Volume 30 Issue 1 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 30 · NO. 1 | January 2025

CONTENTS

IN THIS ISSUE

Editorial (2)

JBO Letters (1)

General (3)

Imaging (7)

Therapeutic (1)

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Editorial

New Year, New JBO Editor-in-Chief

Muyinatu Lediju Bell

Journal of Biomedical Optics, Vol. 30, Issue 01, 010101, (January 2025) https://doi.org/10.1117/1.JBO.30.1.010101

TOPICS: Biomedical optics, Video, Ultrasonics, Systems engineering, Photoacoustic spectroscopy, Photoacoustic imaging, Neurophotonics, Lead, Laboratories, Biomedical applications

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JBO 2024 List of Reviewers

Journal of Biomedical Optics, Vol. 30, Issue 01, 010102, (January 2025) https://doi.org/10.1117/1.JBO.30.1.010102

TOPICS: Lithium, Chaos, Sun, Rain, Nickel, Molybdenum, Lutetium, Gallium nitride, Fluctuations and noise, Content addressable memory

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JBO Letters

Quantitative assessment of the generalizability of a brain tumor Raman spectroscopy machine learning model to various tumor types including astrocytoma and oligodendroglioma

Frédéric Leblond, Frédérick Dallaire, Katherine Ember, Alice Le Moël, Victor Blanquez-Yeste, Hugo Tavera, Guillaume Sheehy, Trang Tran, Marie-Christine Guiot, Alexander Weil, Roy Dudley, Costas Hadjipanayis, Kevin Petrecca

Journal of Biomedical Optics, Vol. 30, Issue 01, 010501, (January 2025) https://doi.org/10.1117/1.JBO.30.1.010501

TOPICS: Tumors, Brain, Raman spectroscopy, Cancer detection, Data modeling, Machine learning, Brain tissue, Education and training, Signal to noise ratio, Surgery

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Significance

Maximal safe resection of brain tumors can be performed by neurosurgeons through the use of accurate and practical guidance tools that provide real-time information during surgery. Current established adjuvant intraoperative technologies include neuronavigation guidance, intraoperative imaging (MRI and ultrasound), and 5-ALA for fluorescence-guided surgery.

Aim

We have developed intraoperative Raman spectroscopy as a real-time decision support system for neurosurgical guidance in brain tumors. Using a machine learning model, trained on data from a multicenter clinical study involving 67 patients, the device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. Here, the aim is to assess the generalizability of a predictive model trained with data from this study to other types of brain tumors.

Approach

A method was developed to assess the generalizability of the model, quantifying performance for tumors including astrocytoma, oligodendroglioma and ependymoma, pediatric glioblastoma, and classification of glioblastoma data acquired in the presence of 5-ALA induced fluorescence. Statistical analyses were conducted to assess the impact of vibrational bands beyond contributors identified in our previous research.

Results

A machine learning brain tumor detection model showed a positive predictive value (PPV) of 70% for astrocytoma, 74% for oligodendroglioma, and 100% for ependymoma. Furthermore, the PPV was 100% in classifying spectra from a pediatric glioblastoma and 90% for detecting adult glioblastoma labeled with 5-ALA-induced fluorescence. Univariate statistical analyses applied to individual vibrational bands demonstrated that the inclusion of Raman biomarkers unexploited to date had the potential to improve detectability, setting the stage for future advances.

Conclusions

Developing predictive models relying on the inelastic scattering contrast from a wider pool of Raman bands may improve detection accuracy for astrocytoma and oligodendroglioma. To do so, larger tumor datasets and a higher Raman photon signal-to-noise ratio may be required.

General

Effects of cold storage on double integrating sphere optical property measurements of porcine dermis and subcutaneous fat from 400 to 1100 nm

Maria A. Hoffman, Mark Keppler, Andrea Smith, Anjelyka Fasci, Matthew Macasadia, Amanda Tijerina, R. Lyle Hood, Michael DeLisi, Joel Bixler

Journal of Biomedical Optics, Vol. 30, Issue 01, 015001, (January 2025) https://doi.org/10.1117/1.JBO.30.1.015001

TOPICS: Tissues, Biological samples, Optical properties, Absorption, Integrating spheres, Scattering, Reflectivity, Skin, Optical spheres, Transmittance

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Analytical sensitivity factors from distributions of time of flight of photons for near-infrared spectroscopy studies in multilayered turbid media

Hector García, Demián A. Vera, Nicolás Carbone, María Waks-Serra, Juan Pomarico

Journal of Biomedical Optics, Vol. 30, Issue 01, 015002, (January 2025) https://doi.org/10.1117/1.JBO.30.1.015002

TOPICS: Simulations, Absorption, Photons, Turbidity, Factor analysis, Sensors, Biological research, Near infrared spectroscopy, Analytic models, Multilayers

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Two-stage diffuse fluorescence tomography for monitoring of drug distribution in photodynamic therapy of tumors

Stefan Šušnjar, Muhammad Daniyal Ghauri, Björn Thomasson, Sanathana Konugolu Venkata Sekar, Stefan Andersson-Engels, Johannes Swartling, Nina Reistad

Journal of Biomedical Optics, Vol. 30, Issue 01, 015003, (January 2025) https://doi.org/10.1117/1.JBO.30.1.015003

TOPICS: Fluorophores, Absorption, Photodynamic therapy, Fluorescence tomography, Optical properties, Reconstruction algorithms, Image restoration, Chemical elements, Tissues, Picosecond phenomena

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Imaging

Development of a portable testing chamber to assess imaging performance of laparoscopes in low- and middle-income countries

Anne Christine Barnes, Michele Kaluzienski, Tri Quang, Jason Chen, Surabhi Singh, Wilhelm Smith, Talya Simcox, Paula Kworekwa, Rebecca Kaaya Nansubuga, Robert Ssekitoleko, Tamara Fitzgerald, Jenna Mueller

Journal of Biomedical Optics, Vol. 30, Issue 01, 016001, (January 2025) https://doi.org/10.1117/1.JBO.30.1.016001

TOPICS: MATLAB, Distortion, Image quality, Image analysis, Laparoscopy, Image resolution, Glasses, Portability, Vacuum chambers, Light sources and illumination

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Comparative analysis of intraoperative thermal and optical imaging for identification of the human primary sensory cortex

Juliane Müller, Martin Oelschlägel, Stephan Sobottka, Matthias Kirsch, Gerald Steiner, Edmund Koch, Christian Schnabel

Journal of Biomedical Optics, Vol. 30, Issue 01, 016002, (January 2025) https://doi.org/10.1117/1.JBO.30.1.016002

TOPICS: Brain, Tumors, Optical imaging, Visualization, Surgery, Brain mapping, Biological research, Spatial resolution, Somatosensory cortex, Thermography

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Significance

The precise identification and preservation of functional brain areas during neurosurgery are crucial for optimizing surgical outcomes and minimizing postoperative deficits. Intraoperative imaging plays a vital role in this context, offering insights that guide surgeons in protecting critical cortical regions.

Aim

We aim to evaluate and compare the efficacy of intraoperative thermal imaging (ITI) and intraoperative optical imaging (IOI) in detecting the primary somatosensory cortex, providing a detailed assessment of their potential integration into surgical practice.

Approach

Data from nine patients undergoing tumor resection in the region of the somatosensory cortex were analyzed. Both IOI and ITI were employed simultaneously, with a specific focus on the areas identified as the primary somatosensory cortex (S1 region). The methodologies included a combination of imaging techniques during distinct phases of rest and stimulation, confirmed by electrophysiological monitoring of somatosensory evoked potentials to verify the functional areas identified by both imaging methods. The data were analyzed using a Fourier-based analytical framework to distinguish physiological signals from background noise.

Results

Both ITI and IOI successfully generated reliable activity maps following median nerve stimulation. IOI showed greater consistency across various clinical scenarios, including those involving cortical tumors. Quantitative analysis revealed that IOI could more effectively differentiate genuine neuronal activity from artifacts compared with ITI, which was occasionally prone to false positives in the presence of cortical abnormalities.

Conclusions

ITI and IOI produce comparable functional maps with moderate agreement in Cohen’s kappa values. Their distinct physiological mechanisms suggest complementary use in specific clinical scenarios, such as cortical tumors or impaired neurovascular coupling. IOI excels in spatial resolution and mapping reliability, whereas ITI provides additional insights into metabolic changes and tissue properties, especially in pathological areas. Combined, these modalities could enhance the understanding and analysis of functional and pathological processes in complex neurosurgical cases.

Wearable optical coherence tomography angiography probe with extended depth of field

Xiaochen Li, Xiangyu Guo, Xinyue Wang, Lingqi Jiang, Mingxin Li, Xiaochuan Dai, Qun Hao, Jingjing Zhao, Yong Huang, Liqun Sun

Journal of Biomedical Optics, Vol. 30, Issue 01, 016003, (January 2025) https://doi.org/10.1117/1.JBO.30.1.016003

TOPICS: Optical coherence tomography, Brain, Angiography, Neuroimaging, Diffractive optical elements, Depth of field, Image resolution, Biomedical optics, Signal to noise ratio, Animals

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Robust estimation of skin physiological parameters from hyperspectral images using Bayesian neural networks

Teo Manojlović, Tadej Tomanič, Ivan Štajduhar, Matija Milanič

Journal of Biomedical Optics, Vol. 30, Issue 01, 016004, (January 2025) https://doi.org/10.1117/1.JBO.30.1.016004

TOPICS: Artificial neural networks, Data modeling, Skin, RGB color model, Education and training, Hyperspectral imaging, Tissues, Nervous system, Performance modeling, In vivo imaging

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Optical coherence tomography–enabled classification of the human venoatrial junction

Arielle Joasil, Aidan Therien, Christine Hendon

Journal of Biomedical Optics, Vol. 30, Issue 01, 016005, (January 2025) https://doi.org/10.1117/1.JBO.30.1.016005

TOPICS: Optical coherence tomography, Photovoltaics, Atrial fibrillation, Deep learning, Veins, Machine learning, Lawrencium, Tissues, Radiofrequency ablation, Heart

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Low-cost microvascular phantom for photoacoustic imaging using loofah

Jinhua Xu, Yixiao Lin, Sanskar Thakur, Haolin Nie, Lukai Wang, Quing Zhu

Journal of Biomedical Optics, Vol. 30, Issue 01, 016006, (January 2025) https://doi.org/10.1117/1.JBO.30.1.016006

TOPICS: Photoacoustic imaging, Tissues, Photoacoustic spectroscopy, Imaging systems, Chromophores, Tumors, Photoacoustic microscopy, Optical properties, Biomedical optics, Fabrication

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Label-free imaging flow cytometry for cell classification based directly on multiple off-axis holographic projections

Dana Aharoni, Matan Dudaie, Itay Barnea, Natan Tzvi Shaked

Journal of Biomedical Optics, Vol. 30, Issue 01, 016007, (January 2025) https://doi.org/10.1117/1.JBO.30.1.016007

TOPICS: Holography, Flow cytometry, Holograms, Biological imaging, Digital holography, Biomedical optics, Education and training, Holographic interferometry, Imaging systems, Convolution

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Significance

Imaging flow cytometry allows highly informative multi-point cell analysis for biological assays and medical diagnosis. Rapid processing of the imaged cells during flow allows real-time classification and sorting of the cells. Off-axis holography enables imaging flow cytometry without chemical cell staining but requires digital processing to the optical path delay profile for each frame before the cells can be classified, which slows down the overall processing throughput. We present a method for real-time cell classification via label-free quantitative imaging flow cytometry using digital holography, offering a comprehensive representation of cellular structures, without the need for digital processing before automatic cell classification.

Aim

We aim to develop an automatic cell classification scheme based directly on the off-axis holographic projections of the cells during flow and test it for stain-free imaging flow cytometry of white blood cells.

Approach

After building a dedicated off-axis holographic microscopy system for acquiring white blood cells during flow, we apply deep-learning classification directly in the off-axis hologram space, rather than in the quantitative phase profile space. This way, we simplify computational processes and allow a significant increase in the cell classification throughput. In addition, by utilizing multiple-viewpoint holographic projections of the cells rotated during flow, instead of using a single projection, we obtain better classification results due to the additional cellular information gained.

Results

Our technique demonstrates increasing accuracy with additional viewpoint holographic projections from the optical system, achieving a 7.69% improvement when processing ten interferometric projections compared with a single interferometric projection (regular off-axis hologram). Our technique also outperforms using multiple optical path delay profile projections, requiring off-axis holographic digital preprocessing, by 17.95%, because the holographic projections are analyzed directly without preprocessing and includes the amplitude information as well.

Conclusions

Our cell classification approach has great potential for high-throughput, high-content, label-free imaging flow cytometry for classification of large-scale cellular datasets and real-time cell classification during flow in clinical settings.

Therapeutic

Photosensitizer spatial heterogeneity and its impact on personalized interstitial photodynamic therapy treatment planning

Tina Saeidi, Shuran Wang, Hector Contreras, Michael Daly, Vaughn Betz, Lothar Lilge

Journal of Biomedical Optics, Vol. 30, Issue 01, 018001, (January 2025) https://doi.org/10.1117/1.JBO.30.1.018001

TOPICS: Picosecond phenomena, Tumors, Photodynamic therapy, Tissues, Spatial resolution, Animals, Brain, Animal model studies, Spherical lenses, Statistical analysis

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