Slow Imaging Speed Research Articles

Adaptive sampling strategy for Fourier single-pixel imaging

Fourier single-pixel imaging is a technique that utilizes Fourier bases as illumination patterns to capture images. While the use of orthogonal bases enhances the imaging quality compared with other methods of single pixel imaging, the slow imaging speed has hindered its broader application. Acquiring only low-frequency Fourier coefficients enables capturing a significant portion of image information at low sampling ratio, thereby enhancing imaging speed. Nevertheless, truncating frequencies results in a loss of image detail and a decrease in picture quality. To address this challenge, this paper introduces an adaptive sampling strategy for Fourier single-pixel imaging, allowing for customized sampling distributions for each specific object. The sampling distribution is dynamically generated based on the acquired Fourier coefficients during the acquisition process, eliminating the need for prior information. Through extensive simulations and experimental analysis, this approach has demonstrated its ability to capture more complex image details at equivalent sampling ratios.

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The ‘CMRxRecon’ dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.

Adaptive block imaging based on compressive sensing in AFM.

Atomic force microscopy (AFM) is a kind of high-precision instrument to measure the surface morphology of various conductive or nonconductive samples. However, obtaining a high-resolution image with standard AFM scanning requires more time. Using block compressive sensing (BCS) is an effective approach to achieve rapid AFM imaging. But, the routine BCS-AFM imaging is difficult to balance the image quality of each local area. It is easy to lead to excessive sampling in some flat areas, resulting in time-consuming. At the same time, there is a lack of sampling in some areas with significant details, resulting in poor imaging quality. Thus, an innovative adaptive BCS-AFM imaging method is proposed. The overlapped block is used to eliminate blocking artifacts. Characteristic parameters (GTV, Lu, and SD) are used to predict the local morphological characteristics of the samples. Back propagation neural network is employed to acquire the appropriate sampling rate of each sub-block. Sampling points are obtained by pre-scanning and adaptive supplementary scanning. Afterward, all sub-block images are reconstructed using the TVAL3 algorithm. Each sample is capable of achieving uniform, excellent image quality. Image visual effects and evaluation indicators (PSNR and SSIM) are employed for the purpose of evaluating and analyzing the imaging effects of samples. Compared with two nonadaptive and two other adaptive imaging schemes, our proposed scheme has the characteristics of a high degree of automation, uniformly high-quality imaging, and rapid imaging speed. HIGHLIGHTS: The proposed adaptive BCS method can address the issues of uneven image quality and slow imaging speed in AFM. The appropriate sampling rate of each sub-block of the sample can be obtained by BP neural network. The introduction of GTV, Lu, and SD can effectively reveal the morphological features of AFM images. Seven samples with different morphology are used to test the performance of the proposed adaptive algorithm. Practical experiments are carried out with two samples to verify the feasibility of the proposed adaptive algorithm.

Magnetic resonance image reconstruction based on image decomposition constrained by total variation and tight frame.

Magnetic resonance imaging (MRI) is a commonly used tool in clinical medicine, but it suffers from the disadvantage of slow imaging speed. To address this, we propose a novel MRI reconstruction algorithm based on image decomposition to realize accurate image reconstruction with undersampled k-space data. In our algorithm, the MR images to be recovered are split into cartoon and texture components utilizing image decomposition theory. Different sparse transform constraints are applied to each component based on their morphological structure characteristics. The total variation transform constraint is used for the smooth cartoon component, while the L0 norm constraint of tight frame redundant transform is used for the oscillatory texture component. Finally, an alternating iterative minimization approach is adopted to complete the reconstruction. Numerous numerical experiments are conducted on several MR images and the results consistently show that, compared with the existing classical compressed sensing algorithm, our algorithm significantly improves the peak signal-to-noise ratio of the reconstructed images and preserves more image details. Our algorithm harnesses the sparse characteristics of different image components to reconstruct MR images accurately with highly undersampled data. It can greatly accelerate MRI speed and be extended to other imaging reconstruction fields.

Cortex-wide transcranial localization microscopy with fluorescently labeled red blood cells

Large-scale imaging of brain activity with high spatio-temporal resolution is crucial for advancing our understanding of brain function. The existing neuroimaging techniques are largely limited by restricted field of view, slow imaging speed, or otherwise do not have the adequate spatial resolution to capture brain activities on a capillary and cellular level. To address these limitations, we introduce fluorescence localization microscopy aided with sparsely-labeled red blood cells for cortex-wide morphological and functional cerebral angiography with 4.9 µm spatial resolution and 1 s temporal resolution. When combined with fluorescence calcium imaging, the proposed method enables extended recordings of stimulus-evoked neuro-vascular changes in the murine brain while providing simultaneous multiparametric readings of intracellular neuronal activity, blood flow velocity/direction/volume, and vessel diameter. Owing to its simplicity and versatility, the proposed approach will become an invaluable tool for deciphering the regulation of cortical microcirculation and neurovascular coupling in health and disease.

Polarization-insensitive high-numerical-aperture metalens for wide-field super-resolution imaging.

The development of super-oscillatory lens (SOL) offers opportunities to realize far-field label-free super-resolution microscopy. Most microscopes based on a high numerical aperture (NA) SOL operate in the point-by-point scanning mode, resulting in a slow imaging speed. Here, we propose a high-NA metalens operating in the single-shot wide-field mode to achieve real-time super-resolution imaging. An optimization model based on the exhaustion algorithm and angular spectrum (AS) theory is developed for metalens design. We numerically demonstrate that the optimized metalens with an NA of 0.8 realizes the imaging resolution (imaging pixel size) about 0.85 times the Rayleigh criterion. The metalens can achieve super-resolution imaging of an object with over 200 pixels, which is one order of magnitude higher than the unoptimized metalens. Our method provides an avenue toward single-shot far-field label-free super-resolution imaging for applications such as real-time imaging of living cells and temporally moving particles.

Rapid, high-contrast, and steady volumetric imaging with Bessel-beam-based two-photon fluorescence microscopy.

Two-photon fluorescence microscopy (TPFM) excited by Gaussian beams requires axial tomographic scanning for three-dimensional (3D) volumetric imaging, which is a time-consuming process, and the slow imaging speed hinders its application for in vivo brain imaging. The Bessel focus, characterized by an extended depth of focus and constant resolution, facilitates the projection of a 3D volume onto a two-dimensional image, which significantly enhances the speed of volumetric imaging. We aimed to demonstrate the ability of a TPFM with a sidelobe-free Bessel beam to provide a promising tool for research in live biological specimens. Comparative in vivo imaging was conducted in live mouse brains and transgenic zebrafish to evaluate the performance of TPFM and Bessel-beam-based TPFM. Additionally, an image-difference method utilizing zeroth-order and third-order Bessel beams was introduced to effectively suppress background interference introduced by sidelobes. In comparison with traditional TPFM, the Bessel-beams-based TPFM demonstrated a 30-fold increase in imaging throughput and speed. Furthermore, the effectiveness of the image-difference method was validated in live biological specimens, resulting in a substantial enhancement of image contrast. Importantly, our TPFM with a sidelobe-free Bessel beam exhibited robustness against axial displacements, a feature of considerable value for in vivo experiments. We achieved rapid, high-contrast, and robust volumetric imaging of the vasculature in live mouse brains and transgenic zebrafish using our TPFM with a sidelobe-free Bessel beam.

Line image sensor-based colony fingerprinting system for rapid pathogenic bacteria identification

The rapid identification of pathogenic bacteria is crucial across various industries, including food or beverage manufacturing. Bacterial microcolony image-based classification has emerged as a promising approach to expedite identification, automate inspections, and reduce costs. However, conventional imaging methods have significant practical limitations, namely low throughput caused by the limited imaging range and slow imaging speed. To address these challenges, we developed an imaging system based on a line image sensor for rapid and wide-field imaging compared to existing colony imaging methods. This system can image a standard Petri dish (92 mm in diameter) completely within 22 s, successfully acquiring bacterial microcolony images. This process yielded a set of discrimination parameters termed as colony fingerprints, which were employed for machine learning. We demonstrated the performance of our system by identifying Staphylococcus aureus in food products using a machine learning model trained on a colony fingerprint dataset of 15 species from 9 genera, including foodborne pathogens. While conventional mass spectrometry-based methods require 24 h of incubation, our colony fingerprinting approach achieved 96% accuracy in just 10 h of incubation. Line image sensor offer high imaging speeds and scalability, allowing for swift and straightforward microbiological testing, eliminating the need for specialized expertise and overcoming the limitations of conventional methods. This innovation marks a transformative shift in industrial applications.

A CSAR 3D Imaging Method Suitable for Edge Computation

Due to the large amount of CSAR echo data carried by UAVs, either the original echo data need to be transmitted to the ground for processing or post-processing must be implemented after the flight. Therefore, it is difficult to use edge computing power such as a UAV onboard computer to implement image processing. The commonly used back projection (BP) algorithm and corresponding improved imaging algorithms require a large amount of computation and have slow imaging speed, which further limits the realization of CSAR 3D imaging on edge nodes. To improve the speed of CSAR 3D imaging, this paper proposes a CSAR 3D imaging method suitable for edge computation. Firstly, the improved Crazy Climber algorithm extracts sine track ridges that represent the amplitude changes in the range-compressed echo. Secondly, two-dimensional (2D) profiles of CSAR with different heights are obtained via inverse Radon transform (IRT). Thirdly, the Hough transform is used to extract the intersection points of the defocused circle along the heights in the X and Y directions. Finally, 3D point cloud extraction is completed through voting screening. In this paper, image detection methods such as ridge extraction, IRT, and Hough transform replace the phase compensation processing of the traditional BP 3D imaging method, which significantly reduces the time of CSAR 3D imaging. The correctness and effectiveness of the proposed method are verified by the 3D imaging results for the simulated data of ideal targets and X-band CSAR outfield flight raw data carried by a small rotor unmanned aerial vehicle (SRUAV). The proposed method provides a new direction for the fast 3D imaging of edge nodes, such as aircraft and small ground terminals. The image can be directly transmitted, which can improve the information transmission efficiency of the Internet of Things (IoT).

Segmentation of MR-guided, high intensity, focused ultrasound treated lesions based on diffusion-weighted imaging and interactive information

As the gold standard of traditional ablation treatment area, DCE-MRI has the disadvantages of slow imaging speed and only single examination during surgery. DWI is gradually being adopted as a new alternative. Considering the complex lesion status of uterine fibroids DWI, the interaction-based image segmentation method has a wider range of applications. Our proposed segmentation algorithm can be applied to a variety of cases and achieves a good segmentation Dice of 0.85.

Intelligent diagnosis value of preoperative T staging of colorectal cancer based on MR medical imaging.

Colorectal cancer is a common malignant tumor in clinic. With the change of people's diet, living environment and living habits, the incidence of colorectal cancer has risen sharply in recent years, which poses a great threat to people's health and quality of life. This paper aims to investigate the pathogenesis of colorectal cancer and improve the efficiency of clinical diagnosis and treatment. This paper firstly introduces MR Medical imaging technology and related theories of colorectal cancer through literature survey, and then applies MR technology to preoperative T staging of colorectal cancer. 150 patients with colorectal cancer admitted to our hospital every month from January 2019 to January 2020 were used as research objects to carry out the application experiment of MR Medical imaging in the intelligent diagnosis of preoperative T staging of colorectal cancer, and to explore the diagnostic sensitivity, specificity and histopathological T staging diagnosis coincidence rate of MR Staging. The final study results showed that there was no statistical significance in the general data of stage T1-2, T3 and T4 patients (p > 0.05); for patients with preoperative T stage of colorectal cancer, the overall diagnosis coincidence rate of MR Was 89.73%, indicating that it was highly consistent with pathological T stage; compared with MR Staging, the overall diagnosis coincidence rate of CT for preoperative T staging of colorectal cancer patients was 86.73%, which was basically consistent with the diagnosis of pathological T staging. At the same time, three different dictionary learning depth techniques are proposed in this study to solve the shortcomings of long MR Scanning time and slow imaging speed. Through performance testing and comparison, it is found that the structural similarity of MR Image reconstructed by depth dictionary method based on convolutional neural network is up to 99.67%, higher than that of analytic dictionary and synthetic dictionary, which proves that it has the best optimization effect on MR Technology. The study indicated the importance of MR Medical imaging in preoperative T staging diagnosis of colorectal cancer and the necessity of its popularization.

High-efficiency single-photon compressed sensing imaging based on the best choice scheme.

With single-photon sensitivity and picosecond resolution, single-photon imaging technology is an ideal solution for extreme conditions and ultra-long distance imaging. However, the current single-photon imaging technology has the problem of slow imaging speed and poor quality caused by the quantum shot noise and the fluctuation of background noise. In this work, an efficient single-photon compressed sensing imaging scheme is proposed, in which a new mask is designed by the Principal Component Analysis algorithm and the Bit-plane Decomposition algorithm. By considering the effects of quantum shot noise, dark count on imaging, the number of masks is optimized to ensure high-quality single-photon compressed sensing imaging with different average photon counts. The imaging speed and quality are greatly improved compared with the commonly used Hadamard scheme. In the experiment, a 64 × 64 pixels' image is obtained with only 50 masks, the sampling compression rate reaches 1.22%, and the sampling speed increases by 81 times. The simulation and experimental results demonstrated that the proposed scheme will effectively promote the application of single-photon imaging in practical scenarios.

Dual-channel structured illumination super-resolution quantitative fluorescence resonance energy transfer imaging

The Structured illumination (SI)-based super resolution fluorescence resonance energy transfer (SR-FRET) imaging technique, known as SISR-FRET, enables the investigation of molecular structures and functions in cellular organelles by resolving sub-diffraction FRET signals within living cells. The FRET microscopy offers unique advantages for quantitatively detecting dynamic interactions and spatial distribution of biomolecules within living cells. The spatial resolution of conventional FRET microscopy is limited by the diffraction limit, and it can only capture the average behavior of these events within the resolution limits of conventional fluorescence microscopy. The SISR-FRET performs sequential linear reconstruction of the three-channel SIM images followed by FRET quantitative analysis by using a common localization mask-based filtering approach. This two-step process ensures the fidelity of the reconstructed SR-FRET signals while effectively removing false-positive FRET signals caused by SIM artifacts. However, the slow imaging speed resulting from the switching of excitation-emission channels in SISR-FRET imaging limits its application in fast imaging scenarios. To address this issue, this study proposes a dual-channel structured illumination super-resolution quantitative FRET imaging system and method. By incorporating an FRET dual-channel imaging and registration module into the imaging pathway, the spatial switching and channel multiplexing of the SISR-FRET excitation-emission channels are achieved. Combining the image reconstruction algorithm with channel sub-pixel registration correction, the dual-channel SISR-FRET technique enhances the temporal resolution by 3.5 times while preserving the quantitative super-resolution FRET analysis. Experimental results are obtained by using a multi-color SIM system to perform super-resolution imaging of living cells expressing mitochondria outer membrane FRET standard plasmids. These experiments validate the improved spatial and temporal resolution of dual-channel SISR-FRET and the fidelity of FRET quantification analysis. In summary, this research presents a novel dual-channel structured illumination super-resolution FRET imaging system and method. It overcomes the limitations of slow imaging speed in SISR-FRET by realizing the spatial switching and channel multiplexing of excitation-emission channels. The proposed technique enhances the temporal resolution while maintaining quantitative analysis of super-resolution FRET. Experimental validation demonstrates the increased spatial and temporal resolution of dual-channel SISR-FRET and the accuracy of FRET quantification analysis. This advancement contributes to the study of molecular structures and functions in cellular organelles, providing valuable insights into the intricate mechanisms of living cells.

Fast and Multiplexed Super Resolution Imaging of Fixed and Immunostained Cells with DNA-PAINT-ERS.

Recent advances in super resolution microscopy have enabled imaging at the 10-20 nm scale on a light microscope, providing unprecedented details of native biological structures and processes in intact and hydrated samples. Of the existing strategies, DNA points accumulation in imaging nanoscale topography (DNA-PAINT) affords convenient multiplexing, an important feature in interrogating complex biological systems. A practical limitation of DNA-PAINT, however, has been the slow imaging speed. In its original form, DNA-PAINT imaging of each target takes tens of minutes to hours to complete. To address this challenge, several improved implementations have been introduced. These include DNA-PAINT-ERS (where E = ethylene carbonate; R = repeat sequence; S = spacer), a set of strategies that leads to both accelerated DNA-PAINT imaging speed and improved image quality. With DNA-PAINT-ERS, imaging of typical cellular targets such as microtubules takes only 5-10 min. Importantly, DNA-PAINT-ERS also facilitates multiplexing and can be easily integrated into current workflows for fluorescence staining of biological samples. Here, we provide a detailed, step-by-step guide for fast and multiplexed DNA-PAINT-ERS imaging of fixed and immunostained cells grown on glass substrates as adherent monolayers. The protocol should be readily extended to biological samples of a different format (for example tissue sections) or staining mechanisms (for example using nanobodies). © 2022 Wiley Periodicals LLC. Basic Protocol 1: Preparation of probes for DNA-PAINT-ERS Basic Protocol 2: Sample preparation for imaging membrane targets with DNA-PAINT-ERS in fixed cells Alternate Protocol: Immunostaining of extracted U2OS cells Basic Protocol 3: Super resolution image acquisition and analysis.

Freehand scanning photoacoustic microscopy with simultaneous localization and mapping

Optical-resolution photoacoustic microscopy offers high-resolution, label-free hemodynamic and functional imaging to many biomedical applications. However, long-standing technical barriers, such as limited field of view, bulky scanning probes, and slow imaging speed, have limited the application of optical-resolution photoacoustic microscopy. Here, we present freehand scanning photoacoustic microscopy (FS-PAM) that can flexibly image various anatomical sites. We develop a compact handheld photoacoustic probe to acquire 3D images with high speed, and great flexibility. The high scanning speed not only enables video camera mode imaging but also allows for the first implementation of simultaneous localization and mapping (SLAM) in photoacoustic microscopy. We demonstrate fast in vivo imaging of some mouse organs, and human oral mucosa. The high imaging speed greatly reduces motion artifacts and distortions from tissue moving, breathing, and unintended handshaking. We demonstrate small-lesion localization in a large region of the brain. FS-PAM offers a flexible high-speed imaging tool with an extendable field of view, enabling more biomedical imaging applications.

ResNet-based image inpainting method for enhancing the imaging speed of single molecule localization microscopy

Single molecule localization microscopy (SMLM) is a mainstream method in the field of super-resolution fluorescence microscopy that can achieve a spatial resolution of 20∼30 nm through a simple optical system. SMLM usually requires thousands of raw images to reconstruct a super-resolution image, and thus suffers from a slow imaging speed. Recently, several methods based on image inpainting have been developed to enhance the imaging speed of SMLM. However, these image inpainting methods may also produce erroneous local features (or called image artifacts), for example, incorrectly joined or split filaments. In this study, we use the ResNet generator, a network with strong local feature extraction capability, to replace the popularly-used U-Net generator to minimize the image artifact problem in current image inpainting methods, and develop an image inpainting method called DI-STORM. We validate our method using both simulated and experimental data, and demonstrate that DI-STORM has the best acceleration capability and produces the least artifacts in the repaired images, as compared with VDSR (the simplest CNN-based image inpainting method in SMLM) and ANNA-PALM (the best GAN-based image inpainting method in SMLM). We believe that DI-STORM could facilitate the application of deep learning-based image inpainting methods for SMLM.

Semantic ghost imaging based on recurrent-neural-network.

Ghost imaging (GI) illuminates an object with a sequence of light patterns and obtains the corresponding total echo intensities with a bucket detector. The correlation between the patterns and the bucket signals results in the image. Due to such a mechanism different from the traditional imaging methods, GI has received extensive attention during the past two decades. However, this mechanism also makes GI suffer from slow imaging speed and poor imaging quality. In previous work, each sample, including an illumination pattern and its detected bucket signal, was treated independently with each other. The correlation is therefore a linear superposition of the sequential data. Inspired by human's speech, where sequential words are linked with each other by a certain semantic logic and an incomplete sentence could still convey a correct meaning, we here propose a different perspective that there is potentially a non-linear connection between the sequential samples in GI. We therefore built a system based on a recurrent neural network (RNN), called GI-RNN, which enables recovering high-quality images at low sampling rates. The test with MNIST's handwriting numbers shows that, under a sampling rate of 1.28%, GI-RNN have a 12.58 dB higher than the traditional basic correlation algorithm and a 6.61 dB higher than compressed sensing algorithm in image quality. After trained with natural images, GI-RNN exhibits a strong generalization ability. Not only does GI-RNN work well with the standard images such as "cameraman", but also it can recover the natural scenes in reality at the 3% sampling rate while the SSIMs are greater than 0.7.

High-speed optical resolution photoacoustic microscopy with MEMS scanner using a novel and simple distortion correction method

Optical resolution photoacoustic microscopy (OR-PAM) is a remarkable biomedical imaging technique that can selectively visualize microtissues with optical-dependent high resolution. However, traditional OR-PAM using mechanical stages provides slow imaging speed, making it difficult to biologically interpret in vivo tissue. In this study, we developed a high-speed OR-PAM using a recently commercialized MEMS mirror. This system (MEMS-OR-PAM) consists of a 1-axis MEMS mirror and a mechanical stage. Furthermore, this study proposes a novel calibration method that quickly removes the spatial distortion caused by fast MEMS scanning. The proposed calibration method can easily correct distortions caused by both the scan geometry of the MEMS mirror and its nonlinear motion by running an image sequence only once using a ruler target. The combination of MEMS-OR-PAM and distortion correction method was verified using three experiments: (1) leaf skeleton phantom imaging to test the distortion correction efficacy; (2) spatial resolution and depth of field (DOF) measurement for system performance; (3) in-vivo finger capillary imaging to verify their biomedical use. The results showed that the combination could achieve a high-speed (32 s in 2 × 4 mm) and high lateral resolution (~ 6 µm) imaging capability and precisely visualize the circulating structure of the finger capillaries.

Fluorogenic DNA-PAINT for faster, low-background super-resolution imaging.

DNA-based points accumulation for imaging in nanoscale topography (DNA-PAINT) is a powerful super-resolution microscopy method that can acquire high-fidelity images at nanometer resolution. It suffers, however, from high background and slow imaging speed, both of which can be attributed to the presence of unbound fluorophores in solution. Here we present two-color fluorogenic DNA-PAINT, which uses improved imager probe and docking strand designs to solve these problems. These self-quenching single-stranded DNA probes are conjugated with a fluorophore and quencher at the terminals, which permits an increase in fluorescence by up to 57-fold upon binding and unquenching. In addition, the engineering of base pair mismatches between the fluorogenic imager probes and docking strands allowed us to achieve both high fluorogenicity and the fast binding kinetics required for fast imaging. We demonstrate a 26-fold increase in imaging speed over regular DNA-PAINT and show that our new implementation enables three-dimensional super-resolution DNA-PAINT imaging without optical sectioning.

Recent Trends in AFM Imaging Speed and Improvement Methods

: Atomic force microscope (AFM) has become the main tool for observation and manipulation in nanotechnology research due to its nano-meter high resolution, but the slow imaging speed is one of the important reasons hindering the further development of AFM. This article first introduces the applications of AFM in cell biology in recent years, expresses the importance of rapid imaging in cell biology, and then summarizes the reasons affecting the imaging speed of AFM from three aspects: the limited bandwidth of system mechanical components, obvious inherent characteristics of piezoelectric scanners, and complex image processing algorithms. The improvement and optimization methods of mechanical parts or structure, control algorithm and image processing are reviewed for different influence reasons. Then, the advantages and of different improvement methods, as well as the improved imaging speed and imaging quality improvement effects, are compared. Imaging speed and resolution are both several to dozens of times higher than before, while ensuring image quality and without damaging the samples. The aim of this review is to enable students, the public and even experts of different knowledge backgrounds to learn directly, and select realizable improvement methods according to realistic conditions. Finally, the future development trend and further prospects of high-speed AFM are discussed.