Volume 29 Issue 3 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 29 · NO. 3 | March 2024

CONTENTS

IN THIS ISSUE

Review Papers (1)

General (2)

Imaging (5)

Microscopy (2)

Sensing (3)

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Review Papers

Fluorescence guided surgery imaging systems for breast cancer identification: a systematic review

Martha S. Kedrzycki, Hazel T. W. Chon, Maria Leiloglou, Vadzim Chalau, Daniel R. Leff, Daniel S. Elson

Journal of Biomedical Optics, Vol. 29, Issue 03, 030901, (March 2024) https://doi.org/10.1117/1.JBO.29.3.030901

TOPICS: Imaging systems, Tumors, Surgery, Cameras, Fluorescence, Breast, Tissues, Breast cancer, Fluorophores, Magnesium

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Significance

Breast-conserving surgery (BCS) is limited by high rates of positive margins and re-operative interventions. Fluorescence-guided surgery seeks to detect the entire lesion in real time, thus guiding the surgeons to remove all the tumor at the index procedure.

Aim

Our aim was to identify the optimal combination of a camera system and fluorophore for fluorescence-guided BCS.

Approach

A systematic review of medical databases using the terms “fluorescence,” “breast cancer,” “surgery,” and “fluorescence imaging” was performed. Cameras were compared using the ratio between the fluorescent signal from the tumor compared to background fluorescence, as well as diagnostic accuracy measures, such as sensitivity, specificity, and positive predictive value.

Results

Twenty-one studies identified 14 camera systems using nine different fluorophores. Twelve cameras worked in the infrared spectrum. Ten studies reported on the difference in strength of the fluorescence signal between cancer and normal tissue, with results ranging from 1.72 to 4.7. In addition, nine studies reported on whether any tumor remained in the resection cavity (5.4% to 32.5%). To date, only three studies used the fluorescent signal for guidance during real BCS. Diagnostic accuracy ranged from 63% to 98% sensitivity, 32% to 97% specificity, and 75% to 100% positive predictive value.

Conclusion

In this systematic review, all the studies reported a clinically significant difference in signal between the tumor and normal tissue using various camera/fluorophore combinations. However, given the heterogeneity in protocols, including camera setup, fluorophore studied, data acquisition, and reporting structure, it was impossible to determine the optimal camera and fluorophore combination for use in BCS. It would be beneficial to develop a standardized reporting structure using similar metrics to provide necessary data for a comparison between camera systems.

General

Continuous monitoring of endotracheal tube position with near infrared light

Tongtong Lu, Pawjai Khampang, Ahmed Beydoun, Anna Berezovsky, Rebecca Rohde, Wenzhou Hong, Bing Yu, Joseph Kerschner

Journal of Biomedical Optics, Vol. 29, Issue 03, 035001, (March 2024) https://doi.org/10.1117/1.JBO.29.3.035001

TOPICS: Sensors, Monte Carlo methods, Near infrared, Tissues, Skin, Calibration, Adipose tissue, In vivo imaging, Position sensors, Chest

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Combined frequency domain near-infrared spectroscopy and diffuse correlation spectroscopy system for comprehensive metabolic monitoring of inspiratory muscles during loading

Carlos Gómez, Laurent Brochard, Ewan Goligher, Dmitry Rozenberg, W. Darlene Reid, Darren Roblyer

Journal of Biomedical Optics, Vol. 29, Issue 03, 035002, (March 2024) https://doi.org/10.1117/1.JBO.29.3.035002

TOPICS: Muscles, Near infrared spectroscopy, Particle filters, Oxygen, Tissues, Blood circulation, Tunable filters, Spectroscopy, Scattering, Absorption

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Significance

Mechanical ventilation (MV) is a cornerstone technology in the intensive care unit as it assists with the delivery of oxygen in critically ill patients. The process of weaning patients from MV can be long and arduous and can lead to serious complications for many patients. Despite the known importance of inspiratory muscle function in the success of weaning, current clinical standards do not include direct monitoring of these muscles.

Aim

The goal of this project was to develop and validate a combined frequency domain near-infrared spectroscopy (FD-NIRS) and diffuse correlation spectroscopy (DCS) system for the noninvasive characterization of inspiratory muscle response to a load.

Approach

The system was fabricated by combining a custom digital FD-NIRS and DCS system. It was validated via liquid phantom titrations and a healthy volunteer study. The sternocleidomastoid (SCM), an accessory muscle of inspiration, was monitored during a short loading period in fourteen young, healthy volunteers. Volunteers performed two different respiratory exercises, a moderate load and a high load, which consisted of a one-minute baseline, a one-minute load, and a six-minute recovery period.

Results

The system has low crosstalk between absorption, reduced scattering, and flow when tested in a set of liquid titrations. Faster dynamics were observed for changes in blood flow index (BFi), and metabolic rate of oxygen (MRO2) compared with hemoglobin + myoglobin (Hb+Mb) based parameters after the onset of loads in males. Additionally, larger percent changes in BFi, and MRO2 were observed compared with Hb+Mb parameters in both males and females. There were also sex differences in baseline values of oxygenated Hb+Mb, total Hb+Mb, and tissue saturation.

Conclusions

The dynamic characteristics of Hb+Mb concentration and blood flow were distinct during loading of the SCM, suggesting that the combination of FD-NIRS and DCS may provide a more complete picture of inspiratory muscle dynamics.

Imaging

Direct three-dimensional segmentation of prostate glands with nnU-Net

Rui Wang, Sarah S. Chow, Robert Serafin, Weisi Xie, Qinghua Han, Elena Baraznenok, Lydia Lan, Kevin Bishop, Jonathan T. Liu

Journal of Biomedical Optics, Vol. 29, Issue 03, 036001, (March 2024) https://doi.org/10.1117/1.JBO.29.3.036001

TOPICS: Image segmentation, 3D modeling, Education and training, 3D image processing, Prostate, Data modeling, Biopsy, Pathology, Prostate cancer, Performance modeling

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Significance

In recent years, we and others have developed non-destructive methods to obtain three-dimensional (3D) pathology datasets of clinical biopsies and surgical specimens. For prostate cancer risk stratification (prognostication), standard-of-care Gleason grading is based on examining the morphology of prostate glands in thin 2D sections. This motivates us to perform 3D segmentation of prostate glands in our 3D pathology datasets for the purposes of computational analysis of 3D glandular features that could offer improved prognostic performance.

Aim

To facilitate prostate cancer risk assessment, we developed a computationally efficient and accurate deep learning model for 3D gland segmentation based on open-top light-sheet microscopy datasets of human prostate biopsies stained with a fluorescent analog of hematoxylin and eosin (H&E).

Approach

For 3D gland segmentation based on our H&E-analog 3D pathology datasets, we previously developed a hybrid deep learning and computer vision-based pipeline, called image translation-assisted segmentation in 3D (ITAS3D), which required a complex two-stage procedure and tedious manual optimization of parameters. To simplify this procedure, we use the 3D gland-segmentation masks previously generated by ITAS3D as training datasets for a direct end-to-end deep learning-based segmentation model, nnU-Net. The inputs to this model are 3D pathology datasets of prostate biopsies rapidly stained with an inexpensive fluorescent analog of H&E and the outputs are 3D semantic segmentation masks of the gland epithelium, gland lumen, and surrounding stromal compartments within the tissue.

Results

nnU-Net demonstrates remarkable accuracy in 3D gland segmentations even with limited training data. Moreover, compared with the previous ITAS3D pipeline, nnU-Net operation is simpler and faster, and it can maintain good accuracy even with lower-resolution inputs.

Conclusions

Our trained DL-based 3D segmentation model will facilitate future studies to demonstrate the value of computational 3D pathology for guiding critical treatment decisions for patients with prostate cancer.

Deep-learning approach to stratified reconstructions of tissue absorption and scattering in time-domain spatial frequency domain imaging

Yaru Zhang, Wenxing Bai, Yihan Dong, Mai Dan, Dongyuan Liu, Feng Gao

Journal of Biomedical Optics, Vol. 29, Issue 03, 036002, (March 2024) https://doi.org/10.1117/1.JBO.29.3.036002

TOPICS: Image restoration, Tissues, Education and training, Biomedical optics, Spatial resolution, Feature extraction, Computer simulations, Image resolution, Data modeling, Spatial frequencies

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Skin characterization of diabetes mellitus revealed by polarization-sensitive optical coherence tomography imaging

Wei Feng, Lisi Wang, Chun-Jie Liu, Chao Zhang

Journal of Biomedical Optics, Vol. 29, Issue 03, 036003, (March 2024) https://doi.org/10.1117/1.JBO.29.3.036003

TOPICS: Skin, Polarization, Collagen, Tissue optics, Optical coherence tomography, Birefringence, Coherence imaging, Tissues, Scanning electron microscopy, Biological imaging

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Structurally constrained and pathology-aware convolutional transformer generative adversarial network for virtual histology staining of human coronary optical coherence tomography images

Xueshen Li, Hongshan Liu, Xiaoyu Song, Charles Marboe, Brigitta Brott, Silvio Litovsky, Yu Gan

Journal of Biomedical Optics, Vol. 29, Issue 03, 036004, (March 2024) https://doi.org/10.1117/1.JBO.29.3.036004

TOPICS: Optical coherence tomography, Transformers, Pathology, Education and training, Design, Histopathology, Arteries, Visualization, Image quality, Gallium nitride

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Significance

There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease (CAD). However, existing methods either require a large pixel-wise paired training dataset or have limited capability to map pathological regions.

Aim

The aim of this work is to generate virtual histological information from coronary OCT images, without a pixel-wise paired training dataset while capable of providing pathological patterns.

Approach

We design a structurally constrained, pathology-aware, transformer generative adversarial network, namely structurally constrained pathology-aware convolutional transformer generative adversarial network (SCPAT-GAN), to generate virtual stained H&E histology from OCT images. We quantitatively evaluate the quality of virtual stained histology images by measuring the Fréchet inception distance (FID) and perceptual hash value (PHV). Moreover, we invite experienced pathologists to evaluate the virtual stained images. Furthermore, we visually inspect the virtual stained image generated by SCPAT-GAN. Also, we perform an ablation study to validate the design of the proposed SCPAT-GAN. Finally, we demonstrate 3D virtual stained histology images.

Results

Compared to previous research, the proposed SCPAT-GAN achieves better FID and PHV scores. The visual inspection suggests that the virtual histology images generated by SCPAT-GAN resemble both normal and pathological features without artifacts. As confirmed by the pathologists, the virtual stained images have good quality compared to real histology images. The ablation study confirms the effectiveness of the combination of proposed pathological awareness and structural constraining modules.

Conclusions

The proposed SCPAT-GAN is the first to demonstrate the feasibility of generating both normal and pathological patterns without pixel-wisely supervised training. We expect the SCPAT-GAN to assist in the clinical evaluation of treating the CAD by providing 2D and 3D histopathological visualizations.

Targeted multispectral filter array design for the optimization of endoscopic cancer detection in the gastrointestinal tract

Michaela Taylor-Williams, Ran Tao, Travis Sawyer, Dale Waterhouse, Jonghee Yoon, Sarah E. Bohndiek

Journal of Biomedical Optics, Vol. 29, Issue 03, 036005, (March 2024) https://doi.org/10.1117/1.JBO.29.3.036005

TOPICS: Tunable filters, Tissues, Colon, Optical filters, Design, Endoscopy, Cancer detection, Image classification, Scattering, Image filtering

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Microscopy

Compact simultaneous label-free autofluorescence multi-harmonic microscopy for user-friendly photodamage-monitored imaging

Geng Wang, Stephen Boppart, Haohua Tu

Journal of Biomedical Optics, Vol. 29, Issue 03, 036501, (March 2024) https://doi.org/10.1117/1.JBO.29.3.036501

TOPICS: Imaging systems, Pulse signals, Biomedical optics, Autofluorescence, Windows, Biological imaging, Signal to noise ratio, Sampling rates, Tissues, Laser sources

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Full-aperture extended-depth oblique plane microscopy through dynamic remote focusing

Paolo Pozzi, Vipin Balan, Alessia Candeo, Alessia Brix, Anna Silvia Pistocchi, Cosimo D’Andrea, Gianluca Valentini, Andrea Bassi

Journal of Biomedical Optics, Vol. 29, Issue 03, 036502, (March 2024) https://doi.org/10.1117/1.JBO.29.3.036502

TOPICS: Objectives, Microscopy, Sensors, Cameras, Image resolution, Camera shutters, Microscopes, Imaging systems, Telescopes, Dichroic mirrors

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Sensing

Non-invasive blood glucose estimation method based on the phase delay between oxy- and deoxyhemoglobin using visible and near-infrared spectroscopy

Tomoya Nakazawa, Rui Sekine, Masato Kitabayashi, Yu Hashimoto, Anna Ienaka, Keiji Morishita, Takeo Fujii, Masaki Ito, Fumie Matsushita

Journal of Biomedical Optics, Vol. 29, Issue 03, 037001, (March 2024) https://doi.org/10.1117/1.JBO.29.3.037001

TOPICS: Glucose, Blood, Prototyping, Near infrared, Sensors, Light sources, Oxygen, Cameras, Near infrared spectroscopy, Visible radiation

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Can diffuse reflectance spectroscopy identify shuntodynia in pediatric hydrocephalus patients?

Olivia Kline, Karthik Vishwanath, Boyd Colbrunn, Andrew Peachman, Jing Zhang, Sudhakar Vadivelu

Journal of Biomedical Optics, Vol. 29, Issue 03, 037002, (March 2024) https://doi.org/10.1117/1.JBO.29.3.037002

TOPICS: Tissues, Tissue optics, Statistical analysis, Data modeling, Hemodynamics, Oxygen, Diffuse reflectance spectroscopy, Scattering, Reflectivity, Skin

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Significance

Shuntodynia is patient reported pain at the site of the implanted ventriculoperitoneal (VP) shunt. Pediatric hydrocephalus requiring shunt placement is a chronic and prevalent standard of care treatment and requires lifetime management. Shuntodynia is a subjective measure of shunt dysfunction. Quantitative, white-light tissue spectroscopy could be used to objectively identify this condition in the clinic.

Aim

Pediatric subjects were recruited for optical sensing during routine clinical follow-up visits, post-VP shunt implantations. Acquired optical signals were translated into skin-hemodynamic signatures and were compared between subjects that reported shuntodynia versus those that did not.

Approach

Diffuse reflectance spectroscopy (DRS) measurements were collected between 450 and 700 nm using a single-channel fiber-optical probe from (N=35) patients. Multiple reflectance spectra were obtained by the attending physician from regions both proximal and distal to the VP shunt sites and from a matched contralateral site for each subject. Acquired reflectance spectra were processed quantitatively into functional tissue optical endpoints. A two-way, repeated measures analysis of variance was used to assess whether and which of the optical variables were statistically separable, across subjects with shuntodynia versus those without.

Results

Analyses indicated that intrapatient differences in vascular oxygen saturation measured between shunt sites relative to that obtained at the scar or contralateral sites was significantly lower in the pain group. We also find that the total hemoglobin concentrations at the shunt site were lowest relative to the other sites for subjects reporting pain. These findings suggest that shuntodynia pain arises in the scalp tissue around the implanted shunts and may be caused due to hypoxia and inflammation.

Conclusions

Optically derived hemodynamic variables were statistically significantly different in subjects presenting with shuntodynia relative to those without. DRS could provide a viable mode in routine bedside monitoring of subjects with VP shunts for clinical management and assessment of shuntodynia.

Remote and low-cost intraocular pressure monitoring by deep learning of speckle patterns

Zeev Kalyuzhner, Sergey Agdarov, Yevgeny Beiderman, Aviya Bennet, Yafim Beiderman, Zeev Zalevsky

Journal of Biomedical Optics, Vol. 29, Issue 03, 037003, (March 2024) https://doi.org/10.1117/1.JBO.29.3.037003

TOPICS: Mercury, Eye, Data modeling, Speckle pattern, Glaucoma, Education and training, Eye models, Deep learning, Sclera, Video

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