The Computational Optics Lab develops new microscopes, cameras and computer algorithms for biomedical applications. The lab is directed by Dr. Roarke Horstmeyer, who is a new Assistant Professor in the Biomedical Engineering Department at Duke Univeristy.
Lab Project Website: deepimaging.io
We are now hosting project pages, which include many more details about our experiments, as well as open-sourced code and data, at deepimaging.io. Please visit there to learn more about what we're up to in our lab!
Most recent papers:
X. Dai et al., "Quantitative Jones matrix imaging using vectorial Fourier ptychography," Biomedical Optics Express (2022).
X. Yao et al., "Increasing a microscope’s effective field of view via overlapped imaging and machine learning," Optics Express (2022).
E. Thomson et al., "Gigapixel behavioral and neural activity imaging with a novel multi-camera array microscope," In Submission (pre-print on bioRxiv, 2021).
X. Yang et al., "Quantized Fourier ptychography with binary images from SPAD cameras" Photonics Research (2021).
C. Cooke et al., "Deep Optical Blood Analysis: COVID-19 Detection as a Case Study in Next Generation Blood Screening," In Submission (pre-print on MedRxiv, 2021).
S. Xu et al., "Imaging dynamics beneath turbid media via parallelized single-photon detection," In Submission (pre-print on ArXiv, 2021).
W. Liu*, R. Qian* et al., "Fast and sensitive diffuse correlation spectroscopy with highly parallelized single photon detection," APL Photonics (2021). Awarded the APL Photonics Future Luminary Award 2021!
K. Kim et al., "Multi-element microscope optimization by a learned sensing network with composite physical layers," Optics Letters (2020).
R. Horstmeyer et al. "Imaging deep in the brain with wavefront engineering," In Handbook of Neurophotonics (2020).
K. Zhou et al., "Diffraction tomography with a deep image prior," Optics Express (2020).
P. C. Konda et al., "Fourier ptychography: current applications and future promises," Optics Express (2020).
L. Loetgering et al., "Generation and characterization of focused helical x-ray beams," Science Advances (2020).
A. Muthumbi et al., "Learned Sensing: jointly optimized microscope hardware for accurate image classification," Biomed. Opt. Express (2019)
*Recent review article in Biophotonics Magazine
It is still surprisingly challenging to clearly capture many important biological events. The image resolution, field-of-view, and video frame rate of many optical recordings are still not as high as they should be. In addition, scattering prevents us from seeing beneath the very top layers of biological tissue. These limits are felt across many areas of study, from the pathologist who can only examine one small part of a histology slide at a time, to the neuroscientist who can only use light to monitor neural activity along the top surface of the brain.
The Computational Optics Lab develops new optical tools and algorithms to overcome these barriers. Here are a few current projects:
1. A microscope to capture gigavoxel-scale 3D images using principles from Fourier ptychography
2. Techniques to optically record in vivo neural activity in freely moving organisms
3. Enabling high-speed detection of blood flow and motion deep within tissue using principles from machine learning
Interested in joining the lab? We are currently seeking PhD and post-doctoral candidates! Please feel free to get in touch with me if you are interested in working with optics, with machine learning, or ideally with both. I am especially interested in those who know their way around an optics table!