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Article ; Online: scatterBrains: an open database of human head models and companion optode locations for realistic Monte Carlo photon simulations.

Wu, Melissa M / Horstmeyer, Roarke / Carp, Stefan A

2023 Volume 28, Issue 10, Page(s) 100501

Abstract: Significance: Monte Carlo (MC) simulations are currently the gold standard in the near-infrared and diffuse correlation spectroscopy (NIRS/DCS) communities for generating light transport paths through tissue. However, realistic and diverse models that ... ...

Abstract	Significance: Monte Carlo (MC) simulations are currently the gold standard in the near-infrared and diffuse correlation spectroscopy (NIRS/DCS) communities for generating light transport paths through tissue. However, realistic and diverse models that capture complex tissue layers are not easily available to all; moreover, manually placing optodes on such models can be tedious and time consuming. Such limitations may hinder the adoption of representative models for basic simulations and the use of these models for large-scale simulations, e.g., for training machine learning algorithms. Aim: We aim to provide the NIRS/DCS communities with an open-source, user-friendly database of morphologically and optically realistic head models, as well as a succinct software pipeline to prepare these models for mesh-based Monte Carlo simulations of light transport. Approach: Sixteen anatomical models were created from segmented T1-weighted magnetic resonance imaging (MRI) head scans and converted to tetrahedral mesh volumes. Approximately 800 companion scalp surface locations were distributed on each model, comprising full head coverage. A pipeline was created to place custom source and optical detectors at each location, and guidance is provided on how to use these parameters to set up MC simulations. Results: The models, head surface locations, and all associated code are freely available under the scatterBrains project on Github. Conclusions: The NIRS/DCS community benefits from having shared resources for conducting MC simulations on realistic head geometries. We hope this will make MRI-based head models and virtual optode placement easily accessible to all. Contributions to the database are welcome and encouraged.
MeSH term(s)	Humans ; Algorithms ; Computer Simulation ; Monte Carlo Method ; Phantoms, Imaging ; Photons ; Software ; Head
Language	English
Publishing date	2023-10-05
Publishing country	United States
Document type	Letter ; Research Support, N.I.H., Extramural
ZDB-ID	1309154-2
ISSN	1560-2281 ; 1083-3668
ISSN (online)	1560-2281
ISSN	1083-3668
DOI	10.1117/1.JBO.28.10.100501
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Diffraction tomography with a deep image prior.

Zhou, Kevin C / Horstmeyer, Roarke

Optics express

2020 Volume 28, Issue 9, Page(s) 12872–12896

Abstract: We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly ... ...

Abstract	We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly varying illumination. DP-DT processes the multi-angle data using a phase retrieval algorithm that is extended by a deep image prior (DIP), which reparameterizes the 3D sample reconstruction with an untrained, deep generative 3D convolutional neural network (CNN). We show that DP-DT effectively addresses the missing cone problem, which otherwise degrades the resolution and quality of standard 3D reconstruction algorithms. As DP-DT does not require pre-captured data or pre-training, it is not biased towards any particular dataset. Hence, it is a general technique that can be applied to a wide variety of 3D samples, including scenarios in which large datasets for supervised training would be infeasible or expensive. We applied DP-DT to obtain 3D RI maps of bead phantoms and complex biological specimens, both in simulation and experiment, and show that DP-DT produces higher-quality results than standard regularization techniques. We further demonstrate the generality of DP-DT, using two different scattering models, the first Born and multi-slice models. Our results point to the potential benefits of DP-DT for other 3D imaging modalities, including X-ray computed tomography, magnetic resonance imaging, and electron microscopy.
Language	English
Publishing date	2020-05-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.379200
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Multi-modal imaging using a cascaded microscope design.

Yang, Xi / Harfouche, Mark / Zhou, Kevin C / Kreiss, Lucas / Xu, Shiqi / Chandra Konda, Pavan / Kim, Kanghyun / Horstmeyer, Roarke

Optics letters

2023 Volume 48, Issue 7, Page(s) 1658–1661

Abstract: We present a multi-modal fiber array snapshot technique (M-FAST) based on an array of 96 compact cameras placed behind a primary objective lens and a fiber bundle array. Our technique is capable of large-area, high-resolution, multi-channel video ... ...

Abstract	We present a multi-modal fiber array snapshot technique (M-FAST) based on an array of 96 compact cameras placed behind a primary objective lens and a fiber bundle array. Our technique is capable of large-area, high-resolution, multi-channel video acquisition. The proposed design provides two key improvements to prior cascaded imaging system approaches: a novel optical arrangement that accommodates the use of planar camera arrays, and a new ability to acquire multi-modal image data acquisition. M-FAST is a multi-modal, scalable imaging system that can acquire snapshot dual-channel fluorescence images as well as differential phase contrast measurements over a large 6.59 mm × 9.74 mm field-of-view at 2.2-
Language	English
Publishing date	2023-05-23
Publishing country	United States
Document type	Journal Article
ISSN	1539-4794
ISSN (online)	1539-4794
DOI	10.1364/OL.471380
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Quantitative Jones matrix imaging using vectorial Fourier ptychography.

Dai, Xiang / Xu, Shiqi / Yang, Xi / Zhou, Kevin C / Glass, Carolyn / Konda, Pavan Chandra / Horstmeyer, Roarke

Biomedical optics express

2022 Volume 13, Issue 3, Page(s) 1457–1470

Abstract: This paper presents a microscopic imaging technique that uses variable-angle illumination to recover the complex polarimetric properties of a specimen at high resolution and over a large field-of-view. The approach extends Fourier ptychography, which is ... ...

Abstract	This paper presents a microscopic imaging technique that uses variable-angle illumination to recover the complex polarimetric properties of a specimen at high resolution and over a large field-of-view. The approach extends Fourier ptychography, which is a synthetic aperture-based imaging approach to improve resolution with phaseless measurements, to additionally account for the vectorial nature of light. After images are acquired using a standard microscope outfitted with an LED illumination array and two polarizers, our vectorial Fourier ptychography (vFP) algorithm solves for the complex 2x2 Jones matrix of the anisotropic specimen of interest at each resolved spatial location. We introduce a new sequential Gauss-Newton-based solver that additionally jointly estimates and removes polarization-dependent imaging system aberrations. We demonstrate effective vFP performance by generating large-area (29 mm
Language	English
Publishing date	2022-02-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	2572216-5
ISSN	2156-7085
ISSN	2156-7085
DOI	10.1364/BOE.448804
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Book ; Online: Diffraction tomography with a deep image prior

Zhou, Kevin C. / Horstmeyer, Roarke

2019

Abstract	We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly varying illumination. DP-DT processes the multi-angle data using a phase retrieval algorithm that is extended by a deep image prior (DIP), which reparameterizes the 3D sample reconstruction with an untrained, deep generative 3D convolutional neural network (CNN). We show that DP-DT effectively addresses the missing cone problem, which otherwise degrades the resolution and quality of standard 3D reconstruction algorithms. As DP-DT does not require pre-captured data or pre-training, it is not biased towards any particular dataset. Hence, it is a general technique that can be applied to a wide variety of 3D samples, including scenarios in which large datasets for supervised training would be infeasible or expensive. We applied DP-DT to obtain 3D RI maps of bead phantoms and complex biological specimens, both in simulation and experiment, and show that DP-DT produces higher-quality results than standard regularization techniques. We further demonstrate the generality of DP-DT, using two different scattering models, the first Born and multi-slice models. Our results point to the potential benefits of DP-DT for other 3D imaging modalities, including X-ray computed tomography, magnetic resonance imaging, and electron microscopy.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Physics - Biological Physics ; Physics - Optics
Subject code	006
Publishing date	2019-12-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: High-resolution single-photon imaging with physics-informed deep learning.

Bian, Liheng / Song, Haoze / Peng, Lintao / Chang, Xuyang / Yang, Xi / Horstmeyer, Roarke / Ye, Lin / Zhu, Chunli / Qin, Tong / Zheng, Dezhi / Zhang, Jun

Nature communications

2023 Volume 14, Issue 1, Page(s) 5902

Abstract: High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit ... ...

Abstract	High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 × 32 pixels, 90 scenes, 10 different bit depths, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this physical noise model, we synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depths, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique in a series of experiments including microfluidic inspection, Fourier ptychography, and high-speed imaging. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance.
Language	English
Publishing date	2023-09-22
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-41597-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Multi-element microscope optimization by a learned sensing network with composite physical layers.

Kim, Kanghyun / Konda, Pavan Chandra / Cooke, Colin L / Appel, Ron / Horstmeyer, Roarke

Optics letters

2020 Volume 45, Issue 20, Page(s) 5684–5687

Abstract: Standard microscopes offer a variety of settings to help improve the visibility of different specimens to the end microscope user. Increasingly, however, digital microscopes are used to capture images for automated interpretation by computer algorithms ( ... ...

Abstract	Standard microscopes offer a variety of settings to help improve the visibility of different specimens to the end microscope user. Increasingly, however, digital microscopes are used to capture images for automated interpretation by computer algorithms (e.g., for feature classification, detection, or segmentation), often without any human involvement. In this work, we investigate an approach to jointly optimize multiple microscope settings, together with a classification network, for improved performance with such automated tasks. We explore the interplay between optimization of programmable illumination and pupil transmission, using experimentally imaged blood smears for automated malaria parasite detection, to show that multi-element "learned sensing" outperforms its single-element counterpart. While not necessarily ideal for human interpretation, the network's resulting low-resolution microscope images (20X-comparable) offer a machine learning network sufficient contrast to match the classification performance of corresponding high-resolution imagery (100X-comparable), pointing a path toward accurate automation over large fields-of-view.
Language	English
Publishing date	2020-10-14
Publishing country	United States
Document type	Journal Article
ISSN	1539-4794
ISSN (online)	1539-4794
DOI	10.1364/OL.401105
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Fourier ptychography: current applications and future promises.

Konda, Pavan Chandra / Loetgering, Lars / Zhou, Kevin C / Xu, Shiqi / Harvey, Andrew R / Horstmeyer, Roarke

Optics express

2020 Volume 28, Issue 7, Page(s) 9603–9630

Abstract: Traditional imaging systems exhibit a well-known trade-off between the resolution and the field of view of their captured images. Typical cameras and microscopes can either "zoom in" and image at high-resolution, or they can "zoom out" to see a larger ... ...

Abstract	Traditional imaging systems exhibit a well-known trade-off between the resolution and the field of view of their captured images. Typical cameras and microscopes can either "zoom in" and image at high-resolution, or they can "zoom out" to see a larger area at lower resolution, but can rarely achieve both effects simultaneously. In this review, we present details about a relatively new procedure termed Fourier ptychography (FP), which addresses the above trade-off to produce gigapixel-scale images without requiring any moving parts. To accomplish this, FP captures multiple low-resolution, large field-of-view images and computationally combines them in the Fourier domain into a high-resolution, large field-of-view result. Here, we present details about the various implementations of FP and highlight its demonstrated advantages to date, such as aberration recovery, phase imaging, and 3D tomographic reconstruction, to name a few. After providing some basics about FP, we list important details for successful experimental implementation, discuss its relationship with other computational imaging techniques, and point to the latest advances in the field while highlighting persisting challenges.
Language	English
Publishing date	2020-03-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.386168
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: High-resolution single-photon imaging with physics-informed deep learning

Liheng Bian / Haoze Song / Lintao Peng / Xuyang Chang / Xi Yang / Roarke Horstmeyer / Lin Ye / Chunli Zhu / Tong Qin / Dezhi Zheng / Jun Zhang

Nature Communications, Vol 14, Iss 1, Pp 1-

2023 Volume 13

Abstract: Abstract High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of ...

Abstract	Abstract High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 × 32 pixels, 90 scenes, 10 different bit depths, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this physical noise model, we synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depths, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique in a series of experiments including microfluidic inspection, Fourier ptychography, and high-speed imaging. The experiments validate the technique’s state-of-the-art super-resolution SPAD imaging performance.
Keywords	Science ; Q
Subject code	006
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning.

DiSpirito, Anthony / Li, Daiwei / Vu, Tri / Chen, Maomao / Zhang, Dong / Luo, Jianwen / Horstmeyer, Roarke / Yao, Junjie

IEEE transactions on medical imaging

2021 Volume 40, Issue 2, Page(s) 562–570

Abstract: One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed. In this study, we propose a novel application of deep learning principles to reconstruct undersampled PAM images ... ...

Abstract	One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed. In this study, we propose a novel application of deep learning principles to reconstruct undersampled PAM images and transcend the trade-off between spatial resolution and imaging speed. We compared various convolutional neural network (CNN) architectures, and selected a Fully Dense U-net (FD U-net) model that produced the best results. To mimic various undersampling conditions in practice, we artificially downsampled fully-sampled PAM images of mouse brain vasculature at different ratios. This allowed us to not only definitively establish the ground truth, but also train and test our deep learning model at various imaging conditions. Our results and numerical analysis have collectively demonstrated the robust performance of our model to reconstruct PAM images with as few as 2% of the original pixels, which can effectively shorten the imaging time without substantially sacrificing the image quality.
MeSH term(s)	Animals ; Deep Learning ; Image Processing, Computer-Assisted ; Mice ; Microscopy ; Neural Networks, Computer ; Spectrum Analysis
Language	English
Publishing date	2021-02-03
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2020.3031541
Database	MEDical Literature Analysis and Retrieval System OnLINE

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