Summary:
UCLA researchers in the Department of Electrical and Computer Engineering have developed an all-optical quantitative phase imaging (QPI) method that could replace traditionally burdensome and power-inefficient computational image processing networks and improve current limitations in biological imaging.
Background:
The application of optical imaging technologies is essential in the field of biomedical research. However, due to the intrinsic weakly-scattering and transparent characteristics of biological samples, the field generally relies on histopathology for contrast and visualization. Due to the costly and burdensome nature of histopathological dies and a tendency to damage biological tissue, these methodologies have generated practical limitations on their widespread applicability. Recent advances in diffractive optical imaging may provide a solution, as these methods facilitate imaging of transparent objects without using exogenous agents such as dies. Quantitative phase imaging (QPI) has emerged as a promising method for application in a wide array of biomedical research applications. QPI applications generally consist of an optical imaging instrument and a computer that runs the image reconstruction algorithm. However, these methods also have limitations in that they generally provide qualitative information as opposed to quantitative. In order to improve the state of the art for QPI applications, there is a clear need to utilize all-optical and label-free technologies that are power-efficient and compact.
Innovation:
Researchers led by Aydogan Ozcan have developed an all-optical diffractive network that quantitatively reconstructs phase shift intensity from optically-transparent samples. The devised QPI network can synthesize a quantitative phase image of an object by converting the input phase information into intensity variations at the output plane. In other words, the diffractive network can reconstruct an output image based on how input light changes after passing through the sample. The solution is compact and removes the need for iterative computational methods to reconstruct images, enabling power-efficient, high frame-rate and compact phase imaging systems. This innovation can revolutionize the fields of microcopy and immunohistochemistry by removing the need for expensive computational methods and cumbersome cellular staining required for biological imaging. In addition, this solution can be applied in various other applications, including holographic displays, lighting, and illumination.
Demonstration Video:
Aydogan Ozcan - Diffractive Optical Networks & Computational Imaging Without a Computer
Potential Applications:
• On-chip microscopy
• Digital immunohistology
• Immunology
• Cell migration dynamics
• Holographic lighting and displays
Advantages:
• All-optical
• Power-efficient
• Generates quantitative outputs of transparent tissues
Development to Date:
First successful demonstration of the innovation completed; prototyping in progress.
Related Papers:
Mengu, Deniz & Ozcan, Aydogan. (2022). Diffractive all-optical computing for quantitative phase imaging. Optics. 2022, https://doi.org/10.48550/arXiv.2201.08964
Mengu, D., Ozcan, A., All-Optical Phase Recovery: Diffractive Computing for Quantitative Phase Imaging. Adv. Optical Mater. 2022, 10, 2200281. https://doi.org/10.1002/adom.202200281
Reference: UCLA Case No. 2022-174
Lead Inventor: Professor Aydogan Ozcan