Volume 27 Issue 10 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 27 · NO. 10 | October 2022

CONTENTS

IN THIS ISSUE

JBO Letters (1)

General (6)

Imaging (7)

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JBO Letters

Detection and differentiation of semi-transparent materials simulating biological structures using optical coherence tomography: a phantom study

Muhammad Mohsin Qureshi, Nader Allam, Taylor Peters, Valentin Demidov, I. Alex Vitkin

Journal of Biomedical Optics, Vol. 27, Issue 10, 100501, (October 2022) https://doi.org/10.1117/1.JBO.27.10.100501

TOPICS: Lymphatic system, Optical coherence tomography, Statistical analysis, Solids, Cancer, Tissues, Speckle, In vivo imaging, Water, Transparency

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General

Spectral imaging of normal, hydrated, and desiccated porcine skin using polarized light

Ben Urban, Steven Jacques, Hrebesh Subhash

Journal of Biomedical Optics, Vol. 27, Issue 10, 105001, (October 2022) https://doi.org/10.1117/1.JBO.27.10.105001

TOPICS: Tissues, Reflectivity, Skin, Tissue optics, Imaging spectroscopy, Light emitting diodes, Cameras, Scattering, Collagen, Polarization

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Water and lipid content of breast tissue measured by six-wavelength time-domain diffuse optical spectroscopy

Hiroko Wada, Nobuko Yoshizawa, Etsuko Ohmae, Yukio Ueda, Kenji Yoshimoto, Tetsuya Mimura, Hatsuko Nasu, Yuko Asano, Hiroyuki Ogura, Harumi Sakahara, Satoshi Goshima

Journal of Biomedical Optics, Vol. 27, Issue 10, 105002, (October 2022) https://doi.org/10.1117/1.JBO.27.10.105002

TOPICS: Breast, Tissues, Chest, Tissue optics, Mammary gland, Breast cancer, Reflectivity, Photonics, Diffuse optical spectroscopy, Mammography

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Highly accurate, automated quantification of 2D/3D orientation for cerebrovasculature using window optimizing method

Jia Meng, Lingxi Zhou, Shuhao Qian, Chuncheng Wang, Zhe Feng, Shenyi Jiang, Rushan Jiang, Zhihua Ding, Jun Qian, Shuangmu Zhuo, Zhiyi Liu

Journal of Biomedical Optics, Vol. 27, Issue 10, 105003, (October 2022) https://doi.org/10.1117/1.JBO.27.10.105003

TOPICS: Blood vessels, 3D image processing, Imaging systems, Brain, Neuroimaging, In vivo imaging, Optical simulations, Structured optical fibers, Visualization, Neuroscience

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Demonstration of an optical dentin hardness measuring device using bovine dentin with different demineralization times

Sota Kondo, Hisanao Hazama, Yutaka Tomioka, Atsushi Mine, Satoshi Yamaguchi, Saeko Okumura, Hiroaki Tanimoto, Kenzo Yasuo, Kazushi Yoshikawa, Kazuyo Yamamoto, Kunio Awazu

Journal of Biomedical Optics, Vol. 27, Issue 10, 105004, (October 2022) https://doi.org/10.1117/1.JBO.27.10.105004

TOPICS: Dental caries, Teeth, Measurement devices, In vivo imaging, Sensors, Glasses, Calibration, Light emitting diodes, Electronics, CMOS cameras

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Significance

The increase in root caries is a serious problem as society ages. Root caries is diagnosed by inspection and palpation, which are qualitative. A method to objectively and quantitatively evaluate the progress of root caries in a clinical setting is strongly desired. The root caries could be diagnosed by measuring hardness because dentin becomes softer as the caries progresses. Vickers hardness has been customarily used as an indicator of tooth hardness. However, this method cannot be used to in vivo teeth because the teeth must be dried prior to measurement to make the indentation. A hardness meter using an indenter with light for tooth monitoring (HAMILTOM) is proposed as an optical device. HAMILTOM could measure hardness of teeth in wet condition as a dark area while applying a load to dentins without drying. Therefore, HAMILTOM may realize hardness measurements of in vivo teeth in a clinical setting quantitatively.

Aim

The aim of our study is to demonstrate the optical dentin hardness measuring device HAMILTOM using bovine dentin with different demineralization times and to evaluate the correlation between the dark areas measured by HAMILTOM and the Vickers hardness measured by the Vickers hardness tester.

Approach

The samples were 20 bovine dentins. They were demineralized by a lactic acid solution with different times and divided into groups 1 and 2 of 10 samples each. In both groups, the dark areas and Vickers hardness were measured for each sample. Group 1 was used to obtain a calibration curve to calculate Vickers hardness from the dark area. Group 2 was used to validate the calibration curve obtained from the dentin samples of group 1.

Results

The areas appearing black without a total internal reflection of the indenter measured by HAMILTOM increased as the demineralization time increased. Additionally, the Vickers hardness of group 2 calculated by the dark areas of group 2 and the calibration curve obtained in group 1 and the Vickers hardness of group 2 measured by the Vickers hardness tester were strongly correlated with a determination coefficient of 0.99.

Conclusions

The results demonstrate that HAMILTOM may be a suitable alternative to the conventional method. Unlike the conventional method, which cannot be used for in vivo teeth, HAMILTOM holds potential to quantitatively evaluate the progress of caries in in vivo teeth.

Quantitative spatial mapping of tissue water and lipid content using spatial frequency domain imaging in the 900- to 1000-nm wavelength region

Bowen Song, Xinman Yin, Yubo Fan, Yanyu Zhao

Journal of Biomedical Optics, Vol. 27, Issue 10, 105005, (October 2022) https://doi.org/10.1117/1.JBO.27.10.105005

TOPICS: Tissues, Spatial frequencies, Absorption, Cameras, Tissue optics, In vivo imaging, Silicon, Short wave infrared radiation, Sensors, Optical properties

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In vivo spectroscopic evaluation of human tissue optical properties and hemodynamics during HPPH-mediated photodynamic therapy of pleural malignancies

Ryan D. Hall Morales, Yi Hong Ong, Jarod Finlay, Andreea Dimofte, Charles Simone II, Joseph Friedberg, Theresa Busch, Keith Cengel, Timothy Zhu

Journal of Biomedical Optics, Vol. 27, Issue 10, 105006, (October 2022) https://doi.org/10.1117/1.JBO.27.10.105006

TOPICS: Tissue optics, Optical properties, Tissues, Photodynamic therapy, Spectroscopy, In vivo imaging, Hemodynamics, Tumors, Diffuse reflectance spectroscopy, Absorption

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Imaging

Influence of edge enhancement applied in endoscopic systems on sharpness and noise

Geert Geleijnse, Bernd Rieger

Journal of Biomedical Optics, Vol. 27, Issue 10, 106001, (October 2022) https://doi.org/10.1117/1.JBO.27.10.106001

TOPICS: Image enhancement, Endoscopes, Video processing, Modulation transfer functions, Endoscopy, Video, Signal to noise ratio, Image processing, Image quality, Image sharpness

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Significance

Flexible endoscopes are essential for medical internal examinations. Digital endoscopes are connected to a video processor that can apply various operations to enhance the image. One of those operations is edge enhancement, which has a major impact on the perceived image quality by medical professionals. However, the specific methods and parameters of this operation are undisclosed and the arbitrary units to express the level of edge enhancement differ per video processor.

Aim

Objectively quantify the level of edge enhancement from the recorded images alone, and measure the effect on sharpness and noise.

Approach

Edge enhancement was studied in four types of flexible digital ear nose and throat endoscopes. Measurements were performed using slanted edges and gray patches. The level of edge enhancement was determined by subtracting the step response of an image without edge enhancement from images with selected settings of edge enhancement and measuring the resulting peak-to-peak differences. These values were then normalized by the step size. Sharpness was characterized by observing the normalized modulation transfer function (MTF) and computing the spatial frequency at 50% MTF. The noise was measured on the gray patches and computed as a weighted sum of variances from the luminance and two chrominance channels of the pixel values.

Results

The measured levels were consistent with the level set via the user interface on the video processor and varied typically from 0 to 1.3. Both sharpness and noise increase with larger levels of edge enhancement with factors of 3 and 4 respectively.

Conclusions

The presented method overcomes the issue of vendors expressing the level of edge enhancement each differently in arbitrary units. This allows us to compare the effects, and we can start exploring the relationship with the subjectively perceived image quality by medical professionals to find substantiated optimal settings.

Three-dimensional imaging of biological cells using surface plasmon coupled emission

Anik Mazumder, Mohammad Mozammal, Muhammad Anisuzzaman Talukder

Journal of Biomedical Optics, Vol. 27, Issue 10, 106002, (October 2022) https://doi.org/10.1117/1.JBO.27.10.106002

TOPICS: 3D image processing, Surface plasmons, Finite-difference time-domain method, Metals, Near field, Silver, Rhodamine B, Stereoscopy, Prisms, Interfaces

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Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

Mark Witteveen, Henricus J. Sterenborg, Ton van Leeuwen, Maurice C. G. Aalders, Theo J. Ruers, Anouk L. Post

Journal of Biomedical Optics, Vol. 27, Issue 10, 106003, (October 2022) https://doi.org/10.1117/1.JBO.27.10.106003

TOPICS: Tissues, Algorithm development, Tissue optics, Breast, Hyperspectral imaging, Tumors, Natural surfaces, Reflectivity, Optical properties, Colon

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Significance

Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.

Aim

To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.

Approach

We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min–max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope—while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.

Conclusions

Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min–max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable.

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging

Arumugaraj Madasamy, Vipul Gujrati, Vasilis Ntziachristos, Jaya Prakash

Journal of Biomedical Optics, Vol. 27, Issue 10, 106004, (October 2022) https://doi.org/10.1117/1.JBO.27.10.106004

TOPICS: 3D modeling, Data modeling, Tissue optics, Absorption, Signal to noise ratio, Inverse optics, Image quality, Gallium nitride, Breast, Acoustics

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Protoporphyrin IX delayed fluorescence imaging: a modality for hypoxia-based surgical guidance

Arthur Pétusseau, Petr Bruza, Brian Pogue

Journal of Biomedical Optics, Vol. 27, Issue 10, 106005, (October 2022) https://doi.org/10.1117/1.JBO.27.10.106005

TOPICS: Tissues, Tumors, Oxygen, Hypoxia, Luminescence, Cameras, Surgery, Molecules, In vivo imaging, Skin

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Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning

Jose Rico-Jimenez, Javier Jo

Journal of Biomedical Optics, Vol. 27, Issue 10, 106006, (October 2022) https://doi.org/10.1117/1.JBO.27.10.106006

TOPICS: Optical coherence tomography, Visualization, Data modeling, Databases, Tissues, Arteries, Coherence imaging, Image classification, Speckle, Neural networks

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Hyperspectral imaging for tumor segmentation on pigmented skin lesions

Eleni Aloupogianni, Takaya Ichimura, Mei Hamada, Masahiro Ishikawa, Takuo Murakami, Atsushi Sasaki, Koichiro Nakamura, Naoki Kobayashi, Takashi Obi

Journal of Biomedical Optics, Vol. 27, Issue 10, 106007, (October 2022) https://doi.org/10.1117/1.JBO.27.10.106007

TOPICS: Tumors, Tissues, Skin, Image segmentation, Pathology, Data modeling, Tumor growth modeling, 3D modeling, Hyperspectral imaging, Convolution

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