Application Progress of Lensless Microscopy Imaging Technology for Point-of-Care Testing

Conghui, 李聪慧 Li; Ruofan, 曹若凡 Cao; Xiayu, 许夏瑜 Xu; Fei, 李菲 Li; Zhenxi, 张镇西 Zhang; Feng, 徐峰 Xu

doi:10.3788/cjl201845.0207018

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, this technology has special requirements for sample preparation, making its application range relatively narrow. Its potential for resolution improvement in the optical spectrum is relatively limited, and it struggles with phase recovery in low-frequency regions [11][12][13]. Consequently, its overall application value is constrained.…”

Section: Introductionmentioning

confidence: 99%

Fourier Ptychographic Microscopy 10 Years on: A Review

Xu,

Wu,

Tan

et al. 2024

Cells

View full text Add to dashboard Cite

Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.

show abstract

Section: Introductionmentioning

confidence: 99%