Journal of biophotonics最新文献

Deep Learning for Autonomous Surgical Guidance Using 3-Dimensional Images From Forward-Viewing Endoscopic Optical Coherence Tomography. 利用前视内窥镜光学相干断层扫描的三维图像进行自主手术指导的深度学习。

Journal of biophotonics Pub Date : 2025-07-25 DOI: 10.1002/jbio.202500181

Sinaro Ly, Adrien Badré, Parker Brandt, Chen Wang, Paul Calle, Justin Reynolds, Qinghao Zhang, Kar-Ming Fung, Haoyang Cui, Zhongxin Yu, Sanjay G Patel, Yunlong Liu, Nathan A Bradley, Qinggong Tang, Chongle Pan

{"title":"Deep Learning for Autonomous Surgical Guidance Using 3-Dimensional Images From Forward-Viewing Endoscopic Optical Coherence Tomography.","authors":"Sinaro Ly, Adrien Badré, Parker Brandt, Chen Wang, Paul Calle, Justin Reynolds, Qinghao Zhang, Kar-Ming Fung, Haoyang Cui, Zhongxin Yu, Sanjay G Patel, Yunlong Liu, Nathan A Bradley, Qinggong Tang, Chongle Pan","doi":"10.1002/jbio.202500181","DOIUrl":"https://doi.org/10.1002/jbio.202500181","url":null,"abstract":"A three-dimensional convolutional neural network (3D-CNN) was developed for the analysis of volumetric optical coherence tomography (OCT) images to enhance endoscopic guidance during percutaneous nephrostomy. The model was performance-benchmarked using a 10-fold nested cross-validation procedure and achieved an average test accuracy of 90.57% across a dataset of 10 porcine kidneys. This performance significantly exceeded that of 2D-CNN models that attained average test accuracies ranging from 85.63% to 88.22% using 1, 10, or 100 radial sections extracted from the 3D OCT volumes. The 3D-CNN (~12 million parameters) was benchmarked against three state-of-the-art volumetric architectures: the 3D Vision Transformer (3D-ViT, ~45 million parameters), 3D-DenseNet121 (~12 million parameters), and the Multi-plane and Multi-slice Transformer (M3T, ~29 million parameters). While these models achieved comparable inferencing accuracy, the 3D-CNN exhibited lower inference latency (33 ms) than 3D-ViT (86 ms), 3D-DenseNet121 (58 ms), and M3T (93 ms), representing a critical advantage for real-time surgical guidance applications. These results demonstrate the 3D-CNN's capability as a powerful and practical tool for computer-aided diagnosis in OCT-guided surgical interventions.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500181"},"PeriodicalIF":0.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three-Dimensional Siamese Multi-Level Features Neural Network Based 3D Fusion Improves the Depth of Field in Photoacoustic Microscopy. 基于三维连体多层次特征神经网络的三维融合提高了光声显微镜的景深。

Journal of biophotonics Pub Date : 2025-07-22 DOI: 10.1002/jbio.202500195

Bokang You, Guobin Liu, Jiahuan He, Yubin Cao, Yiguang Wang, Guolin Liu, Siyi Cao, Shangkun Hou, Kangjun Guo, Qiegen Liu, Xianlin Song

{"title":"Three-Dimensional Siamese Multi-Level Features Neural Network Based 3D Fusion Improves the Depth of Field in Photoacoustic Microscopy.","authors":"Bokang You, Guobin Liu, Jiahuan He, Yubin Cao, Yiguang Wang, Guolin Liu, Siyi Cao, Shangkun Hou, Kangjun Guo, Qiegen Liu, Xianlin Song","doi":"10.1002/jbio.202500195","DOIUrl":"https://doi.org/10.1002/jbio.202500195","url":null,"abstract":"Microscopic imaging techniques pursue high-resolution, large depth of field (DoF) imaging but are limited by hardware, especially the strong focusing of objective lenses. Optical-resolution photoacoustic microscopy (OR-PAM) has a narrow DoF due to the intense laser focusing needed for high-resolution imaging. To address this, we propose a novel volumetric information fusion method using a three-dimensional siamese multi-level features convolutional neural network (3DSMFCNN) for cost-effective, large-DoF imaging. Initially, an initial decision map (IDM) is produced by performing focus region identification on multi-focus 3D photoacoustic data with the pre-trained 3DSMFCNN. The IDM is then refined through consistency verification and Gaussian filtering to generate the final decision map (FDM). A DoF-enhanced photoacoustic image is obtained by voxel-weighted averaging based on the FDM. Experiments with multi-focus 3D simulated fibers, blood vessels, and real data demonstrate that the method significantly extends the DoF of OR-PAM without sacrificing lateral resolution, which confirms its effectiveness, robustness, and applicability.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500195"},"PeriodicalIF":0.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantitative Assessment of Human Anisotropic Skin Elasticity Using the Dispersion Curve of Surface Acoustic Wave Elastography. 用表面声波弹性谱色散曲线定量评价人体皮肤各向异性弹性。

Journal of biophotonics Pub Date : 2025-07-21 DOI: 10.1002/jbio.202500299

Guangyu Zhang, Zhengshuyi Feng, Chunhui Li, Zhihong Huang

{"title":"Quantitative Assessment of Human Anisotropic Skin Elasticity Using the Dispersion Curve of Surface Acoustic Wave Elastography.","authors":"Guangyu Zhang, Zhengshuyi Feng, Chunhui Li, Zhihong Huang","doi":"10.1002/jbio.202500299","DOIUrl":"https://doi.org/10.1002/jbio.202500299","url":null,"abstract":"Accurate assessment of skin elasticity is critical for understanding its physiological and pathological conditions. Conventional models often neglect anisotropy, leading to inconsistent measurements. We investigated skin anisotropic using Surface acoustic wave (SAW) based Optical Coherence Elastography (OCE), analyzing the angular ( <math> <semantics><mrow><mi>θ</mi></mrow> <annotation>$$ theta $$</annotation></semantics> </math> ) dependence of SAW velocity ( <math> <semantics> <mrow><msub><mi>C</mi> <mi>R</mi></msub> </mrow> <annotation>$$ {C}_R $$</annotation></semantics> </math> ) relative to fiber orientation. Validation experiments were conducted on chicken thighs and human forearms. In chicken thighs, <math> <semantics> <mrow><msub><mi>C</mi> <mi>R</mi></msub> </mrow> <annotation>$$ {C}_R $$</annotation></semantics> </math> showed significant differences across propagation directions ranging from 90° to 0° ( <math> <semantics><mrow><mi>p</mi></mrow> <annotation>$$ p $$</annotation></semantics> </math> = 0.008 < 0.05). In the dermis layer of forearms, the <math> <semantics> <mrow><msub><mi>C</mi> <mi>R</mi></msub> </mrow> <annotation>$$ {C}_R $$</annotation></semantics> </math> demonstrated significant angular dependence ( <math> <semantics><mrow><mi>p</mi></mrow> <annotation>$$ p $$</annotation></semantics> </math> = 0.031), with a percentage change of 31% while Young's modulus ( <math> <semantics><mrow><mi>E</mi></mrow> <annotation>$$ E $$</annotation></semantics> </math> ) increased by 21.7 ± 11.5 kPa (60.32%) from 90° to 0°. No significant dependence was found in the hypodermis layer. These results demonstrate that incorporating anisotropy improves elasticity estimation and provides a practical foundation for skin assessment.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500299"},"PeriodicalIF":0.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Algorithms for Intraoperative Neurovascular Inclusion Detection, Diameter and Depth Prediction Based on Frequency Domain Near Infrared Spectroscopy. 基于频域近红外光谱的术中神经血管包涵体检测、直径和深度预测算法。

Journal of biophotonics Pub Date : 2025-07-21 DOI: 10.1002/jbio.202500220

Mariia Belsheva, Larisa Safonova, Alexey Shkarubo, Ilya Chernov

引用次数: 0

Correlation Between Fingerprint-Guided Sweat Ducts Features From OCT and Diabetic Neuropathy Using Voronoi Diagram. 指纹引导的OCT汗管特征与糖尿病神经病变的相关性研究。

Journal of biophotonics Pub Date : 2025-07-19 DOI: 10.1002/jbio.202500096

Wangbiao Li, Zhida Chen, Hui Lin, Shidi Hu, Kaihong Chen, Yong Guo, Shulian Wu, Hui Li, Yu Chen, Zhifang Li

{"title":"Correlation Between Fingerprint-Guided Sweat Ducts Features From OCT and Diabetic Neuropathy Using Voronoi Diagram.","authors":"Wangbiao Li, Zhida Chen, Hui Lin, Shidi Hu, Kaihong Chen, Yong Guo, Shulian Wu, Hui Li, Yu Chen, Zhifang Li","doi":"10.1002/jbio.202500096","DOIUrl":"https://doi.org/10.1002/jbio.202500096","url":null,"abstract":"Diabetic neuropathy (DN) is a prevalent chronic complication of diabetes. Sweat glands are directly controlled by the sympathetic nervous system, whose neuropathy affects the thermal regulation of the skin and results in morphological changes in sweat ducts. This study aims to investigate the correlation between the characteristics of fingerprint-guided sweat ducts assessed by optical coherence tomography and DN based on a predictive model using a back propagation neural network (BPNN) and principal component analysis (PCA). The results demonstrate that the number, volume, and spacing of sweat ducts are correlated with the severity of DN. The Voronoi diagram of the sweat duct distribution demonstrates irregularities in the spatial distribution among patients with DN. Furthermore, the PCA-based BPNN model has good predictive accuracy between patients with non-neuropathic, neuropathic, and severe neuropathic diabetes. These findings suggest that OCT-assessed sweat duct features may serve as non-invasive biomarkers for DN in patients with diabetes.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500096"},"PeriodicalIF":0.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain Activity Within Prefrontal Cortex: A Resting-State fNIRS Comparative Study in High-Functioning Autism Preschoolers and Typically Developed Peers. 前额叶皮层内的脑活动：高功能自闭症学龄前儿童与正常发育儿童静息状态fNIRS比较研究。

Journal of biophotonics Pub Date : 2025-07-16 DOI: 10.1002/jbio.202500257

Wenlong Song, Bin Zuo, Caiming Jiang, Zhicheng Zhang

引用次数: 0

Multiparametric Wide-Field Fluorescence Imaging via Polarization Modulation With Liquid Crystal Rotators. 液晶旋转器偏振调制多参数宽场荧光成像。

Journal of biophotonics Pub Date : 2025-07-15 DOI: 10.1002/jbio.202500187

Wenshuai Li, Bin Xu, Chaofu Sun, Weiping Liu, Yang Zhang, Ji Wu, Xuefeng Liu, Jichuan Xiong

{"title":"Multiparametric Wide-Field Fluorescence Imaging via Polarization Modulation With Liquid Crystal Rotators.","authors":"Wenshuai Li, Bin Xu, Chaofu Sun, Weiping Liu, Yang Zhang, Ji Wu, Xuefeng Liu, Jichuan Xiong","doi":"10.1002/jbio.202500187","DOIUrl":"https://doi.org/10.1002/jbio.202500187","url":null,"abstract":"Fluorescence polarization imaging provides critical insights into molecular orientation, yet existing methods face limitations in parameter extraction efficiency and implementation complexity. This study proposes Wide-Field Multiparametric Fluorescence Imaging (WMPFI) using a Liquid Crystal Polarization Rotator (LCPR) for rapid polarization state modulation that generates pixel-level intensity modulations that encode fluorophore orientation. By analyzing fluorescence intensity variations under different polarization excitations, WMPFI reconstructs sample structural information through parametric imaging without requiring optical lock-in detection or computational reconstruction algorithms. Comparative experiments with Conventional Microscopy (CM) demonstrate WMPFI's enhanced sensitivity to anisotropic fluorescent dipole orientations, achieving superior contrast and resolution in imaging neural stem cells and skin tissues. The method's capacity for multi-parameter acquisition through polarization modulation offers a simplified approach for probing subcellular material exchange dynamics, with potential extensions to super-resolution imaging modalities.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500187"},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144639101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Controlled Thermal Stimulation Using 980-nm Laser for Collagen Remodeling. 利用980纳米激光控制热刺激胶原蛋白重塑。

Journal of biophotonics Pub Date : 2025-07-14 DOI: 10.1002/jbio.202500213

Mijeong Kim, Hwarang Shin, Jiho Lee, Hyun Wook Kang

引用次数: 0

Hyperspectral Imaging for Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Differentiation. 肝细胞癌与肝内胆管癌鉴别的高光谱成像。

Journal of biophotonics Pub Date : 2025-07-14 DOI: 10.1002/jbio.202500227

Yunze Li, Haiyan Chen, Wei Li, Meng Yu, Jinlin Deng, Qize Lv, Yifei Liu, Shuai Gao

{"title":"Hyperspectral Imaging for Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Differentiation.","authors":"Yunze Li, Haiyan Chen, Wei Li, Meng Yu, Jinlin Deng, Qize Lv, Yifei Liu, Shuai Gao","doi":"10.1002/jbio.202500227","DOIUrl":"https://doi.org/10.1002/jbio.202500227","url":null,"abstract":"This study proposed an intelligent intraoperative diagnostic framework that combines hyperspectral imaging (HSI) with deep reinforcement learning to accurately differentiate hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), the two main subtypes of primary liver cancer. To address the limitations of conventional imaging techniques and serum biomarkers, the authors constructed the first clinical HSI dataset of liver tumors (n = 131, spectral range 400-1000 nm). The proposed method integrates a 3D residual neural network (3D-ResNet) with a Proximal Policy Optimization (PPO)-based reinforcement learning algorithm, framing spectral band selection as a Markov decision process. An intraclass constrained cross-entropy loss further enhances class separability and compactness. Experimental results demonstrate a classification accuracy of 95%, outperforming traditional band selection approaches. This framework enables rapid, real-time tumor subtyping during surgery, addressing the critical clinical need for timely and accurate liver cancer diagnosis, and offers a promising tool for advancing precision oncology and improving intraoperative decision making.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500227"},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144628342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantitative Characterization of Undulation Patterns and Regional Features of the Oral Basement Membrane Zone Using Optical Coherence Tomography. 使用光学相干断层成像定量表征口腔基底膜区波动模式和区域特征。

Journal of biophotonics Pub Date : 2025-07-11 DOI: 10.1002/jbio.202500252

Jiacheng Gu, Jinpeng Liao, Tianyu Zhang, Yilong Zhang, Simon Shepherd, Michaelina Macluskey, Zhihong Huang, Chunhui Li

{"title":"Quantitative Characterization of Undulation Patterns and Regional Features of the Oral Basement Membrane Zone Using Optical Coherence Tomography.","authors":"Jiacheng Gu, Jinpeng Liao, Tianyu Zhang, Yilong Zhang, Simon Shepherd, Michaelina Macluskey, Zhihong Huang, Chunhui Li","doi":"10.1002/jbio.202500252","DOIUrl":"https://doi.org/10.1002/jbio.202500252","url":null,"abstract":"Alterations in the undulation pattern of the basement membrane zone (BMZ) reflect early pathological changes in oral squamous cell carcinoma (OSCC). Optical coherence tomography (OCT) provides real-time, high-resolution, in vivo three-dimensional (3D) imaging of the oral mucosal microstructures, including BMZ. In this study, we quantified the undulation index of BMZ at four oral sites: the floor of the mouth, lower lip, buccal mucosa, and hard palate, and visualized their 3D morphological structures. Among regular participants, the mean undulation index varied across these sites: 14.64% ± 9.07% for floor, 8.74% ± 4.65% for lower lip, 9.45% ± 3.64% for buccal mucosa, and 14.84% ± 7.71% for hard palate. The corresponding epithelial thicknesses were 209.48 ± 87.51, 311.31 ± 106.85, 596.10 ± 138.40, and 444.83 ± 61.83 μm. It highlights the significance of BMZ morphology and epithelium thickness as potential diagnostic markers, offering a new approach for the early detection of OSCC.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500252"},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0