The novel technology, created by scientists at the University of California, provides a workable way to get around a bottleneck caused by more laborious and computationally demanding existing 3D QPI techniques. A new work using a wavelength-multiplexed diffractive optical processor presented a state-of-the-art method for 3D QPI and was published in Advanced Photonics.
Artistic depiction of a wavelength-multiplexed diffractive optical processor for 3D quantitative phase imaging. Image Credit: Ozcan Lab, University of California
Light waves undergo a temporal delay when they go through a medium. This delay can reveal important details regarding the underlying structural and compositional properties. A state-of-the-art optical technology called Quantitative Phase Imaging (QPI) shows changes in the optical path length as light passes through materials, biological samples, and other transparent structures.
In contrast to conventional imaging techniques that depend on staining or labeling, QPI produces high-contrast images that enable noninvasive examinations vital to disciplines including biology, materials science, and engineering. These images allow researchers to observe and quantify phase fluctuations.
The UCLA team created a wavelength-multiplexed diffractive optical processor to all-optically convert the phase distributions of numerous 2D objects at different axial positions into intensity patterns, each stored at a distinct wavelength channel.
The design uses an intensity-only image sensor to obtain quantitative phase images of input objects situated at various axial planes, eliminating the requirement for digital phase recovery techniques.
We are excited about the potential of this new approach for biomedical imaging and sensing. Our wavelength-multiplexed diffractive optical processor offers a novel solution for high-resolution, label-free imaging of transparent specimens, which could greatly benefit biomedical microscopy, sensing, and diagnostics applications.
Aydogan Ozcan, Lead Researcher and Chancellor’s Professor, University of California
Deep learning optimizes wavelength multiplexing and passive diffractive optical elements in the novel multiplane QPI design. This design provides fast quantitative phase imaging of specimens across many axial planes by executing spectrally multiplexed phase-to-intensity transformations.
This system is a competitive analog substitute for conventional digital QPI techniques due to its compact design and all-optical phase recovery capability.
The method was validated by a proof-of-concept experiment that successfully imaged unique phase objects at various axial positions in the terahertz band. The design's scalability facilitates adaptation to various electromagnetic spectrum regions, such as the visible and infrared bands, through suitable nanofabrication techniques.
This opens up new avenues for integrating phase imaging solutions with focal plane arrays or image sensor arrays to create effective on-chip imaging and sensing devices.
This research will greatly impact numerous domains, including biological imaging, sensing, materials science, and environmental analysis. This technique can improve various applications, such as environmental sample monitoring, material characterization, illness diagnosis and research, and more, by offering a quicker and more effective way for 3D QPI.
Journal Reference:
Shen, C.-Y., et al. (2024) Multiplane quantitative phase imaging using a wavelength-multiplexed diffractive optical processor. Advanced Photonics. doi.org/10.1117/1.ap.6.5.056003.