Publications

@article{Xue:2022,

title = {Three-dimensional multi-site random access photostimulation (3D-MAP)},

author = {Yi Xue and Laura Waller and Hillel Adesnik and Nicolas Pégard},

doi = {10.7554/eLife.73266},

year  = {2022},

date = {2022-02-14},

journal = {eLife},

volume = { 2022},

number = {11},

pages = {e73266},

keywords = {3D imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Yanny:22,

title = {Deep learning for fast spatially varying deconvolution},

author = {Kyrollos Yanny and Kristina Monakhova and Richard W Shuai and Laura Waller},

url = {http://www.osapublishing.org/optica/abstract.cfm?URI=optica-9-1-96},

doi = {10.1364/OPTICA.442438},

year  = {2022},

date = {2022-01-01},

journal = {Optica},

volume = {9},

number = {1},

pages = {96--99},

publisher = {OSA},

abstract = {Deconvolution can be used to obtain sharp images or volumes from blurry or encoded measurements in imaging systems. Given knowledge of the system's point spread function (PSF) over the field of view, a reconstruction algorithm can be used to recover a clear image or volume. Most deconvolution algorithms assume shift-invariance; however, in realistic systems, the PSF varies laterally and axially across the field of view due to aberrations or design. Shift-varying models can be used, but are often slow and computationally intensive. In this work, we propose a deep-learning-based approach that leverages knowledge about the system's spatially varying PSFs for fast 2D and 3D reconstructions. Our approach, termed MultiWienerNet, uses multiple differentiable Wiener filters paired with a convolutional neural network to incorporate spatial variance. Trained using simulated data and tested on experimental data, our approach offers a 625textminus1600texttimes increase in speed compared to iterative methods with a spatially varying model, and outperforms existing deep-learning-based methods that assume shift invariance.},

keywords = {3D imaging, Hyperspectral imaging; Image quality; Imaging systems; Neural networks; Reconstruction algorithms; Three dimensional reconstruction, spatially-varying},

pubstate = {published},

tppubtype = {article}

}

@article{yanny2020,

title = {Miniscope3D: optimized single-shot miniature 3D fluorescence microscopy},

author = {Kyrollos Yanny and Nick Antipa and William Liberti and Sam Dehaeck and Kristina Monakhova and Fanglin Linda Liu and Konlin Shen and Ren Ng and Laura Waller},

url = {https://www.nature.com/articles/s41377-020-00403-7},

doi = {https://doi.org/10.1038/s41377-020-00403-7},

year  = {2020},

date = {2020-10-02},

journal = {Light: Science & Applications},

volume = {9},

number = {171},

abstract = {Miniature fluorescence microscopes are a standard tool in systems biology. However, widefield miniature microscopes capture only 2D information, and modifications that enable 3D capabilities increase the size and weight and have poor resolution outside a narrow depth range. Here, we achieve the 3D capability by replacing the tube lens of a conventional 2D Miniscope with an optimized multifocal phase mask at the objective’s aperture stop. Placing the phase mask at the aperture stop significantly reduces the size of the device, and varying the focal lengths enables a uniform resolution across a wide depth range. The phase mask encodes the 3D fluorescence intensity into a single 2D measurement, and the 3D volume is recovered by solving a sparsity-constrained inverse problem. We provide methods for designing and fabricating the phase mask and an efficient forward model that accounts for the field-varying aberrations in miniature objectives. We demonstrate a prototype that is 17 mm tall and weighs 2.5 grams, achieving 2.76 μm lateral, and 15 μm axial resolution across most of the 900 × 700 × 390 μm3 volume at 40 volumes per second. The performance is validated experimentally on resolution targets, dynamic biological samples, and mouse brain tissue. Compared with existing miniature single-shot volume-capture implementations, our system is smaller and lighter and achieves a more than 2× better lateral and axial resolution throughout a 10× larger usable depth range. Our microscope design provides single-shot 3D imaging for applications where a compact platform matters, such as volumetric neural imaging in freely moving animals and 3D motion studies of dynamic samples in incubators and lab-on-a-chip devices.},

keywords = {3D imaging, algorithms, diffuser, fluorescence imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Chen:20,

title = {Multi-layer Born multiple-scattering model for 3D phase microscopy},

author = {Michael Chen and David Ren and Hsiou-Yuan Liu and Shwetadwip Chowdhury and Laura Waller},

url = {http://www.osapublishing.org/optica/abstract.cfm?URI=optica-7-5-394},

doi = {10.1364/OPTICA.383030},

year  = {2020},

date = {2020-05-01},

journal = {Optica},

volume = {7},

number = {5},

pages = {394--403},

publisher = {OSA},

abstract = {We propose an accurate and computationally efficient 3D scattering model, multi-layer Born (MLB), and use it to recover the 3D refractive index (RI) of thick biological samples. For inverse problems recovering the complex field of thick samples, weak scattering models (e.g., first Born) may fail or underestimate the RI, especially with a large index contrast. Multi-slice (MS) beam propagation methods model multiple scattering to provide more realistic reconstructions; however, MS does not properly account for highly oblique scattering, nor does it model backward scattering. Our proposed MLB model uses a first Born model at each of many slices, accurately capturing the oblique scattering effects and estimating the backward scattering process. When used in conjunction with an inverse solver, the model provides more accurate RI reconstructions for high-resolution phase tomography. Importantly, MLB retains a reasonable computation time that is critical for practical implementation with iterative inverse algorithms.},

keywords = {3D imaging, multiple-scattering, optical models, phase imaging},

pubstate = {published},

tppubtype = {article}

}

@article{kuo2020chip,

title = {On-chip fluorescence microscopy with a random microlens diffuser},

author = { Grace Kuo and Fanglin Linda Liu and Irene Grossrubatscher and Ren Ng and Laura Waller},

url = {https://doi.org/10.1364/OE.382055},

doi = {10.1364/OE.382055},

year  = {2020},

date = {2020-03-09},

journal = {Optics Express},

volume = {28},

number = {6},

pages = {8384--8399},

publisher = {Optical Society of America},

keywords = {3D imaging, diffuser, fluorescence imaging, lensless imaging, on-chip},

pubstate = {published},

tppubtype = {article}

}

@article{ren2020multiple,

title = {A multiple scattering algorithm for three dimensional phase contrast atomic electron tomography},

author = { David Ren and Colin Ophus and Michael Chen and Laura Waller},

url = {https://www.sciencedirect.com/science/article/pii/S030439911930052X},

year  = {2020},

date = {2020-01-01},

journal = {Ultramicroscopy},

volume = {208},

pages = {112860},

publisher = {North-Holland},

keywords = {3D imaging, algorithms, multiple-scattering, phase imaging, TEM},

pubstate = {published},

tppubtype = {article}

}

@article{chowdhury2019high,

title = {High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images},

author = { Shwetadwip Chowdhury and Michael Chen and Regina Eckert and David Ren and Fan Wu and Nicole A Repina and Laura Waller},

url = {https://doi.org/10.1364/OPTICA.6.001211},

doi = {10.1364/OPTICA.6.001211},

year  = {2019},

date = {2019-09-16},

journal = {Optica},

volume = {6},

number = {9},

pages = {1211--1219},

publisher = {Optical Society of America},

keywords = {3D imaging, multiple-scattering, phase imaging},

pubstate = {published},

tppubtype = {article}

}

@article{ophus20193d,

title = {3D Imaging Using HAADF-STEM and HRTEM Atomic Electron Tomography},

author = { Colin Ophus and David Ren and Jihan Zhou and Hannah Devyldere and Michael Chen and Philipp M Pelz and Peter Ercius and Jianwei Miao and Mary C Scott and Laura Waller},

url = {https://doi.org/10.1017/S1431927619002708},

doi = {10.1017/S1431927619002708},

year  = {2019},

date = {2019-08-05},

journal = {Microscopy and Microanalysis},

volume = {25},

number = {S2},

pages = {394--395},

publisher = {Cambridge University Press},

keywords = {3D imaging, multiple-scattering, phase imaging, TEM},

pubstate = {published},

tppubtype = {article}

}

@article{yeh2019speckle,

title = {Speckle-structured illumination for 3D phase and fluorescence computational microscopy},

author = { Li-Hao Yeh and Shwetadwip Chowdhury and Nicole A Repina and Laura Waller},

url = {https://doi.org/10.1364/BOE.10.003635},

doi = {10.1364/BOE.10.003635},

year  = {2019},

date = {2019-07-01},

journal = {Biomedical optics express},

volume = {10},

number = {7},

pages = {3635--3653},

publisher = {Optical Society of America},

keywords = {3D imaging, high-throughput, multiple-scattering, speckle},

pubstate = {published},

tppubtype = {article}

}

@article{ophus2018linear,

title = {Linear and Nonlinear Reconstruction Algorithms for Atomic-Resolution Tomography Using Phase Contrast Electron Microscopy},

author = { Colin Ophus and David Ren and Michael Chen and Catherine Groschner and Mary C Scott and Laura Waller},

url = {https://doi.org/10.1017/S1431927618001046},

doi = {10.1017/S1431927618001046},

year  = {2018},

date = {2018-08-01},

journal = {Microscopy and Microanalysis},

volume = {24},

number = {S1},

pages = {110--111},

publisher = {Cambridge University Press},

keywords = {3D imaging, algorithms, phase imaging, TEM},

pubstate = {published},

tppubtype = {article}

}