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 University.
Lab Project Website: deepimaging.io
We've also been adding videos summarizing our work at our Lab YouTube Channel - please check this page out for lectures and other clips describing some of our work.
Multi-Camera Array Microscopes (MCAMs): Please check out mcam.deepimaging.io for details about our new MCAM devices. Details and more videos recorded by our new 3D Gigapixel MCAM can be found here! And gigapixel images can be viewed in our online gigaviewer platform!
The above two links host details about our specific experiments and projects, as well as open-sourced code and data. Please visit deepimaging.io and mcam.deepimaging.io to learn more about what we're up to in our lab!
Recent papers about Micro-camera array microscopes (MCAMs):
K. Kim et al., "Rapid 3D imaging at cellular resolution for digital cytopathology with a multi-camera array scanner (MCAS)," NPJ Imaging (2024). Project Page available here, and gigapixel scans viewable here !
K. Zhou et al., "Computational 3D topographic microscopy from terabytes of data per sample," Journal of Big Data (2024).
K. Zhou et al., "Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second," Nature Photonics (2023). Pre-print available here, and Project Page available here.
M. Harfouche et al., "Imaging across multiple spatial scales with the multi-camera array microscope", Optica (2023). Pre-print available here, and Project Page available here.
X. Yang et al., "Multi-modal imaging using a cascaded microscope design", Optics Letters (2023). Pre-print available here, and Project Page available here.
E. Thomson et al., "Gigapixel imaging with a multi-camera array microscope," eLife (2022). Project Page available here.
Other recent publications:
S. Xu et al., "Tensorial tomographic Fourier Ptychography with applications to muscle tissue imaging," Advanced Photonics (2024). Project Page available here.
L. Kriess et al., "Digital staining in optical microscopy using deep learning - a review," PhotoniX (2023). Project Page available here.
M. Wu et al., "scatterBrains: an open database of human head models and companion optode locations for realistic Monte Carlo photon simulations," J. Biomed. Opt. (2023). Associated open-source code and dataset is available here!
L. Bian et al., "High-resolution single-photon imaging with physics-informed deep learning," Nature Communications (2023).
T. Aidukas et al., "Fourier Ptychography Part III: Applications and Extensions," Microscopy Today (2022).
L. Loetgering et al., "Fourier Ptychography Part II: Phase Retrieval and High-Resolution Image Formation," Microscopy Today (2022).
C. Cooke et al., "A multiple instance learning approach for detecting COVID-19 in peripheral blood smears," PLOS Digital Health (2022).
S. Xu et al., "Transient Motion Classification Through Turbid Volumes via Parallelized Single-Photon Detection and Deep Contrastive Embedding," Frontiers in Neuroscience (2022).
S. Xu et al., "Imaging dynamics beneath turbid media via parallelized single-photon detection," Advanced Science (2022, pre-print available on arXiv)
K.C. Zhou et al., "Introduction to Fourier Ptychography: Part I," Microscopy Today (2022)
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).
X. Yang et al., "Quantized Fourier ptychography with binary images from SPAD cameras" Photonics Research (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
*Associated press releases from Duke, Phys.org, Science Daily
Summary
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. Novel imaging and image post-processing architectures that use dense arrays of micro-cameras
2. Microscopes 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
Opportunities!
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!