Volume 1 Issue 2 | Biophotonics Discovery

Biophotonics Discovery

VOL. 1 · NO. 2 | July 2024

CONTENTS

IN THIS ISSUE

Review Paper (1)

Research (4)

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Review Paper

Review of Cherenkov imaging technology advances in radiotherapy: single-photon-level imaging in high ambient light and radiation backgrounds

Aubrey Parks, Jeremy Hallett, Alexander Niver, Rongxiao Zhang, Petr Bruza, Brian Pogue

Biophotonics Discovery, Vol. 1, Issue 02, 020901, (July 2024) https://doi.org/10.1117/1.BIOS.1.2.020901

TOPICS: Radiotherapy, Cameras, Tissues, Scintillators, Signal detection, Interference (communication), Tunable filters, Optical filters, Imaging technologies, Biomedical optics

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Significance

Single-photon-level imaging has been utilized for decades in closed dark environments; however, the utility for macroscopic imaging is more limited because it involves time-gating, filtering, and processing to view signals of interest. In radiation therapy delivery, a low-level signal called Cherenkov emission occurs from patients’ bodies, which is imaged with single-photon level sensitivity, mapping radiation dose deposition in tissue. Several key technological advances have been leveraged to make this extremely low-light signal overcome high background and noise in clinical settings.

Aim

Our review summarizes specific technological advances that have led to a single-photon imaging in high radiation noise and high optical background environments possible. Our work discusses applications and future opportunities.

Approach

Physical fundamentals of Cherenkov light, ambient room light, optical filtering, time-gating, and image processing are reviewed with key technological camera choices. This is followed by discussion of image quality, noise, and postprocessing, with current and future applications.

Results

Invention and optimization of time-gating techniques and cameras with a single-photon capability were required to achieve real-time Cherenkov imaging. Requirements of video frame rate (≈10 to 30 fps), fast triggering (≈μs), clinically relevant spatial resolution (≈mm), single-photon/pixel sensitivity, and large field of view all led to intensified complementary metal-oxide-semiconductor cameras. Additional innovations in wavelength filtering, lens choices, and spatial and temporal postprocessing have allowed imaging that is not overwhelmed by ambient radiation noise or room lights. The current use provides real-time visualization of external beam radiotherapy on patient’s skin. Several emerging research areas may improve image quality and provide additional capabilities in biochemical sensing and quantification of delivery.

Conclusion

The technical inventions and discoveries on how this light signal is sampled have led to real-time beam observation for dose delivery verification in settings where single-photon sensitive imaging is seemingly implausible while also opening the door to additional research applications.

Research

Computer-assisted discrimination of cancerous and pre-cancerous from benign oral lesions based on multispectral autofluorescence imaging endoscopy

Elvis de Jesus Duran Sierra, Shuna Cheng, Rodrigo Cuenca Martinez, Beena Ahmed, Jim Ji, Vladislav Yakovlev, Mathias Martinez, Moustafa Al-Khalil, Hussain Al-Enazi, Carlos Busso, Javier Jo

Biophotonics Discovery, Vol. 1, Issue 02, 025001, (July 2024) https://doi.org/10.1117/1.BIOS.1.2.025001

TOPICS: Autofluorescence, Tumor growth modeling, Multispectral imaging, Endoscopy, Cancer, Image classification, Autofluorescence imaging, Cancer detection, Tissues, Diagnostics

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Significance

Diagnosis of cancerous and pre-cancerous oral lesions at early stages is critical for the improvement of patient care, to increase survival rates and minimize the invasiveness of tumor resection surgery. Unfortunately, oral precancerous and early-stage cancerous lesions are often difficult to distinguish from oral benign lesions with the existing diagnostic tools used during standard clinical oral examination. In consequence, early diagnosis of oral cancer can be achieved in only about 30% of patients. Therefore, clinical diagnostic technologies for fast, minimally invasive, and accurate oral cancer screening are urgently needed.

Aim

This study investigated the use of multispectral autofluorescence imaging endoscopy for the automated and noninvasive discrimination of cancerous and precancerous from benign oral epithelial lesions.

Approach

In vivo multispectral autofluorescence endoscopic images of clinically suspicious oral lesions were acquired from 67 patients undergoing tissue biopsy examination. The imaged lesions were classified as precancerous (n=4), cancerous (n=29), and benign (n=34) lesions based on histopathology diagnosis. Multispectral autofluorescence intensity feature maps were generated for each oral lesion and used to train and optimize support vector machine (SVM) models for automated discrimination of cancerous and precancerous from benign oral lesions.

Results

After a leave-one-patient-out cross-validation strategy, an optimized SVM model developed with four multispectral autofluorescence features yielded levels of sensitivity and specificity of 85% and 71%, respectively and overall accuracy of 78% in the discrimination of cancerous/precancerous versus benign oral lesions.

Conclusion

This study demonstrates the potentials of a computer-assisted detection system based on multispectral autofluorescence imaging endoscopy for the early detection of cancerous and precancerous oral lesions.

In vivo spectroscopy to concurrently characterize five metabolic and vascular endpoints relevant to aggressive breast cancer

Victoria D’Agostino, Riley Deutsch, Michelle Kwan, Enakshi Sunassee, Megan Madonna, Gregory Palmer, Brian Crouch, Nirmala Ramanujam

Biophotonics Discovery, Vol. 1, Issue 02, 025002, (July 2024) https://doi.org/10.1117/1.BIOS.1.2.025002

TOPICS: Tumors, Fluorophores, Tissues, In vivo imaging, Monte Carlo methods, Spectroscopy, Fluorescence, Crosstalk, Biomedical optics, Glucose

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Predictive model for laser-induced tissue necrosis with immunohistochemistry validation

J. Junior Arroyo, Arunima Sharma, Jiaxin Zhang, Muyinatu A. Lediju Bell

Biophotonics Discovery, Vol. 1, Issue 02, 025003, (August 2024) https://doi.org/10.1117/1.BIOS.1.2.025003

TOPICS: Tissues, Liver, Laser irradiation, Biological samples, Laser energy, Image segmentation, Optical simulations, Monte Carlo methods, Laser applications, Biomedical optics

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Significance

Photoacoustic imaging holds promise to provide critical guidance in surgical interventions, but its widespread use is challenged by the absence of applicable safety guidelines across diverse target tissues. The biosafety of this technology is primarily associated with the risk of necrosis generation, which is an irreversible thermal effect that can result from prolonged, high-energy laser applications.

Aim

We introduce the first known numerical simulation approaches to assess laser-induced necrosis in liver tissue and present a novel microscopy analysis framework to validate performance.

Approach

Our simulation methods integrate Monte Carlo simulations of laser-tissue interaction with the COMSOL interface, model local tissue heating, and predict associated tissue damage to quantify the percentage of tissue necrosis resulting from laser application. Our initial predictions are based on 30 and 73 mJ mean laser energies, laser irradiation times of 1, 10, and 20 min, and a 750 nm laser wavelength. Empirical validations with in vivo porcine liver exposed to a mean laser energy of 73 mJ and 750 nm laser wavelength were performed based on H&E and cleaved Caspase-3 immunohistochemistry (IHC) results. Simulation results from the lower 30 mJ laser energy were additionally cross-referenced with previous qualitative H&E-based reports.

Results

Negligible tissue damage was observed with necrosis predictions ≤15.05%, damage thresholds were determined to be within the 15.05% to 66.23% necrosis prediction range, and necrosis predictions deviated from quantitative IHC results by 0.01% to 8.1%.

Conclusions

We successfully demonstrated an in silico alternative to the otherwise time-consuming and expensive empirical assessments that would be required to create tissue-specific laser safety guidelines. The presented methods have the potential to be translated to multiple tissues and additional laser properties.

Enhanced peripheral tissue oxygenation and hemoglobin concentration after a high-fat meal measured with spatial frequency domain imaging

Anahita Pilvar, Jorge Plutzky, Darren Roblyer

Biophotonics Discovery, Vol. 1, Issue 02, 025004, (September 2024) https://doi.org/10.1117/1.BIOS.1.2.025004

TOPICS: Tissues, Blood, Blood pressure, Optical properties, Oxygenation, Hemodynamics, Glucose, Biomedical optics, Heart, Scattering

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Significance

The magnitude and temporal dynamics of changes in blood nutrient and lipid levels following a high-fat meal have been previously shown to be an important indicator of current and future cardiovascular health and disease. Measurement of circulating nutrients and lipids currently requires invasive blood draws. The development of a non-invasive method for continuous monitoring of postprandial (i.e., after-meal) changes may assist in enhancing cardiovascular health management, dietary monitoring, and identification of disease-promoting factors. Spatial frequency domain imaging (SFDI) is a non-contact, label-free optical technique that can quantify tissue optical properties and hemodynamics in vivo. We hypothesized that SFDI may track the postprandial state in peripheral tissue.

Aim

We aim to investigate the relationship between postprandial factors, namely triglycerides and glucose, and the optical properties and oxygenation of peripheral tissue measured with SFDI.

Approach

Fifteen healthy volunteers consumed both a low- (2 g) and high- (60 g) fat meal on different days. A custom SFDI device was used to measure the dorsal hand surface of volunteers before the meal and each hour for 5 h after the meal. Measurements were taken at 730, 880, and 1100 nm. Longitudinal postprandial changes in tissue optical properties were correlated with changes in blood triglycerides and glucose levels as well as blood pressure, heart rate, and room temperature. A machine-learning model was trained to estimate triglyceride levels from SFDI metrics.

Results

Several SFDI metrics increased and peaked 3 to 4 h following the high-fat meal, including tissue oxygen saturation (StO2) and oxyhemoglobin (HbO2) concentration, and were substantially different from the low-fat cohort (p<0.05 at 3 h). The increases were large, >5% for StO2 and >10% for HbO2 concentration on average. The temporal changes in these metrics broadly tracked triglyceride levels, which peaked at 3 h post-meal. The predictive model accurately estimated blood triglyceride levels (RMSE 40 mg/dL).

Conclusion

These findings suggest that SFDI could serve as a powerful non-invasive tool to monitor postprandial hemodynamics. In the future, SFDI measurements may help enhance cardiovascular disease prediction and management.

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