Biomedical optics express最新文献

{"title":"Scattering reduced imaging of spheroids via refractive index estimation and shift-variant deconvolution.","authors":"Yoshimasa Suzuki, Shintaro Fujii, Satoshi Watanabe, Shinichi Hayashi","doi":"10.1364/BOE.569674","DOIUrl":"10.1364/BOE.569674","url":null,"abstract":"<p><p>Confocal laser scanning microscopy (CLSM) is widely used in biological research, but imaging the deep regions of three-dimensional samples like spheroids is challenging due to scattering. We propose a computational method that estimates the refractive index distribution from CLSM images, calculates position-dependent point-spread functions (PSFs) using a multi-diffraction propagation model for both excitation and emission light, and applies shift-variant deconvolution. This approach enables the resolution of deep spheroid structures that could not be resolved in conventional CLSM images. It requires no hardware modifications to conventional CLSM systems, enabling high-quality three-dimensional imaging of scattering samples using conventional equipment.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 8","pages":"3437-3453"},"PeriodicalIF":3.2,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrochemical-assisted scattering imaging system for lymphoma cell classification using machine learning.","authors":"Linyan Xie, Ning Zhang, Kai Yang, Mengfei Wang, Xiangyu Wei, Qiongqiong Ren","doi":"10.1364/BOE.569911","DOIUrl":"10.1364/BOE.569911","url":null,"abstract":"<p><p>Lymphoma is one of the most common malignancies globally, making early diagnosis crucial for improving survival. This study introduces an electrochemical-assisted scattering imaging system (ESIS) for lymphoma cell classification. The system integrates scattering imaging with electrochemical measurements, using a fiber-optic probe for scattering excitation and a 3D rGO-Ti₃C₂-MWCNTs composite electrode to simultaneously monitor H₂O₂ release. Data from these modalities are combined with an SVM algorithm, improving classification performance significantly, with the AUC for HMy2.CIR cells increased from 0.79 to 0.97. The dual-modality approach achieved 90% accuracy, outperforming scattering imaging alone. This method enhances lymphoma subtype differentiation and shows promise for personalized cancer therapies.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 8","pages":"3424-3436"},"PeriodicalIF":3.2,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339310/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate flow speed measurement through correlation ratios using a mode-selective photonic lantern in optical coherence tomography.","authors":"Raphaël Maltais-Tariant, Rodrigo Itzamna Becerra-Deana, Simon Brais-Brunet, Mathieu Dehaes, Caroline Boudoux","doi":"10.1364/BOE.564589","DOIUrl":"10.1364/BOE.564589","url":null,"abstract":"<p><p>A novel method for measuring non-axial flow speed using optical techniques such as optical coherence tomography is introduced. The approach was based on the use of a modally-specific photonic lantern, which permits simultaneous probing of the sample with three distinct coherent spread functions. Transverse flow speed is measured from the ratio between the cross-correlation and autocorrelation of the signals. It achieved a 3 to 5 times higher accuracy than common autocorrelation approaches and measured flows as slow as 0.5 mm/s for an integration time of 1 second. Additionally, the method gives information on the flow's three-dimensional orientation, does not require information about the diffusion coefficient, and is more robust to bias errors such as a gradient in the axial flow velocity.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 8","pages":"3395-3414"},"PeriodicalIF":3.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Label-free deep UV microscopy in oral cytology: a step towards stain-free diagnostics.","authors":"Sumsum P Sunny, Jiabin Chen, Yihan Wang, Bharghabi Paulmajumder, Bofan Song, A R Subhashini, Vijay Pillai, Moni A Kuriakose, Praveen Birur N, Amritha Suresh, Rongguang Liang","doi":"10.1364/BOE.569553","DOIUrl":"10.1364/BOE.569553","url":null,"abstract":"<p><p>Oral cancer remains a significant global health challenge. Early detection is essential for improving prognostic outcomes, yet current diagnostic practices are hindered by the invasive nature of biopsies and the reliance on staining methods. This study presents a low-cost, label-free deep ultraviolet (UV) microscopy system, integrated with artificial intelligence (AI), for analyzing unstained cytology specimens. Leveraging the absorption properties of nuclei under UV light, this technology produces high-resolution molecular images, enabling real-time, automated, and objective analysis of cellular and nuclear morphology. Forty patients with oral lesions-spanning benign, oral potentially malignant disorders (OPMD), and oral squamous cell carcinoma (OSCC)-participated in this study. Cytology nuclei were segmented using a deep learning-based U-Net architecture, and key nuclear features, including intensity, solidity, eccentricity, and axis ratio, were extracted and analyzed. These features demonstrated high sensitivity (>80%) and specificity (>79%) in distinguishing diagnostic groups. Furthermore, unsupervised clustering based on these features effectively classified patient cohorts, underscoring its potential for early diagnosis. The proposed method eliminates the need for staining, reduces processing time, and minimizes environmental impact, making it particularly suited for primary healthcare settings. By integrating advanced imaging with AI, this scalable approach addresses critical gaps in early oral cancer detection, offering significant potential to improve patient outcomes. Validation in larger and more diverse cohorts is required to enhance its clinical utility.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 8","pages":"3415-3423"},"PeriodicalIF":3.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised cross-modal biomedical image fusion framework with dual-path detail enhancement and global context awareness.","authors":"Yao Liu, Wujie Chen, Zhen-Li Huang, ZhengXia Wang","doi":"10.1364/BOE.562137","DOIUrl":"10.1364/BOE.562137","url":null,"abstract":"<p><p>Fluorescence imaging and phase-contrast imaging are two important imaging techniques in molecular biology research. Green fluorescent protein images can locate high-intensity protein regions in Arabidopsis cells, while phase-contrast images provide information on cellular structures. The fusion of these two types of images facilitates protein localization and interaction studies. However, traditional multimodal optical imaging systems have complex optical components and cumbersome operations. Although deep learning has provided new solutions for multimodal image fusion, existing methods are usually based on convolution operations, which have limitations such as ignoring long-range contextual information and losing detailed information. To address these limitations, we propose an unsupervised cross-modal biomedical image fusion framework, called UCBFusion. First, we design a dual-branch feature extraction module to retain the local detail information of each modality and prevent the loss of texture details during convolution operations. Second, we introduce a context-aware attention fusion module to enhance the ability to extract global features and establish long-range relationships. Lastly, our framework adopts an interactive parallel architecture to achieve the interactive fusion of local and global information. Experimental results on Arabidopsis thaliana datasets and other image fusion tasks indicate that UCBFusion achieves superior fusion results compared with state-of-the-art algorithms, in terms of performance and generalization ability across different types of datasets. This study provides a crucial driving force for the development of Arabidopsis thaliana research.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 8","pages":"3378-3394"},"PeriodicalIF":3.2,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of quantum imaging in biology.","authors":"Hoda Lotfipour, Hassan Sobhani, Mohamad Taghi Dejpasand, Morteza Sasani Ghamsari","doi":"10.1364/BOE.566801","DOIUrl":"10.1364/BOE.566801","url":null,"abstract":"<p><p>Application of quantum imaging in biology In biology, one of the main challenges is achieving a balance between high precision and minimal invasiveness in measurements. With cutting-edge techniques, the primary limitations to experimental accuracy often stem not from device-related noise but from fundamental physical constraints. Since nature is the underlying source of these constraints, it makes sense to go to quantum mechanics, the most basic theory of matter, for a solution. Improved measurement performance may be possible through the application of quantum effects, particularly those pertaining to coherence. It offers useful tools that, at the very least, offer interesting technical solutions even when they don't fully display cohesive behaviors. One of the primary applications of quantum technologies is quantum metrology, which uses the non-classical state of light to measure physical properties with great resolution and sensitivity. For biological applications, the quantum state of light may be utilized for precision enhancement and quantum noise reduction. This explains how quantum metrology, and particularly quantum imaging, can be used to enhance picture quality, measure shifts in quantum scales in biological systems, and boost imaging precision and resolution using quantum light sources, devices, and protocols. In this study, we give a summary of the possible uses of quantum technology in biology and medicine. This review presents a comprehensive overview of how quantum technologies can be applied in biology and medicine. It also explores the latest developments in quantum biological imaging, quantum microscopy, and quantum materials, while discussing the challenges and opportunities these emerging technologies bring.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 8","pages":"3349-3377"},"PeriodicalIF":3.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning-enhanced 3D fiber orientation mapping in thick cardiac tissues.","authors":"Eda Nur Saruhan, Hakancan Ozturk, Demet Kul, Bortecine Sevgin, Merve Nur Coban, Kerem Pekkan","doi":"10.1364/BOE.563643","DOIUrl":"10.1364/BOE.563643","url":null,"abstract":"<p><p>Fibrous proteins, such as elastin and collagen, are crucial for the structural integrity of the cardiovascular system. For thin tissue-engineered heart valves and surgical patches, the two-dimensional mapping of fiber orientation is well-established. However, for three-dimensional (3D) thick tissue samples, e.g., the embryonic whole heart, robust 3D fiber analysis tools are not available. This information is essential for computational vascular modeling and tissue microstructure characterization. Therefore, this study employs machine learning (ML) and deep learning (DL) techniques to analyze the 3D cardiovascular fiber structures in thick samples of porcine pericardium and embryonic whole hearts. It is hypothesized that ML/DL-based fiber orientation analysis will outperform traditional Fourier transform and directional filter methods by offering higher spatial accuracy and reduced dependency on manual preprocessing. We trained our ML/DL models on both synthetic and real-world cardiovascular datasets obtained from confocal imaging. The evaluation used a mixed dataset of 1200 samples and a porcine/bovine dataset of 400 samples. Support vector regression (SVR) demonstrated the highest accuracy, achieving a normalized mean absolute error (nMAE) of 5.0% on the mixed dataset and 13.0% on the biological dataset. Among DL models, convolutional neural network (CNN) and residual network-50 (ResNet50) had an nMAE of 12.0% and 11.0% on the mixed dataset and 23.0% and 22.0% on the biological dataset, respectively. Attention mechanisms improved performance further, with the channel attention ResNet50 achieving an nMAE of 5.8% on the mixed dataset and 21.0% on the biological dataset. These findings highlight the potential of ML and DL techniques in improving 3D fiber orientation detection, enabling detailed cardiovascular microstructural assessment.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 8","pages":"3315-3336"},"PeriodicalIF":3.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-spectral optoacoustic microscopy driven by gas-filled hollow-core fiber laser pulses.","authors":"Cuiling Zhang, Marcello Meneghetti, J E Antonio-Lopez, Rodrigo Amezcua-Correa, Yazhou Wang, Christos Markos","doi":"10.1364/BOE.567845","DOIUrl":"10.1364/BOE.567845","url":null,"abstract":"<p><p>Multi-spectral optoacoustic microscopy (MS-OAM) requires high-performance light sources capable of delivering multiple intense spectral lines precisely matched to the absorption characteristics of selected biomolecules. We present a gas-filled anti-resonant hollow-core fiber (ARHCF) laser source optimized for near-infrared (NIR) MS-OAM. The hydrogen (H₂)-filled ARHCF laser emits multiple spectral lines with high pulse energy and narrow linewidths (<0.1 nm) across a broad spectral range (∼1100 nm to ∼2200 nm). Several Raman laser lines were generated to overlap with key biomolecular absorption bands, including lipids (1210 nm and 1700nm), collagen (∼1540 nm), and water (∼1400 nm and ∼1900 nm). We demonstrate the system's performance by mapping absorbers in the first and second overtone regions of hair, pig tissue, and collagen samples. This work aims to bring the gas-filled fiber technology into MS-OAM applications and paves the way for high-resolution, label-free bio-imaging across extended infrared and ultraviolet regimes.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 8","pages":"3337-3348"},"PeriodicalIF":3.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation-based dosimetry of transcranial and intranasal photobiomodulation of the human brain: the roles of wavelength, power density, and skin tone.","authors":"Hannah Van Lankveld, Anh Q Mai, Lew Lim, Nazanin Hosseinkhah, Paolo Cassano, J Jean Chen","doi":"10.1364/BOE.567345","DOIUrl":"10.1364/BOE.567345","url":null,"abstract":"<p><p>Photobiomodulation (PBM) using near-infrared (NIR) light is a novel neuromodulation technique. However, despite the many in vivo studies, the stimulation protocols for PBM vary across studies, and the current understanding of the physiological effects of PBM, as well as the dose dependence, is limited. Specifically, although NIR light can be absorbed by melanin in the skin, the understanding of how skin tones compare and how their influence interacts with other dose parameters remains limited. This study investigates the effect of melanin, optical power density, and wavelength on light penetration and energy accumulation via forehead and intranasal PBM. We use Monte Carlo simulations of a single laser source for transcranial (tPBM, forehead position) and intranasal (iPBM, nostril position) irradiation on a healthy human brain model. We investigate wavelengths of 670, 810, and 1064 nm at various power densities in combination with light (\"Caucasian\"), medium (\"Asian\"), and dark (\"African\") skin tone categories as defined in the literature. Our simulations show that a maximum of 15% of the incidental energy for tPBM and 1% for iPBM reaches the cortex from the light source. The rostral dorsal prefrontal cortex and the ventromedial prefrontal cortex accumulate the highest light energy in tPBM and iPBM, respectively, for both wavelengths. Notably, we show that nominally \"Caucasian\" skin allows the highest energy accumulation of all three skin tones. Moreover, the 810 nm wavelength for tPBM and the 1064 nm wavelength for iPBM produced the highest cortical energy accumulation, which was linearly correlated with optical power density, but these variations could be overridden by a difference in skin tone in the tPBM case.The simulations serve as a starting point for enabling hypothesis generation for in vivo PBM investigations. This study is the first to account for skin tone as a tPBM dosing consideration. For the future of PBM research, it is important to evaluate combinations of stimulation parameters (wavelength, optical power density, pulsation frequency, duration, light source) when working to determine an optimal dosage for PBM-based therapy.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 8","pages":"3295-3314"},"PeriodicalIF":3.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging pretrained vision transformers for automated cancer diagnosis in optical coherence tomography images.","authors":"Soumyajit Ray, Cheng-Yu Lee, Hyeon-Cheol Park, David W Nauen, Chetan Bettegowda, Xingde Li, Rama Chellappa","doi":"10.1364/BOE.563694","DOIUrl":"10.1364/BOE.563694","url":null,"abstract":"<p><p>This study presents an approach to brain cancer detection based on optical coherence tomography (OCT) images and advanced machine learning techniques. The research addresses the critical need for accurate, real-time differentiation between cancerous and noncancerous brain tissue during neurosurgical procedures. The proposed method combines a pre-trained large vision transformer (ViT) model, specifically DINOv2, with a convolutional neural network (CNN) operating on the grey level co-occurrence matrix (GLCM) texture features. This dual-path architecture leverages both the global contextual feature extraction capabilities of transformers and the local texture analysis strengths of GLCM + CNNs. To mitigate patient-specific bias from the limited cohort, we incorporate an adversarial discriminator network that attempts to identify individual patients from feature representations, creating a competing objective that forces the model to learn generalizable cancer-indicative features rather than patient-specific characteristics. We also explore an alternative state space model approach using MambaVision blocks, which achieves comparable performance. The dataset comprised OCT images from 11 patients, with 5,831 B-frame slices from 7 patients used for training and validation, and 1,610 slices from 4 patients used for testing. The model achieved high accuracy in distinguishing cancerous from noncancerous tissue, with over 99% accuracy on the training dataset, 98.8% on the validation dataset and 98.6% accuracy on the test dataset. This approach demonstrates significant potential for achieving and improving intraoperative decision-making in brain cancer surgeries, offering real-time, high-accuracy tissue classification and surgical guidance.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 8","pages":"3283-3294"},"PeriodicalIF":3.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}