Acute leukemia (AL) is one of the top life-threatening diseases. Accurate typing of AL can significantly improve its prognosis. However, conventional methods for AL typing often require cell staining, which...

The physical mechanism of the dynamics in laser–material interaction has been an important research area. In addition to theoretical analysis, direct imaging‐based observation of ultrafast dynamic processes is an important approach to understand many fundamental issues in laser–material interaction such as inertial confinement fusion (ICF), laser accelerator construction, and advanced laser production. In this review, the principles and applications of three types of commonly used ultrafast imaging methods are introduced, including the pump–probe, X‐ray diagnosis, and single‐shot optical burst imaging. We focus on the technical features such as the spatial and temporal resolution for each technique, and present several conventional applications.

Optical time-stretch (OTS) imaging has shown significant advantages in many applications, such as highthroughput cell screening, for its high frame rate and the capability of continuous image acquisition. Unfortunately, its application in practice is fundamentally limited by the pressure on data transmission and storage caused by the extreme throughput. Compressed sensing (CS) has been considered a promising approach to solve this problem by recovering the signal at a sampling rate significantly lower than the Nyquist sampling rate. However, the temporal stretch and compression processes, which are currently realized with two sections of dispersive media with complementary GVD, make the system complex and introduce notable power loss to the system, leading to a low signal-to-noise ratio (SNR). In this work, we propose and demonstrate all-optical Fourier-domain-compressed time-stretch imaging with low-pass filtering. Only one section of dispersive media is needed to temporal stretch the pulse, while the compression of the stretched pulses is achieved with low-pass-filtering after optical-electrical conversion. Therefore, the structure of the system is notably simplified, and the SNR or the quality of the images can be improved. The principle of this method is theoretically analyzed, and its performance is experimentally demonstrated. The results show that our system can image flowing cells at a high flowing speed of 1 m/s, with a spatial resolution of 2.19 μm on condition that the original data are compressed by 80%. Our method can boost the application of OTS imaging in the fields where high-throughput and real-time imaging is required.

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Essential thrombocythemia (ET) is an uncommon situation in which the body produces too many platelets. This can cause blood clots anywhere in the body and results in various symptoms and even strokes or heart attacks. Removing excessive platelets using acoustofluidic methods receives extensive attention due to their high efficiency and high yield. While the damage to the remaining cells, such as erythrocytes and leukocytes is yet evaluated. Existing cell damage evaluation methods usually require cell staining, which are time‐consuming and labor‐intensive. In this paper, we investigate cell damage by optical time‐stretch (OTS) imaging flow cytometry with high throughput and in a label‐free manner. Specifically, we first image the erythrocytes and leukocytes sorted by acoustofluidic sorting chip with different acoustic wave powers and flowing speed using OTS imaging flow cytometry at a flowing speed up to 1 m/s. Then, we employ machine learning algorithms to extract biophysical phenotypic features from the cellular images, as well as to cluster and identify images. The results show that both the errors of the biophysical phenotypic features and the proportion of abnormal cells are within 10% in the undamaged cell groups, while the errors are much greater than 10% in the damaged cell groups, indicating that acoustofluidic sorting causes little damage to the cells within the appropriate acoustic power, agreeing well with clinical assays. Our method provides a novel approach for high‐throughput and label‐free cell damage evaluation in scientific research and clinical settings.