step method for quantitative microwave-induced thermoacoustic tomography.

Electrical conductivity directly correlates with tissue functional information such as blood and water contents, and quantitative extraction of tissue conductivity is of significant importance for disease detection and diagnosis using microwave-induced thermoacoustic tomography (TAT). The existing quantitative TAT (qTAT) approaches capable of extracting tissue conductivity require two steps for the recovery of conductivity. Such two steps approaches depend on an accurate knowledge of the microwave energy loss distribution in tissue and offer a slow computational convergence rate. The purpose of this study is to develop a new algorithm to reconstruct tissue conductivity with higher reconstruction accuracy and greater computational efficiency. We propose an improved qTAT method for direct recovery of tissue conductivity from thermoacoustic data measured along the boundary with only one step without the dependence of microwave energy loss information. The feasibility of our one-step qTAT method is validated in both simulated and tissue-mimicking phantom experiments with single-target and multi-target configurations with different contrast levels. Compared with the previous two-step methods, our one-step qTAT method improves the accuracy of conductivity recovery with approximately one-fold reduction in the mean absolute error (MAE) and root mean square error (RMSE) with p-values greater than 0.05. In addition, the convergence rate is improved by more than two folds for the one-step method. The study demonstrates that new method can quantitatively reconstruct conductivity of tissue more accurately and efficiently over the existing qTAT methods, leading to potentially enhanced accuracy for disease detection and diagnosis.

Quantitative Reconstruction of Dielectric Properties Based on Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography

Quantitative reconstruction of dielectric properties has enabled a wealth of biomedical applications. Although traditional microwave imaging and microwave-induced thermoacoustic tomography (MITAT) techniques have been widely explored for quantitative reconstruction, it is still highly challenging for them to deal with biological samples with high permittivity and conductivity. This work leverages deep-learning-enabled MITAT (DL-MITAT) approach to quantitatively reconstruct dielectric properties of biological samples with high quality. We construct a new network structure to separately reconstruct the permittivity and conductivity. By simulation and experimental testing, we demonstrate that the DL-MITAT technique is able to reliably reconstruct inhomogeneous biological samples with tumor, muscle, and fat. The experimental reconstruction error is only 5%. The network exhibits excellent generalization capability in terms of sample’s geometry. This work provides a useful paradigm and alternative way for quantitative reconstruction of dielectric properties and paves the way toward practical applications.

Dual-frequency microwave-induced thermoacoustic imaging method and its application

为解决传统微波热声成像技术对含有骨骼的复杂生物组织难以完整清晰重建的问题, 本文提出新型双频微波热声成像技术. 即利用两个不同中心频率短脉宽微波激励组织得到两组单频微波热声数据, 两组数据利用区域像素均值与区域吸收系数补偿进行能量校准后, 以骨骼在不同频率下微波能量吸收系数的比值进行加权相减, 最后得到减小骨骼副作用的双频微波热声图像. 本文首先对双频微波热声成像方法的原理进行了研究, 然后介绍了搭建的双频微波热声成像系统, 最后进行了兔膝关节与鸡跖骨成像实验, 并对成像结果进行了定性观察与半定量量化分析. 实验结果表明, 与单频微波热声成像相比, 双频微波热声成像中的组织估算尺寸和微波热声信号趋势与相应的组织实际尺寸和理论分析更加符合. 双频微波热声成像技术能够减小骨骼的“负造影”作用带来的影响, 从而使受此作用影响的半月板、交叉韧带和骨髓腔清晰完整重建. 综上, 此方法能够提高微波热声成像技术对含骨复杂生物组织的成像能力, 从而推动相关应用研究的进展, 并为需要缓解骨骼副作用的其它影像学方法提供参考.

Microwave-induced thermoacoustic imaging with a multi-cell AlScN piezoelectric micromachined ultrasonic transducer

This Letter reports on microwave-induced thermoacoustic imaging utilizing a multi-cell aluminum scandium nitride (AlScN) piezoelectric micromachined ultrasonic transducer (PMUT). Thermoacoustic signals are induced by microwave pulses and then detected by the ultrasonic transducer. It carries biological information, thus enabling noninvasive clinical diagnosis. Our AlScN PMUT device was characterized and experimented to show that it could significantly improve the imaging of the biological sample, decreasing the mean square error by up to 72%, which benefits from a 2.96 times larger detection angle of thermoacoustic signals compared to commercial ultrasonic transducers. The miniaturized and high-performance PMUT devices have the potential to be applied to clinical or personal diagnosis and monitoring equipment.

Compressive sensing combined deep learning for fast microwave-induced thermoacoustic tomography

Breast cancer is the second leading cause of cancer death in women, and early detection of breast cancer is essential for more effective treatment. Recently, microwave-induced thermoacoustic tomography (MITAT) based on compressive sensing has been proven to have great potential as a new detection tool for early breast cancer within low sampling time. However, the traditional MITAT reconstruction method based on compressive sensing requires many computing resources. To find a balance between low computing resources and high-resolution images for the method based on compressive sensing, especially in the environment of a non-uniform tissue, we developed a MITAT based on deep learning (DL-MITAT) imaging scheme compressive sensing-super-resolution thermoacoustic imaging (CS-SRTAI) method which decomposed the single reconstruction step into the initial reconstruction part and the super-resolution part. The initial reconstruction part extracts the necessary physical information into the low-resolution image space. The super-resolution part maps the low-resolution image space to the high-resolution image space. Specifically, we proposed a neural network in the super-resolution part. Both numerical simulation and the experiment demonstrate the effectiveness of the proposed method. The proposed method achieved 88% structural similarity index measure within computing resources of 21 s and 1.0 GB for the numerical simulation. Moreover, for the real breast tumor and non-uniform tissue experiment, the CS-SRTAI performs well at recovering the location, size, and number of the tumor within computing resources of 65 s and 1.1 GB. It is worth noting that the proposed DL-MITAT imaging strategy reduces computing resources with great imaging quality. It is promising to use in the fields where the computing resources for imaging are restricted.

Introduction to special issue on single cell analysis

Journal of Innovative Optical Health SciencesVol. 16, No. 02, 2302002 (2023) EditorialOpen AccessIntroduction to special issue on single cell analysisShuhua Yue, Xuantao Su, Minbiao Ji, Fu Wang, and Xunbin WeiShuhua YueSchool of Biological Science and Medical Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaE-mail Address: [email protected] Search for more papers by this author , Xuantao SuSchool of Microelectronics, Shandong University, 1500 Shunhua Road, Gaoxin District, Jinan 250101, ChinaE-mail Address: [email protected] Search for more papers by this author , Minbiao JiDepartment of Physics, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, ChinaE-mail Address: [email protected] Search for more papers by this author , Fu WangSchool of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Xuhui District, Shanghai 200030, ChinaE-mail Address: [email protected] Search for more papers by this author , and Xunbin WeiDepartment of Biomedical Engineering, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, ChinaE-mail Address: [email protected]Corresponding author. Search for more papers by this author https://doi.org/10.1142/S1793545823020029Cited by:0 Next This article is part of the issue: Special Issue on Single Cell AnalysisGuest Editors: Shuhua Yue, Xuantao Su, Minbiao Ji, Fu Wang and Xunbin Wei AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Librarian ShareShare onFacebookTwitterLinked InRedditEmail Increasing evidence has shown that cell populations are not homogeneous, but rather heterogeneous, even within very small cell populations. Bulk measurements based on the homogenized cell population do not account for the critical changes occurring in individual cells and are sometimes misleading. Cellular heterogeneity characteristics may be the key to address previously unsolved questions in disease development and progression. Therefore, it is crucial to develop novel single cell analysis techniques, including spectroscopy, imaging, sensing, manipulating, and sorting. This special issue, including three review articles and five original research articles, highlights recent progress in the development of single-cell optical analysis techniques and their applications in biological discovery, disease diagnosis, and treatment.A number of advanced optical imaging methods have been recently developed for single cell analysis. Stimulated Raman scattering (SRS) microscopy, an attractive imaging technique with advantages of high chemical selectivity, high spatial resolution, and high imaging speed, has become a powerful tool for biology and medicine. Jing Huang and Minbiao Ji reviewed the principles of SRS, discussed the technical developments and implementations of SRS microscopy, then highlighted and summarized its applications on biological cellular machinery, and finally shared visions of potential breakthroughs in the future.1 Light field microscopy (LFM) is an elegant non-scanning imaging tool to achieve real-time three-dimensional (3D) volumetric imaging of single cells with a single shot. Beibei Gao et al. reviewed the principle and development of LFM, discussed the improved approaches based on hardware designs and 3D reconstruction algorithms, and presented the applications in single-cell imaging.2 As an emerging single-cell imaging modality, microwave-induced thermoacoustic imaging (MTAI), with the assistance of functional nanoparticles, has shown broad prospects in biomedical and clinical applications. Xiaoyu Tang et al. reviewed the recent progress, challenges, and future directions of MTAI integrated with functional nanoparticles.3 Besides imaging instrumentation, processing algorithm is also very important for single-cell analysis. Shutong Liu et al. proposed a new feature extraction method in fluorescence imaging of nucleus to achieve automatic and accurate identification of apoptosis with low cost in terms of time,4 which may facilitate large-scale single-cell analysis.The advanced single-cell optical methods have been increasingly applied for biological discovery and disease diagnosis. Exosome has been considered as a promising biomarker for diagnosis of early-stage cancer. Xiao Ma et al. achieved label-free breast cancer detection and classification by convolutional neural network-based exosomes surface-enhanced Raman scattering.5 Actin cytoskeleton plays crucial roles in various cellular functions. Fulin Xing et al. reported the regulation of actin cytoskeleton via photolithographic micropatterning, which provides new insights into how geometry of extracellular matrix (ECM) regulates the organization of actin cytoskeleton.6 Siyi Qiu et al. revealed microbial spore responses to microwave radiation by single-cell analysis, providing a new perspective on the responses of living single cells to microwave radiation.7 Fanyi Kong et al. showed continuous infrared light suppressed transmembrane Na currents irrelevant to K channel on the freshly isolated hippocampal neuron cell, which could be explained by a membrane capacitance mediation process.8Finally, we would like to thank all the contributing authors for making this issue possible. More articles focused on Single Cell Analysis will be published in the next special issue.