arXiv subjects

Kai Long

Publications and source records attributed to Kai Long.

4 recordsLinked to original sources

arXiv2025

Slanted light-sheet array microscopy for large volume imaging at rates exceeding 100 Hz

High-speed image acquisition in light microscopy is essential for a wide range of applications, including observing dynamic biological processes and enabling high-throughput sample analysis. However, traditional imaging speeds are often limited by the scanning mechanisms and the signal-to-noise ratio, and these constraints are further exacerbated by the need for volumetric imaging, optical sectioning, high spatial resolution, and large fields of view. To address these challenges, we have developed a slanted light-sheet array microscope (SLAM), which enables ultrafast volumetric imaging without compromising key technical specifications. SLAM is built on a standard wide-field compound microscope with minimal and straightforward modifications to the illumination path, allowing for easy integration. It can acquire multi-dimensional, high-resolution images at rates exceeding 100 volumes per second across large imaging regions (e.g., exceeding 500 pixels in transverse dimensions and 200 layers in depth). In addition, a deep learning approach based on conditional denoising diffusion probabilistic models is proposed to achieve isotropic resolution. Like traditional light-sheet microscopy, SLAM offers intrinsic optical sectioning and localized photochemistry, while its innovative optomechanical design is compatible with most biological samples prepared using conventional protocols. This makes SLAM a versatile and powerful imaging platform that is accessible to the broader biomedical research community.

physics.optics↗

arXiv2024

Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications

Photoacoustic imaging (PAI) represents an innovative biomedical imaging modality that harnesses the advantages of optical resolution and acoustic penetration depth while ensuring enhanced safety. Despite its promising potential across a diverse array of preclinical and clinical applications, the clinical implementation of PAI faces significant challenges, including the trade-off between penetration depth and spatial resolution, as well as the demand for faster imaging speeds. This paper explores the fundamental principles underlying PAI, with a particular emphasis on three primary implementations: photoacoustic computed tomography (PACT), photoacoustic microscopy (PAM), and photoacoustic endoscopy (PAE). We undertake a critical assessment of their respective strengths and practical limitations. Furthermore, recent developments in utilizing conventional or deep learning (DL) methodologies for image reconstruction and artefact mitigation across PACT, PAM, and PAE are outlined, demonstrating considerable potential to enhance image quality and accelerate imaging processes. Furthermore, this paper examines the recent developments in quantitative analysis within PAI, including the quantification of haemoglobin concentration, oxygen saturation, and other physiological parameters within tissues. Finally, our discussion encompasses current trends and future directions in PAI research while emphasizing the transformative impact of deep learning on advancing PAI.

cs.CV↗

arXiv2022

TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving

The next-generation high-resolution automotive radar (4D radar) can provide additional elevation measurement and denser point clouds, which has great potential for 3D sensing in autonomous driving. In this paper, we introduce a dataset named TJ4DRadSet with 4D radar points for autonomous driving research. The dataset was collected in various driving scenarios, with a total of 7757 synchronized frames in 44 consecutive sequences, which are well annotated with 3D bounding boxes and track ids. We provide a 4D radar-based 3D object detection baseline for our dataset to demonstrate the effectiveness of deep learning methods for 4D radar point clouds. The dataset can be accessed via the following link: https://github.com/TJRadarLab/TJ4DRadSet.

cs.CV↗

arXiv2018

Miura-ori tube metamaterial with tunable dynamic property

Structures and/or materials with engineered functionality, capable of achieving targeted mechanical responses reacting to changes in external excitation, have various potential engineering applications, e.g. aerospace, oceanographic engineering, soft robot, and several others. Yet tunable mechanical performance is normally realized through carefully designing the architecture of structures, which is usually porous, leading to the complexity of the fabrication of the structures even using the recently emerged 3D printing technique. In this study we show that origami technique can provide an alternative solution to achieving the aim by carefully stacking the classical Miura sheets into the Miura-ori tube metamaterial and tuning the geometric parameters of the origami metamaterial. By combining numerical and experimental methods, we have demonstrated that an extremely broad range of natural frequency and dynamic response of the metamaterial can be achieved. The proposed structure can be easily fabricated from a single thin sheet made of one material and simultaneously owns better mechanical properties than the Miura sheet.

physics.app-ph↗