Volume 29 Issue 9 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 29 · NO. 9 | September 2024

CONTENTS

IN THIS ISSUE

Special Section on Hyperspectral Imaging, Part 1 (10)

General (3)

Imaging (3)

Sensing (1)

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Special Section on Hyperspectral Imaging, Part 1

Effects of phantom microstructure on their optical properties

Jošt Stergar, Rok Hren, Matija Milanič

Journal of Biomedical Optics, Vol. 29, Issue 09, 093502, (May 2024) https://doi.org/10.1117/1.JBO.29.9.093502

TOPICS: Optical spheres, Scattering, Optical properties, Mie scattering, Absorption, Tissues, Microspheres, Reflectivity, Glasses, Transmittance

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Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery

Jeeseong Hwang, Philip Cheney, Stephen Kanick, Hanh N. Le, David McClatchy III, Helen Zhang, Nian Liu, Zhan-Qian John Lu, Tae Joon Cho, Kimberly Briggman, David Allen, Wendy Wells, Brian Pogue

Journal of Biomedical Optics, Vol. 29, Issue 09, 093503, (May 2024) https://doi.org/10.1117/1.JBO.29.9.093503

TOPICS: Tissues, Biological samples, Image segmentation, Tumors, Breast, Microscopy, Printed circuit board testing, Blood, Library classification systems, Surgery

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Significance

Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries.

Aim

We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples.

Approach

Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes.

Results

The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas’ unique endmembers produced by the two methods agree with each other within <2% residual error margin.

Conclusions

Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas’ unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.

Hyperspectral imaging of 4T1 mammary carcinomas grown in dorsal skinfold window chambers: spectral renormalization and fluorescence modeling

Tadej Tomanic, Tim Bozic, Bostjan Markelc, Jost Stergar, Gregor Sersa, Matija Milanic

Journal of Biomedical Optics, Vol. 29, Issue 09, 093504, (July 2024) https://doi.org/10.1117/1.JBO.29.9.093504

TOPICS: Tumors, Tissues, Hyperspectral imaging, Tumor growth modeling, Glasses, Skin, Reflectivity, Fluorescence, Image segmentation, Imaging systems

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Significance

Hyperspectral imaging (HSI) of murine tumor models grown in dorsal skinfold window chambers (DSWCs) offers invaluable insight into the tumor microenvironment. However, light loss in a glass coverslip is often overlooked, and particular tissue characteristics are improperly modeled, leading to errors in tissue properties extracted from hyperspectral images.

Aim

We highlight the significance of spectral renormalization in HSI of DSWC models and demonstrate the benefit of incorporating enhanced green fluorescent protein (EGFP) excitation and emission in the skin tissue model for tumors expressing genes to produce EGFP.

Approach

We employed an HSI system for intravital imaging of mice with 4T1 mammary carcinoma in a DSWC over 14 days. We performed spectral renormalization of hyperspectral images based on the measured reflectance spectra of glass coverslips and utilized an inverse adding–doubling (IAD) algorithm with a two-layer murine skin model, to extract tissue parameters, such as total hemoglobin concentration and tissue oxygenation (StO2). The model was upgraded to consider EGFP fluorescence excitation and emission. Moreover, we conducted additional experiments involving tissue phantoms, human forearm skin imaging, and numerical simulations.

Results

Hyperspectral image renormalization and the addition of EGFP fluorescence in the murine skin model reduced the mean absolute percentage errors (MAPEs) of fitted and measured spectra by up to 10% in tissue phantoms, 0.55% to 1.5% in the human forearm experiment and numerical simulations, and up to 0.7% in 4T1 tumors. Similarly, the MAPEs for tissue parameters extracted by IAD were reduced by up to 3% in human forearms and numerical simulations. For some parameters, statistically significant differences (p<0.05) were observed in 4T1 tumors. Ultimately, we have shown that fluorescence emission could be helpful for 4T1 tumor segmentation.

Conclusions

The results contribute to improving intravital monitoring of DWSC models using HSI and pave the way for more accurate and precise quantitative imaging.

Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks

Minh Ha Tran, Ling Ma, Hasan Mubarak, Ofelia Gomez, James Yu, Michelle Bryarly, Baowei Fei

Journal of Biomedical Optics, Vol. 29, Issue 09, 093505, (July 2024) https://doi.org/10.1117/1.JBO.29.9.093505

TOPICS: Thyroid, Education and training, RGB color model, Hyperspectral imaging, Cancer detection, Cancer, Transformers, Machine learning, Image classification, Visualization

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Significance

Hyperspectral imaging (HSI) is an emerging imaging modality for oncological applications and can improve cancer detection with digital pathology.

Aim

The study aims to highlight the increased accuracy and sensitivity of detecting the margin of thyroid carcinoma in hematoxylin and eosin (H&E)-stained histological slides using HSI and data augmentation methods.

Approach

Using an automated microscopic imaging system, we captured 2599 hyperspectral images from 65 H&E-stained human thyroid slides. Images were then preprocessed into 153,906 image patches of dimension 250×250×84 pixels. We modified the TimeSformer network architecture, which used alternating spectral attention and spatial attention layers. We implemented several data augmentation methods for HSI based on the RandAugment algorithm. We compared the performances of TimeSformer on HSI against the performances of pretrained ConvNext and pretrained vision transformers (ViT) networks on red, green, and blue (RGB) images. Finally, we applied attention unrolling techniques on the trained TimeSformer network to identify the biological features to which the network paid attention.

Results

In the testing dataset, TimeSformer achieved an accuracy of 90.87%, a weighted F1 score of 89.79%, a sensitivity of 91.50%, and an area under the receiving operator characteristic curve (AU-ROC) score of 97.04%. Additionally, TimeSformer produced thyroid carcinoma tumor margins with an average Jaccard score of 0.76 mm. Without data augmentation, TimeSformer achieved an accuracy of 88.23%, a weighted F1 score of 86.46%, a sensitivity of 85.53%, and an AU-ROC score of 94.94%. In comparison, the ViT network achieved an 89.98% accuracy, an 88.14% weighted F1 score, an 84.77% sensitivity, and a 96.17% AU-ROC. Our visualization results showed that the network paid attention to biological features.

Conclusions

The TimeSformer model trained with hyperspectral histological data consistently outperformed conventional RGB-based models, highlighting the superiority of HSI in this context. Our proposed augmentation methods improved the accuracy, the F1 score, and the sensitivity score.

Design and validation of a high-speed hyperspectral laparoscopic imaging system

Kelden Pruitt, Ling Ma, Armand Rathgeb, Jeffrey Gahan, Brett Johnson, Douglas Strand, Baowei Fei

Journal of Biomedical Optics, Vol. 29, Issue 09, 093506, (August 2024) https://doi.org/10.1117/1.JBO.29.9.093506

TOPICS: Cameras, Imaging systems, Laparoscopy, Tissues, Reflectivity, Hyperspectral imaging, Near infrared, Spatial resolution, Design, Tunable filters

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Significance

Minimally invasive surgery (MIS) has shown vast improvement over open surgery by reducing post-operative stays, intraoperative blood loss, and infection rates. However, in spite of these improvements, there are still prevalent issues surrounding MIS that may be addressed through hyperspectral imaging (HSI). We present a laparoscopic HSI system to further advance the field of MIS.

Aim

We present an imaging system that integrates high-speed HSI technology with a clinical laparoscopic setup and validate the system’s accuracy and functionality. Different configurations that cover the visible (VIS) to near-infrared (NIR) range of electromagnetism are assessed by gauging the spectral fidelity and spatial resolution of each hyperspectral camera.

Approach

Standard Spectralon reflectance tiles were used to provide ground truth spectral footprints to compare with those acquired by our system using the root mean squared error (RMSE). Demosaicing techniques were investigated and used to measure and improve spatial resolution, which was assessed with a USAF resolution test target. A perception-based image quality evaluator was used to assess the demosaicing techniques we developed. Two configurations of the system were developed for evaluation. The functionality of the system was investigated in a phantom study and by imaging ex vivo tissues.

Results

Multiple configurations of our system were tested, each covering different spectral ranges, including VIS (460 to 600 nm), red/NIR (RNIR) (610 to 850 nm), and NIR (665 to 950 nm). Each configuration is capable of achieving real-time imaging speeds of up to 20 frames per second. RMSE values of 3.51±2.03%, 3.43±0.84%, and 3.47% were achieved for the VIS, RNIR, and NIR systems, respectively. We obtained sub-millimeter resolution using our demosaicing techniques.

Conclusions

We developed and validated a high-speed hyperspectral laparoscopic imaging system. The HSI system can be used as an intraoperative imaging tool for tissue classification during laparoscopic surgery.

Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering

Thomas Livecchi, Steven Jacques, Hrebesh Subhash, Mark Pierce

Journal of Biomedical Optics, Vol. 29, Issue 09, 093507, (September 2024) https://doi.org/10.1117/1.JBO.29.9.093507

TOPICS: Artificial neural networks, Tissues, Scattering, Hyperspectral imaging, Blood, Oxygen, Diffuse reflectance spectroscopy, Skin, Monte Carlo methods, Education and training

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Transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies

Luca Giannoni, Marta Marradi, Kevin Scibilia, Ivan Ezhov, Camilla Bonaudo, Angelos Artemiou, Anam Toaha, Frédéric Lange, Charly Caredda, Bruno Montcel, Alessandro Della Puppa, Ilias Tachtsidis, Daniel Rückert, Francesco Saverio Pavone

Journal of Biomedical Optics, Vol. 29, Issue 09, 093508, (September 2024) https://doi.org/10.1117/1.JBO.29.9.093508

TOPICS: Biopsy, Biological samples, Tissues, Near infrared, Visible radiation, Hyperspectral imaging, 3D modeling, Reflectivity, Statistical analysis, Tumors

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Significance

Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long time required for the laboratory analysis and pathological screening, typically taking several days or even weeks to be completed.

Aim

We propose here a transportable, high-density, spectral scanning-based hyperspectral imaging (HSI) setup, named HyperProbe1, that can provide in situ, fast biochemical analysis, and mapping of fresh surgical tissue samples, right after excision, and without the need for fixing, staining nor compromising the integrity of the tissue properties.

Approach

HyperProbe1 is based on spectral scanning via supercontinuum laser illumination filtered with acousto-optic tunable filters. Such methodology allows the user to select any number and type of wavelength bands in the visible and near-infrared range between 510 and 900 nm (up to a maximum of 79) and to reconstruct 3D hypercubes composed of high-resolution (4 to 5 μm), widefield images (0.9×0.9 mm2) of the surgical samples, where each pixel is associated with a complete spectrum.

Results

The HyperProbe1 setup is here presented and characterized. The system is applied to 11 fresh surgical biopsies of glioma from routine patients, including different grades of tumor classification. Quantitative analysis of the composition of the tissue is performed via fast spectral unmixing to reconstruct the mapping of major biomarkers, such as oxy-(HbO2) and deoxyhemoglobin (HHb), as well as cytochrome-c-oxidase (CCO). We also provided a preliminary attempt to infer tumor classification based on differences in composition in the samples, suggesting the possibility of using lipid content and differential CCO concentrations to distinguish between lower and higher-grade gliomas.

Conclusions

A proof of concept of the performances of HyperProbe1 for quantitative, biochemical mapping of surgical biopsies is demonstrated, paving the way for improving current post-surgical, histopathological practice via non-destructive, in situ streamlined screening of fresh tissue samples in a matter of minutes after excision.

Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue

Ivan Ezhov, Kevin Scibilia, Luca Giannoni, Florian Kofler, Ivan Iliash, Felix Hsieh, Suprosanna Shit, Charly Caredda, Frédéric Lange, Bruno Montcel, Ilias Tachtsidis, Daniel Rueckert

Journal of Biomedical Optics, Vol. 29, Issue 09, 093509, (September 2024) https://doi.org/10.1117/1.JBO.29.9.093509

TOPICS: Scattering, Data modeling, Machine learning, Education and training, Body composition, RGB color model, Near infrared spectroscopy, Brain tissue, Tissues, Absorption

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Spectral analysis comparison of pushbroom and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification

Alberto Martín-Pérez, Alejandro Martinez de Ternero, Alfonso Lagares, Eduardo Juarez, César Sanz

Journal of Biomedical Optics, Vol. 29, Issue 09, 093510, (September 2024) https://doi.org/10.1117/1.JBO.29.9.093510

TOPICS: Cameras, Tissues, Line scan image sensors, Brain, Reflectivity, Chromophores, In vivo imaging, Tumors, Sensors, Neuroimaging

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Significance

Hyperspectral imaging sensors have rapidly advanced, aiding in tumor diagnostics for in vivo brain tumors. Linescan cameras effectively distinguish between pathological and healthy tissue, whereas snapshot cameras offer a potential alternative to reduce acquisition time.

Aim

Our research compares linescan and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification.

Approach

We compared a linescan pushbroom camera and a snapshot camera using images from 10 patients with various pathologies. Objective comparisons were made using unnormalized and normalized data for healthy and pathological tissues. We utilized the interquartile range (IQR) for the spectral angle mapping (SAM), the goodness-of-fit coefficient (GFC), and the root mean square error (RMSE) within the 659.95 to 951.42 nm range. In addition, we assessed the ability of both cameras to capture tissue chromophores by analyzing absorbance from reflectance information.

Results

The SAM metric indicates reduced dispersion and high similarity between cameras for pathological samples, with a 9.68% IQR for normalized data compared with 2.38% for unnormalized data. This pattern is consistent across GFC and RMSE metrics, regardless of tissue type. Moreover, both cameras could identify absorption peaks of certain chromophores. For instance, using the absorbance measurements of the linescan camera, we obtained SAM values below 0.235 for four peaks, regardless of the tissue and type of data under inspection. These peaks are one for cytochrome b in its oxidized form at λ=422 nm, two for HbO2 at λ=542 nm and λ=576 nm, and one for water at λ=976 nm.

Conclusion

The spectral signatures of the cameras show more similarity with unnormalized data, likely due to snapshot sensor noise, resulting in noisier signatures post-normalization. Comparisons in this study suggest that snapshot cameras might be viable alternatives to linescan cameras for real-time brain tissue identification.

Machine learning-assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues

Wihan Adi, Bryan E. Rubio Perez, Yuming Liu, Sydney Runkle, Kevin W. Eliceiri, Filiz Yesilkoy

Journal of Biomedical Optics, Vol. 29, Issue 09, 093511, (September 2024) https://doi.org/10.1117/1.JBO.29.9.093511

TOPICS: Collagen, Second harmonic generation, Tissues, Education and training, Mid infrared, Biological samples, Machine learning, Tumors, Hyperspectral imaging, Data modeling

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General

Impact of scattering phase function and polarization on the accuracy of diffuse and sub-diffuse spatial frequency domain imaging

Alec Walter, E. Duco Jansen

Journal of Biomedical Optics, Vol. 29, Issue 09, 095001, (September 2024) https://doi.org/10.1117/1.JBO.29.9.095001

TOPICS: Scattering, Reflectivity, Optical properties, Polarization, Absorption, Spatial frequencies, Zinc oxide, Backscatter, Photons, Monte Carlo methods

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Significance

Although spatial frequency domain imaging (SFDI) has been well characterized under diffuse optical conditions, tissue measurements made outside the diffuse regime can provide new diagnostic information. Before such measurements can become clinically relevant, however, the behavior of sub-diffuse SFDI and its effect on the accuracy of derived tissue parameters must be assessed.

Aim

We aim to characterize the impact that both the assumed scattering phase function (SPF) and the polarization state of the illumination light source have on the accuracy of SFDI-derived optical properties when operating under diffuse or sub-diffuse conditions, respectively.

Approach

Through the use of a set of well-characterized optical phantoms, SFDI accuracy was assessed at four wavelengths (395, 545, 625, and 850 nm) and two different spatial frequencies (0.3 and 1.0 mm−1), which provided a broad range of diffuse and sub-diffuse conditions, using three different SPFs. To determine the effects of polarization, the SFDI accuracy was assessed using both unpolarized and cross-polarized illumination.

Results

It was found that the assumed SPF has a direct and significant impact on the accuracy of the SFDI-derived optical properties, with the best choice of SPF being dictated by the polarization state. As unpolarized SFDI retains the sub-diffuse portion of the signal, optical properties were found to be more accurate when using the full SPF that includes forward and backscattering components. By contrast, cross-polarized SFDI yielded accurate optical properties when using a forward-scattering SPF, matching the behavior of cross-polarization to attenuate the immediate backscattering of sub-diffuse reflectance. Using the correct pairings of SPF and polarization enabled using a reflectance standard, instead of a more subjective phantom, as the reference measurement.

Conclusions

These results provide the foundation for a more thorough understanding of SFDI and enable new applications of this technology in which sub-diffuse conditions dominate (e.g., μa≮μs′) or high spatial frequencies are required.

Dual optical elastography detects TGF-β-induced alterations in the biomechanical properties of skin scaffolds

Taye T. Mekonnen, Yogeshwari S. Ambekar, Christian Zevallos-Delgado, Achuth Nair, Fernando Zvietcovich, Hoda Zarkoob, Manmohan Singh, Yi Wei Lim, Marc Ferrer, Salavat R. Aglyamov, Giuliano Scarcelli, Min Jae Song, Kirill V. Larin

Journal of Biomedical Optics, Vol. 29, Issue 09, 095002, (September 2024) https://doi.org/10.1117/1.JBO.29.9.095002

TOPICS: Skin, Tissues, Elastography, Biomechanics, Microscopy, Tissue optics, Elasticity, Collagen, Optical coherence tomography, Wave propagation

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Significance

The skin’s mechanical properties are tightly regulated. Various pathologies can affect skin stiffness, and understanding these changes is a focus in tissue engineering. Ex vivo skin scaffolds are a robust platform for evaluating the effects of various genetic and molecular interactions on the skin. Transforming growth factor-beta (TGF-β) is a critical signaling molecule in the skin that can regulate the amount of collagen and elastin in the skin and, consequently, its mechanical properties.

Aim

This study investigates the biomechanical properties of bio-engineered skin scaffolds, focusing on the influence of TGF-β, a signaling molecule with diverse cellular functions.

Approach

The TGF-β receptor I inhibitor, galunisertib, was employed to assess the mechanical changes resulting from dysregulation of TGF-β. Skin scaffold samples, grouped into three categories (control, TGF-β-treated, and TGF-β + galunisertib-treated), were prepared in two distinct culture media—one with aprotinin (AP) and another without. Two optical elastography techniques, namely wave-based optical coherence elastography (OCE) and Brillouin microscopy, were utilized to quantify the biomechanical properties of the tissues.

Results

Results showed significantly higher wave speed (with AP, p<0.001; without AP, p<0.001) and Brillouin frequency shift (with AP, p<0.001; without AP, p=0.01) in TGF-β-treated group compared with the control group. The difference in wave speed between the control and TGF-β + galunisertib with (p=0.10) and without AP (p=0.36) was not significant. Moreover, the TGF-β + galunisertib-treated group exhibited lower wave speed without and with AP and reduced Brillouin frequency shift than the TGF-β-treated group without AP, further strengthening the potential role of TGF-β in regulating the mechanical properties of the samples.

Conclusions

These findings offer valuable insights into TGF-β-induced biomechanical alterations in bio-engineered skin scaffolds, highlighting the potential of OCE and Brillouin microscopy in the development of targeted therapies in conditions involving abnormal tissue remodeling and fibrosis.

Relationship between depth-wise refractive index and biomechanical properties of human articular cartilage

Bilour Khan, Ervin Nippolainen, Fatemeh Shahini, Nonappa, Alexey Popov, Juha Töyräs, Isaac Afara

Journal of Biomedical Optics, Vol. 29, Issue 09, 095003, (September 2024) https://doi.org/10.1117/1.JBO.29.9.095003

TOPICS: Cartilage, Biomechanics, Tissues, Collagen, Refractive index, Biological samples, Body composition, Natural surfaces, Optical properties, Materials properties

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Significance

Optical properties of biological tissues, such as refractive index (RI), are fundamental properties, intrinsically linked to the tissue's composition and structure. We hypothesize that, as the RI and the functional properties of articular cartilage (AC) are dependent on the tissue’s structure and composition, the RI of AC is related to its biomechanical properties.

Aim

This study aims to investigate the relationship between RI of human AC and its biomechanical properties.

Approach

Human cartilage samples (n=22) were extracted from the right knee joint of three cadaver donors (one female, aged 47 years, and two males, aged 64 and 68 years) obtained from a commercial biobank (Science Care, Phoenix, Arizona, United States). The samples were initially subjected to mechanical indentation testing to determine elastic [equilibrium modulus (EM) and instantaneous modulus (IM)] and dynamic [dynamic modulus (DM)] viscoelastic properties. An Abbemat 3200 automatic one-wavelength refractometer operating at 600 nm was used to measure the RI of the extracted sections. Similarly, Spearman’s and Pearson’s correlation coefficients were employed for non-normal and normal datasets, respectively, to determine the correlation between the depth-wise RI and biomechanical properties of the cartilage samples as a function of the collagen fibril orientation.

Results

A positive correlation with statistically significant relations (p−values<0.05) was observed between the RI and the biomechanical properties (EM, IM, and DM) along the tissue depth for each zone, e.g., superficial, middle, and deep zones. Likewise, a lower positive correlation with statistically significant relations (p−values<0.05) was also observed for collagen fibril orientation of all zones with the biomechanical properties.

Conclusions

The results indicate that, although the RI exhibits different levels of correlation with different biomechanical properties, the relationship varies as a function of the tissue depth. This knowledge paves the way for optically monitoring changes in AC biomechanical properties nondestructively via changes in the RI. Thus, the RI could be a potential biomarker for assessing the mechanical competency of AC, particularly in degenerative diseases, such as osteoarthritis.

Imaging

Monte Carlo simulation of spatial frequency domain imaging for breast tumors during compression

Constance Robbins, Kuanren Qian, Yongjie Jessica Zhang, Jana M. Kainerstorfer

Journal of Biomedical Optics, Vol. 29, Issue 09, 096001, (September 2024) https://doi.org/10.1117/1.JBO.29.9.096001

TOPICS: Monte Carlo methods, Tumors, Tissues, Breast, Voxels, Optical properties, Biomedical optics, Natural surfaces, Finite element methods, Blood

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PoLambRimetry: a multispectral polarimetric atlas of lamb brain

Verónica Mieites Alonso, Giulio Anichini, Ji Qi, Kevin O’Neill, Olga Conde, Daniel Elson

Journal of Biomedical Optics, Vol. 29, Issue 09, 096002, (September 2024) https://doi.org/10.1117/1.JBO.29.9.096002

TOPICS: Brain, Polarimetry, Depolarization, Mueller matrices, Polarization, Anisotropy, Tissues, Education and training, Matrices, Brain tissue

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All-reflective tethered capsule endoscope for multimodal optical coherence tomography in the esophagus

Xavier Attendu, Paul Bloemen, Niels Kind, Dirk Faber, Daniel de Bruin, Caroline Boudoux, Ton van Leeuwen

Journal of Biomedical Optics, Vol. 29, Issue 09, 096003, (September 2024) https://doi.org/10.1117/1.JBO.29.9.096003

TOPICS: Optical coherence tomography, Endoscopes, Prisms, Design, Reflection, Mirrors, Light sources and illumination, Reflectivity, Imaging systems, Cladding

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Sensing

Depth detection limit of a fluorescent object in tissue-like medium with background emission in continuous-wave measurements: a phantom study

Goro Nishimura, Takahiro Suzuki, Yukio Yamada, Haruki Niwa, Takuji Koike

Journal of Biomedical Optics, Vol. 29, Issue 09, 097001, (September 2024) https://doi.org/10.1117/1.JBO.29.9.097001

TOPICS: Fluorescence, Fluorescence intensity, Target detection, Sensors, Photon counting, Biomedical applications, Tissues, Solids, Neck, Fluorophores

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