Biomedical optics express最新文献

{"title":"Adaptive optical third-harmonic generation microscopy for in vivo imaging of tissues","authors":"Cristina Rodríguez, Daisong Pan, Ryan G. Natan, Manuel A. Mohr, Max Miao, Xiaoke Chen, Trent R. Northen, John P. Vogel, Na Ji","doi":"10.1364/boe.527357","DOIUrl":"https://doi.org/10.1364/boe.527357","url":null,"abstract":"Third-harmonic generation microscopy is a powerful label-free nonlinear imaging technique, providing essential information about structural characteristics of cells and tissues without requiring external labelling agents. In this work, we integrated a recently developed compact adaptive optics module into a third-harmonic generation microscope, to measure and correct for optical aberrations in complex tissues. Taking advantage of the high sensitivity of the third-harmonic generation process to material interfaces and thin membranes, along with the 1,300-nm excitation wavelength used here, our adaptive optical third-harmonic generation microscope enabled high-resolution in vivo imaging within highly scattering biological model systems. Examples include imaging of myelinated axons and vascular structures within the mouse spinal cord and deep cortical layers of the mouse brain, along with imaging of key anatomical features in the roots of the model plant <jats:italic>Brachypodium distachyon</jats:italic>. In all instances, aberration correction led to enhancements in image quality.","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141868425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic volumetric speckle suppression in OCT using deep learning","authors":"Bhaskara Rao Chintada, Sebastián Ruiz-Lopera, René Restrepo, Brett E. Bouma, Martin Villiger, Néstor Uribe-Patarroyo","doi":"10.1364/boe.523716","DOIUrl":"https://doi.org/10.1364/boe.523716","url":null,"abstract":"We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling–TNode– to generate training data. We show that, while TNode processing is computationally demanding, it serves as a convenient, accessible gold-standard source for training data; our cGAN replicates efficient suppression of speckle while preserving tissue structures with dimensions approaching the system resolution of non-local means despeckling while being two orders of magnitude faster than TNode. We demonstrate fast, effective, and high-quality despeckling of the proposed network in different tissue types that are not part of the training. This was achieved with training data composed of just three OCT volumes and demonstrated in three different OCT systems. The open-source nature of our work facilitates re-training and deployment in any OCT system with an all-software implementation, working around the challenge of generating high-quality, speckle-free training data.","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141868631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coronary artery calcification and cardiovascular outcome as assessed by intravascular OCT and artificial intelligence","authors":"Jinwei Tian, Chao Li, Zhifeng Qin, Yanwen Zhang, Qinglu Xu, Yuqi Zheng, Xiangyu Meng, Peng Zhao, Kaiwen Li, Suhong Zhao, Shan Zhong, Xinyu Hou, Xiang Peng, Yuxin Yang, Yu Liu, Songzhi Wu, Yidan Wang, Xiangwen Xi, Yanan Tian, Wenbo Qu, Na Sun, Fan Wang, Yan Wang, Jie Xiong, Xiaofang Ban, Taishi Yonetsu, Rocco Vergallo, Bo Zhang, Bo Yu, Zhao Wang","doi":"10.1364/boe.524946","DOIUrl":"https://doi.org/10.1364/boe.524946","url":null,"abstract":"Coronary artery calcification (CAC) is a marker of atherosclerosis and is thought to be associated with worse clinical outcomes. However, evidence from large-scale high-resolution imaging data is lacking. We proposed a novel deep learning method that can automatically identify and quantify CAC in massive intravascular OCT data trained using efficiently generated sparse labels. 1,106,291 OCT images from 1,048 patients were collected and utilized to train and evaluate the method. The Dice similarity coefficient for CAC segmentation and the accuracy for CAC classification are 0.693 and 0.932, respectively, close to human-level performance. Applying the method to 1259 ST-segment elevated myocardial infarction patients imaged with OCT, we found that patients with a greater extent and more severe calcification in the culprit vessels were significantly more likely to have major adverse cardiovascular and cerebrovascular events (MACCE) (p &lt; 0.05), while the CAC in non-culprit vessels did not differ significantly between MACCE and non-MACCE groups.","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141868430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term imaging of three-dimensional hyphal development using the ePetri dish.","authors":"Oumeng Zhang, Nic Dahlquist, Zachary Leete, Michael Xu, Dean Schneider, Changhuei Yang","doi":"10.1364/BOE.530483","DOIUrl":"10.1364/BOE.530483","url":null,"abstract":"<p><p>Imaging three-dimensional microbial development and behavior over extended periods is crucial for advancing microbiological studies. Here, we introduce an upgraded ePetri dish system specifically designed for extended microbial culturing and 3D imaging, addressing the limitations of existing methods. Our approach includes a sealed growth chamber to enable long-term culturing, and a multi-step reconstruction algorithm that integrates 3D deconvolution, image filtering, ridge, and skeleton detection for detailed visualization of the hyphal network. The system effectively monitored the development of Aspergillus brasiliensis hyphae over a seven-day period, demonstrating the growth medium's stability within the chamber. The system's 3D imaging capability was validated in a volume of 5.5 mm × 4 mm × 0.5 mm, revealing a radial growth pattern of fungal hyphae. Additionally, we show that the system can identify potential filter failures that are undetectable with 2D imaging. With these capabilities, the upgraded ePetri dish represents a significant advancement in long-term 3D microbial imaging, promising new insights into microbial development and behavior across various microbiological research areas.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11249690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141632548","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":"Tunable dynamical tissue phantom for laser speckle imaging","authors":"Soumyajit Sarkar, Murali K, Hari M. Varma","doi":"10.1364/boe.528286","DOIUrl":"https://doi.org/10.1364/boe.528286","url":null,"abstract":"We introduce a novel method to design and implement a tunable dynamical tissue phantom for laser speckle-based <jats:italic>in-vivo</jats:italic> blood flow imaging. This approach relies on stochastic differential equations (SDE) to control a piezoelectric actuator which, upon illuminated with a laser source, generates speckles of pre-defined probability density function and auto-correlation. The validation experiments show that the phantom can generate dynamic speckles that closely replicate both surfaces as well as deep tissue blood flow for a reasonably wide range and accuracy.","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141868428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised denoising of photoacoustic images based on the Noise2Noise network","authors":"Yanda Cheng, Wenhan Zheng, Robert Bing, Huijuan Zhang, Chuqin Huang, Peizhou Huang, Leslie Ying, Jun Xia","doi":"10.1364/boe.529253","DOIUrl":"https://doi.org/10.1364/boe.529253","url":null,"abstract":"In this study, we implemented an unsupervised deep learning method, the Noise2Noise network, for the improvement of linear-array-based photoacoustic (PA) imaging. Unlike supervised learning, which requires a noise-free ground truth, the Noise2Noise network can learn noise patterns from a pair of noisy images. This is particularly important for in vivo PA imaging, where the ground truth is not available. In this study, we developed a method to generate noise pairs from a single set of PA images and verified our approach through simulation and experimental studies. Our results reveal that the method can effectively remove noise, improve signal-to-noise ratio, and enhance vascular structures at deeper depths. The denoised images show clear and detailed vascular structure at different depths, providing valuable insights for preclinical research and potential clinical applications.","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141868429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optofluidic photonic crystal fiber platform for sensitive and reliable fluorescence based biosensing.","authors":"Baptiste Moeglen-Paget, Jayakumar Perumal, Georges Humbert, Malini Olivo, U S Dinish","doi":"10.1364/BOE.527248","DOIUrl":"10.1364/BOE.527248","url":null,"abstract":"<p><p>Biosensing plays a pivotal role in various scientific domains, offering significant contributions to medical diagnostics, environmental monitoring, and biotechnology. Fluorescence biosensing relies on the fluorescence emission from labelled biomolecules to enable sensitive and selective identification and quantification of specific biological targets in various samples. Photonic crystal fibers (PCFs) have led to the development of optofluidic fibers enabling efficient light-liquid interaction within small liquid volume. Herein, we present the development of a user-friendly optofluidic-fiber platform with simple hardware requirements for sensitive and reliable fluorescence biosensing with high measurement repeatability. We demonstrate a sensitivity improvement of the fluorescence emission up to 17 times compared to standard cuvette measurement, with a limit of detection of Cy5 fluorophore as low as 100 pM. The improvement in measurement repeatability is exploited for detecting haptoglobin protein, a relevant biomarker to diagnose several diseases, by using commercially available Cy5 labelled antibodies. The study aims to showcase an optofluidic platform leveraging the benefits provided by optofluidic fibers, which encompass easy light injection, robustness, and high sensitivity.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11249680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141632565","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":"Holistic vibrational spectromics assessment of human cartilage for osteoarthritis diagnosis.","authors":"Hiroki Cook, Anna Crisford, Konstantinos Bourdakos, Douglas Dunlop, Richard O C Oreffo, Sumeet Mahajan","doi":"10.1364/BOE.520171","DOIUrl":"10.1364/BOE.520171","url":null,"abstract":"<p><p>Osteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK [Osteoarthr. Cartil.28(6), 792 (2020)10.1016/j.joca.2020.03.004]. There is an unmet need for patient friendly paradigms for clinical assessment that do not use ionizing radiation (CT), exogenous contrast enhancing dyes (MRI), and biopsy. Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal for providing label-free, deep tissue interrogation. This study demonstrates multimodal \"spectromics\", low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable \"fingerprint\" for diagnosis of OA in human cartilage. This is proposed as method level innovation applicable to both arthro- or endoscopic (minimally invasive) or potential exoscopic (non-invasive) optical approaches. Samples were excised from femoral heads post hip arthroplasty from OA patients (n = 13) and age-matched control (osteoporosis) patients (n = 14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes (using 10 principal components), and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, clinically relevant tissue components were identified through discriminatory spectral features - spectromics biomarkers - allowing interpretable feedback from the enhanced fingerprint. In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic. This novel and elegant approach for data fusion is compatible with various NIR-SWIR optical devices that will allow deep non-destructive penetration.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11249685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141632546","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":"<i>In vivo</i> endoscopic optical coherence elastography based on a miniature probe.","authors":"Haoxing Xu, Qingrong Xia, Chengyou Shu, Jiale Lan, Xiatian Wang, Wen Gao, Shengmiao Lv, Riqiang Lin, Zhihua Xie, Xiaohui Xiong, Fei Li, Jinke Zhang, Xiaojing Gong","doi":"10.1364/BOE.521154","DOIUrl":"10.1364/BOE.521154","url":null,"abstract":"<p><p>Optical coherence elastography (OCE) is a functional extension of optical coherence tomography (OCT). It offers high-resolution elasticity assessment with nanoscale tissue displacement sensitivity and high quantification accuracy, promising to enhance diagnostic precision. However, <i>in vivo</i> endoscopic OCE imaging has not been demonstrated yet, which needs to overcome key challenges related to probe miniaturization, high excitation efficiency and speed. This study presents a novel endoscopic OCE system, achieving the first endoscopic OCE imaging <i>in vivo</i>. The system features the smallest integrated OCE probe with an outer diameter of only 0.9 mm (with a 1.2-mm protective tube during imaging). Utilizing a single 38-MHz high-frequency ultrasound transducer, the system induced rapid deformation in tissues with enhanced excitation efficiency. In phantom studies, the OCE quantification results match well with compression testing results, showing the system's high accuracy. The <i>in vivo</i> imaging of the rat vagina demonstrated the system's capability to detect changes in tissue elasticity continually and distinguish between normal tissue, hematomas, and tissue with increased collagen fibers precisely. This research narrows the gap for the clinical implementation of the endoscopic OCE system, offering the potential for the early diagnosis of intraluminal diseases.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11249679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141632541","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":"Spectral library and method for sparse unmixing of hyperspectral images in fluorescence guided resection of brain tumors","authors":"David Black, Benoit Liquet, Antonio Di Ieva, Walter Stummer, Eric Suero Molina","doi":"10.1364/boe.528535","DOIUrl":"https://doi.org/10.1364/boe.528535","url":null,"abstract":"Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled the detection and classification of tumor regions invisible to the human eye. Prior unmixing work has focused on determining a minimal set of viable fluorophore spectra known to be present in the brain and effectively reconstructing human data without overfitting. With these endmembers, non-negative least squares regression (NNLS) was commonly used to compute the abundances. However, HSI images are heterogeneous, so one small set of endmember spectra may not fit all pixels well. Additionally, NNLS is the maximum likelihood estimator only if the measurement is normally distributed, and it does not enforce sparsity, which leads to overfitting and unphysical results. In this paper, we analyzed 555666 HSI fluorescence spectra from 891 ex vivo measurements of patients with various brain tumors to show that a Poisson distribution indeed models the measured data 82% better than a Gaussian in terms of the Kullback-Leibler divergence, and that the endmember abundance vectors are sparse. With this knowledge, we introduce (1) a library of 9 endmember spectra, including PpIX (620 nm and 634 nm photostates), NADH, FAD, flavins, lipofuscin, melanin, elastin, and collagen, (2) a sparse, non-negative Poisson regression algorithm to perform physics-informed unmixing with this library without overfitting, and (3) a highly realistic spectral measurement simulation with known endmember abundances. The new unmixing method was then tested on the human and simulated data and compared to four other candidate methods. It outperforms previous methods with 25% lower error in the computed abundances on the simulated data than NNLS, lower reconstruction error on human data, better sparsity, and 31 times faster runtime than state-of-the-art Poisson regression. This method and library of endmember spectra can enable more accurate spectral unmixing to aid the surgeon better during brain tumor resection.","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141868632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}