Volume 28 Issue 3 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 28 · NO. 3 | March 2023

CONTENTS

IN THIS ISSUE

General (2)

Imaging (9)

Microscopy (1)

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General

Deep learning-based optical approach for skin analysis of melanin and hemoglobin distribution

Geunho Jung, Semin Kim, Jongha Lee, Sangwook Yoo

Journal of Biomedical Optics, Vol. 28, Issue 03, 035001, (March 2023) https://doi.org/10.1117/1.JBO.28.3.035001

TOPICS: Skin, RGB color model, Education and training, Cameras, Tissue optics, Image processing, Light sources, Image acquisition, Performance modeling, Correlation coefficients

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Evaluating clinical near-infrared surgical camera systems with a view to optimizing operator and computational signal analysis

Jeffrey Dalli, Abhinav Jindal, Gareth Gallagher, Jonathan P. Epperlein, Niall P. Hardy, Ra’ed Malallah, Kilian O’Donoghue, Padraig Cantillon-Murphy, Pól G. Mac Aonghusa, Ronan A. Cahill

Journal of Biomedical Optics, Vol. 28, Issue 03, 035002, (March 2023) https://doi.org/10.1117/1.JBO.28.3.035002

TOPICS: Cameras, Imaging systems, Fluorescence, Laparoscopy, Fluorescence intensity, Near infrared, Sensors, Tissues, Tumors, Visualization

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Imaging

Characterizing a photoacoustic and fluorescence imaging platform for preclinical murine longitudinal studies

Weylan R. Thompson, Hans-Peter F. Brecht, Vassili Ivanov, Anthony M. Yu, Diego S. Dumani, Dylan J. Lawrence, Stanislav Y. Emelianov, Sergey A. Ermilov

Journal of Biomedical Optics, Vol. 28, Issue 03, 036001, (March 2023) https://doi.org/10.1117/1.JBO.28.3.036001

TOPICS: Spatial resolution, Imaging systems, Photoacoustic spectroscopy, In vivo imaging, Acquisition tracking and pointing, Fluorescence imaging, Vacuum chambers, Biological imaging, Cameras, Reconstruction algorithms

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Unrolled-DOT: an interpretable deep network for diffuse optical tomography

Yongyi Zhao, Ankit Raghuram, Fay Wang, Stephen Hyunkeol Kim, Andreas Hielscher, Jacob Robinson, Ashok Veeraraghavan

Journal of Biomedical Optics, Vol. 28, Issue 03, 036002, (March 2023) https://doi.org/10.1117/1.JBO.28.3.036002

TOPICS: Image restoration, Education and training, Reconstruction algorithms, Machine learning, Image quality, Data modeling, Diffuse optical tomography, Optical networks, Matrices, Visualization

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Using pressure-driven flow systems to evaluate laser speckle contrast imaging

Colin T. Sullender, Adam Santorelli, Lisa M. Richards, Pawan K. Mannava, Christopher Smith, Andrew K. Dunn

Journal of Biomedical Optics, Vol. 28, Issue 03, 036003, (March 2023) https://doi.org/10.1117/1.JBO.28.3.036003

TOPICS: Laser speckle contrast imaging, Microfluidics, Sensors, Imaging systems, Laser systems engineering, Equipment, Control systems, Speckle, Cameras, Reproducibility

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Solid optical tissue phantom tools based on upconverting nanoparticles for biomedical applications

Gokhan Dumlupinar, Sanathana Konugolu Venkata Sekar, Claudia Nunzia Guadagno, Jean S. Matias, Pranav Lanka, Chris K.W. Kho, Stefan Andersson-Engels

Journal of Biomedical Optics, Vol. 28, Issue 03, 036004, (March 2023) https://doi.org/10.1117/1.JBO.28.3.036004

TOPICS: Tissues, Biomedical optics, Solids, Tissue optics, Nanoparticles, Scattering, Optical properties, Biophotonic applications, Silicon, Deep tissue imaging

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Color-resolved Cherenkov imaging allows for differential signal detection in blood and melanin content

Vihan A. Wickramasinghe, Savannah M. Decker, Samuel S. Streeter, Austin M. Sloop, Arthur F. Petusseau, Daniel A. Alexander, Petr Bruza, David J. Gladstone, Rongxiao Zhang, Brian W. Pogue

Journal of Biomedical Optics, Vol. 28, Issue 03, 036005, (March 2023) https://doi.org/10.1117/1.JBO.28.3.036005

TOPICS: Blood, Tissues, Cameras, Color imaging, Signal attenuation, RGB color model, Biological imaging, Skin, Signal detection, Color

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Significance

High-energy x-ray delivery from a linear accelerator results in the production of spectrally continuous broadband Cherenkov light inside tissue. In the absence of attenuation, there is a linear relationship between Cherenkov emission and deposited dose; however, scattering and absorption result in the distortion of this linear relationship. As Cherenkov emission exits the absorption by tissue dominates the observed Cherenkov emission spectrum. Spectroscopic interpretation of this effects may help to better relate Cherenkov emission to ionizing radiation dose delivered during radiotherapy.

Aim

In this study, we examined how color Cherenkov imaging intensity variations are caused by absorption from both melanin and hemoglobin level variations, so that future Cherenkov emission imaging might be corrected for linearity to delivered dose.

Approach

A custom, time-gated, three-channel intensified camera was used to image the red, green, and blue wavelengths of Cherenkov emission from tissue phantoms with synthetic melanin layers and varying blood concentrations. Our hypothesis was that spectroscopic separation of Cherenkov emission would allow for the identification of attenuated signals that varied in response to changes in blood content versus melanin content, because of their different characteristic absorption spectra.

Results

Cherenkov emission scaled with dose linearly in all channels. Absorption in the blue and green channels increased with increasing oxy-hemoglobin in the blood to a greater extent than in the red channel. Melanin was found to absorb with only slight differences between all channels. These spectral differences can be used to derive dose from measured Cherenkov emission.

Conclusions

Color Cherenkov emission imaging may be used to improve the optical measurement and determination of dose delivered in tissues. Calibration for these factors to minimize the influence of the tissue types and skin tones may be possible using color camera system information based upon the linearity of the observed signals.

Adaptive correction method of hybrid aberrations in Fourier ptychographic microscopy

Ruofei Wu, Jiaxiong Luo, Jiancong Li, Hanbao Chen, Junrui Zhen, Sicong Zhu, Zicong Luo, Yanxiong Wu

Journal of Biomedical Optics, Vol. 28, Issue 03, 036006, (March 2023) https://doi.org/10.1117/1.JBO.28.3.036006

TOPICS: Reconstruction algorithms, Image restoration, Pupil aberrations, Imaging systems, Aberration correction, Phase reconstruction, Lawrencium, Wavefront aberrations, Biological samples, Microscopy

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Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning

Martin Hultman, Marcus Larsson, Tomas Strömberg, Ingemar Fredriksson

Journal of Biomedical Optics, Vol. 28, Issue 03, 036007, (March 2023) https://doi.org/10.1117/1.JBO.28.3.036007

TOPICS: Tissues, Laser speckle contrast imaging, Artificial neural networks, Doppler effect, Red blood cells, Perfusion imaging, Data modeling, Education and training, Blood, Skin

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Significance

Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverse Monte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC × mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances in multi-exposure LSCI (MELSCI) enable the analysis in an imaging modality.

Aim

To present a method for speed-resolved perfusion imaging in absolute units %RBC × mm/s, computed from multi-exposure speckle contrast images.

Approach

An artificial neural network (ANN) was trained on a large simulated dataset of multi-exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation.

Results

Speed-resolved perfusion was estimated in the three speed intervals 0 to 1 mm / s, 1 to 10 mm / s, and >10 mm / s, with relative errors 9.8%, 12%, and 19%, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures.

Conclusions

The ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates the physiological interpretation of measurements using MELSCI and may increase the clinical impact of the technique.

Toward reliable calcification detection: calibration of uncertainty in object detection from coronary optical coherence tomography images

Hongshan Liu, Xueshen Li, Abdul Latif Bamba, Xiaoyu Song, Brigitta C. Brott, Silvio H. Litovsky, Yu Gan

Journal of Biomedical Optics, Vol. 28, Issue 03, 036008, (March 2023) https://doi.org/10.1117/1.JBO.28.3.036008

TOPICS: Object detection, Calibration, Optical coherence tomography, Deep learning, Image segmentation, Arteries, Data modeling, Electrochemical etching, Computer aided detection, Image processing

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In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer

Sandryne David, Trang Tran, Frédérick Dallaire, Guillaume Sheehy, Feryel Azzi, Dominique Trudel, Francine Tremblay, Atilla Omeroglu, Frédéric Leblond, Sarkis Meterissian

Journal of Biomedical Optics, Vol. 28, Issue 03, 036009, (March 2023) https://doi.org/10.1117/1.JBO.28.3.036009

TOPICS: Cancer, Raman spectroscopy, Tissues, Tumor growth modeling, Machine learning, Breast, Surgery, Breast cancer, Biomolecules, Tumors

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Microscopy

Small training dataset convolutional neural networks for application-specific super-resolution microscopy

Varun Mannam, Scott Howard

Journal of Biomedical Optics, Vol. 28, Issue 03, 036501, (March 2023) https://doi.org/10.1117/1.JBO.28.3.036501

TOPICS: Education and training, Image analysis, Data modeling, Convolutional neural networks, Fluorescence microscopy, Gallium nitride, Point spread functions, Performance modeling, Lawrencium, Network architectures

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Significance

Machine learning (ML) models based on deep convolutional neural networks have been used to significantly increase microscopy resolution, speed [signal-to-noise ratio (SNR)], and data interpretation. The bottleneck in developing effective ML systems is often the need to acquire large datasets to train the neural network. We demonstrate how adding a “dense encoder-decoder” (DenseED) block can be used to effectively train a neural network that produces super-resolution (SR) images from conventional microscopy diffraction-limited (DL) images trained using a small dataset [15 fields of view (FOVs)].

Aim

The ML helps to retrieve SR information from a DL image when trained with a massive training dataset. The aim of this work is to demonstrate a neural network that estimates SR images from DL images using modifications that enable training with a small dataset.

Approach

We employ “DenseED” blocks in existing SR ML network architectures. DenseED blocks use a dense layer that concatenates features from the previous convolutional layer to the next convolutional layer. DenseED blocks in fully convolutional networks (FCNs) estimate the SR images when trained with a small training dataset (15 FOVs) of human cells from the Widefield2SIM dataset and in fluorescent-labeled fixed bovine pulmonary artery endothelial cells samples.

Results

Conventional ML models without DenseED blocks trained on small datasets fail to accurately estimate SR images while models including the DenseED blocks can. The average peak SNR (PSNR) and resolution improvements achieved by networks containing DenseED blocks are ≈3.2 dB and 2 × , respectively. We evaluated various configurations of target image generation methods (e.g., experimentally captured a target and computationally generated target) that are used to train FCNs with and without DenseED blocks and showed that including DenseED blocks in simple FCNs outperforms compared to simple FCNs without DenseED blocks.

Conclusions

DenseED blocks in neural networks show accurate extraction of SR images even if the ML model is trained with a small training dataset of 15 FOVs. This approach shows that microscopy applications can use DenseED blocks to train on smaller datasets that are application-specific imaging platforms and there is promise for applying this to other imaging modalities, such as MRI/x-ray, etc.

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