Volume 29 Issue 7 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 29 · NO. 7 | July 2024

CONTENTS

IN THIS ISSUE

Review Papers (1)

General (1)

Imaging (8)

Microscopy (2)

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Review Papers

Challenges and advances in two-dimensional photoacoustic computed tomography: a review

Shunyao Zhang, Jingyi Miao, Lei S. Li

Journal of Biomedical Optics, Vol. 29, Issue 07, 070901, (July 2024) https://doi.org/10.1117/1.JBO.29.7.070901

TOPICS: Image restoration, Photoacoustic tomography, Acoustics, Tissues, Aliasing, Transducers, 3D image reconstruction, In vivo imaging, Image resolution, Photoacoustic spectroscopy

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General

Depolarization diagrams for circularly polarized light scattering for biological particle monitoring

Nozomi Nishizawa, Asato Esumi, Yukito Ganko

Journal of Biomedical Optics, Vol. 29, Issue 07, 075001, (June 2024) https://doi.org/10.1117/1.JBO.29.7.075001

TOPICS: Depolarization, Particles, Light scattering, Scattered light, Polarization, Scattering, Tissues, Reflection, Mie scattering, Refractive index

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Imaging

Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity

Behrouz Ebrahimi, David Le, Mansour Abtahi, Albert Dadzie, Alfa Rossi, Mojtaba Rahimi, Taeyoon Son, Susan Ostmo, J. Peter Campbell, R. V. Paul Chan, Xincheng Yao

Journal of Biomedical Optics, Vol. 29, Issue 07, 076001, (June 2024) https://doi.org/10.1117/1.JBO.29.7.076001

TOPICS: Deep learning, Image classification, Color, Photography, Data modeling, Image fusion, Education and training, Performance modeling, Light sources and illumination, Multispectral imaging

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Compressed intracellular motility via non-uniform temporal sampling in dynamic optical coherence tomography

Amy L. Oldenburg, Pan Ji, Xiao Yu, Lin Yang

Journal of Biomedical Optics, Vol. 29, Issue 07, 076002, (July 2024) https://doi.org/10.1117/1.JBO.29.7.076002

TOPICS: Optical coherence tomography, Autocorrelation, Simulations, Statistical analysis, Nonuniform sampling, Image compression, Biological imaging, Signal to noise ratio, Signal processing, Monte Carlo methods

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Non-contact elasticity contrast imaging using photon counting

Zipei Zheng, Yong Meng Sua, Shenyu Zhu, Patrick Rehain, Yu-Ping Huang

Journal of Biomedical Optics, Vol. 29, Issue 07, 076003, (July 2024) https://doi.org/10.1117/1.JBO.29.7.076003

TOPICS: Elasticity, Photon counting, Elastography, Tissues, Image restoration, Acoustic waves, Picosecond phenomena, Signal detection, Pulse signals, Vibration

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Deep learning-enabled high-speed, multi-parameter diffuse optical tomography

Robin Dale, Biao Zheng, Felipe Orihuela-Espina, Nicholas Ross, Thomas O'Sullivan, Scott Howard, Hamid Dehghani

Journal of Biomedical Optics, Vol. 29, Issue 07, 076004, (July 2024) https://doi.org/10.1117/1.JBO.29.7.076004

TOPICS: Education and training, Data modeling, Optical properties, Finite element methods, Scattering, 3D modeling, Reconstruction algorithms, Tissues, Absorption, Voxels

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Convolutional neural network advances in demosaicing for fluorescent cancer imaging with color–near-infrared sensors

Yifei Jin, Borislav Kondov, Goran Kondov, Sunil Singhal, Shuming Nie, Viktor Gruev

Journal of Biomedical Optics, Vol. 29, Issue 07, 076005, (July 2024) https://doi.org/10.1117/1.JBO.29.7.076005

TOPICS: Near infrared, Interpolation, Image sensors, Optical filters, Tunable filters, Image restoration, Image filtering, Visible radiation, Education and training, Convolutional neural networks

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Optimization of handheld spectrally encoded coherence tomography and reflectometry for point-of-care ophthalmic diagnostic imaging

Jacob Watson, Rachel Hecht, Yuankai Tao

Journal of Biomedical Optics, Vol. 29, Issue 07, 076006, (July 2024) https://doi.org/10.1117/1.JBO.29.7.076006

TOPICS: Optical coherence tomography, Head, Imaging systems, Optical alignment, Aluminum, Point-of-care devices, Relays, Finite element methods, Photography, Design

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Ultrasound and diffuse optical tomography-transformer model for assessing pathological complete response to neoadjuvant chemotherapy in breast cancer

Yun Zou, Minghao Xue, Md Iqbal Hossain, Quing Zhu

Journal of Biomedical Optics, Vol. 29, Issue 07, 076007, (July 2024) https://doi.org/10.1117/1.JBO.29.7.076007

TOPICS: Tumors, Tumor growth modeling, Data modeling, Performance modeling, Breast cancer, Diffuse optical tomography, Ultrasonography, Optical tomography, Acoustic tomography, Transformers

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Significance

We evaluate the efficiency of integrating ultrasound (US) and diffuse optical tomography (DOT) images for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. The ultrasound-diffuse optical tomography (USDOT)-Transformer model represents a significant step toward accurate prediction of pCR, which is critical for personalized treatment planning.

Aim

We aim to develop and assess the performance of the USDOT-Transformer model, which combines US and DOT images with tumor receptor biomarkers to predict the pCR of breast cancer patients under NAC.

Approach

We developed the USDOT-Transformer model using a dual-input transformer to process co-registered US and DOT images along with tumor receptor biomarkers. Our dataset comprised imaging data from 60 patients at multiple time points during their chemotherapy treatment. We used fivefold cross-validation to assess the model’s performance, comparing its results against a single modality of US or DOT.

Results

The USDOT-Transformer model demonstrated excellent predictive performance, with a mean area under the receiving characteristic curve of 0.96 (95%CI: 0.93 to 0.99) across the fivefold cross-validation. The integration of US and DOT images significantly enhanced the model’s ability to predict pCR, outperforming models that relied on a single imaging modality (0.87 for US and 0.82 for DOT). This performance indicates the potential of advanced deep learning techniques and multimodal imaging data for improving the accuracy (ACC) of pCR prediction.

Conclusion

The USDOT-Transformer model offers a promising non-invasive approach for predicting pCR to NAC in breast cancer patients. By leveraging the structural and functional information from US and DOT images, the model offers a faster and more reliable tool for personalized treatment planning. Future work will focus on expanding the dataset and refining the model to further improve its accuracy and generalizability.

iStent insertion orientation and impact on trabecular meshwork motion resolved by optical coherence tomography imaging

Zhaoyu Gong, Murray Johnstone, Ruikang Wang

Journal of Biomedical Optics, Vol. 29, Issue 07, 076008, (July 2024) https://doi.org/10.1117/1.JBO.29.7.076008

TOPICS: Optical coherence tomography, Image segmentation, Tissues, 3D image processing, Structural imaging, Glaucoma, Biomechanics, 3D metrology, Iris, Image processing

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Microscopy

NerveTracker: a Python-based software toolkit for visualizing and tracking groups of nerve fibers in serial block-face microscopy with ultraviolet surface excitation images

Chaitanya Kolluru, Naomi Joseph, James Seckler, Farzad Fereidouni, Richard Levenson, Andrew Shoffstall, Michael Jenkins, David Wilson

Journal of Biomedical Optics, Vol. 29, Issue 07, 076501, (June 2024) https://doi.org/10.1117/1.JBO.29.7.076501

TOPICS: Nerve, Visualization, Image segmentation, Microscopy, Video, 3D applications, Structured optical fibers, Ultraviolet radiation, Imaging systems, Image resolution

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Significance

Information about the spatial organization of fibers within a nerve is crucial to our understanding of nerve anatomy and its response to neuromodulation therapies. A serial block-face microscopy method [three-dimensional microscopy with ultraviolet surface excitation (3D-MUSE)] has been developed to image nerves over extended depths ex vivo. To routinely visualize and track nerve fibers in these datasets, a dedicated and customizable software tool is required.

Aim

Our objective was to develop custom software that includes image processing and visualization methods to perform microscopic tractography along the length of a peripheral nerve sample.

Approach

We modified common computer vision algorithms (optic flow and structure tensor) to track groups of peripheral nerve fibers along the length of the nerve. Interactive streamline visualization and manual editing tools are provided. Optionally, deep learning segmentation of fascicles (fiber bundles) can be applied to constrain the tracts from inadvertently crossing into the epineurium. As an example, we performed tractography on vagus and tibial nerve datasets and assessed accuracy by comparing the resulting nerve tracts with segmentations of fascicles as they split and merge with each other in the nerve sample stack.

Results

We found that a normalized Dice overlap (Dicenorm) metric had a mean value above 0.75 across several millimeters along the nerve. We also found that the tractograms were robust to changes in certain image properties (e.g., downsampling in-plane and out-of-plane), which resulted in only a 2% to 9% change to the mean Dicenorm values. In a vagus nerve sample, tractography allowed us to readily identify that subsets of fibers from four distinct fascicles merge into a single fascicle as we move ∼5 mm along the nerve’s length.

Conclusions

Overall, we demonstrated the feasibility of performing automated microscopic tractography on 3D-MUSE datasets of peripheral nerves. The software should be applicable to other imaging approaches. The code is available at https://github.com/ckolluru/NerveTracker.

Single-beam digital holographic reconstruction: a phase-support enhanced complex wavefront on phase-only function for twin-image elimination

Charlotte Kyeremah, Matthew Weiss, Dila Kandel, Daniel Haehn, Chandra Yelleswarapu

Journal of Biomedical Optics, Vol. 29, Issue 07, 076502, (July 2024) https://doi.org/10.1117/1.JBO.29.7.076502

TOPICS: 3D image reconstruction, Digital holography, Holography, Phase reconstruction, Holograms, Image restoration, Reconstruction algorithms, Tunable filters, Image processing, Wavefronts

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