BME frontiers最新文献

{"title":"Recent Advancements in Optical Harmonic Generation Microscopy: Applications and Perspectives.","authors":"Darian S James,&nbsp;Paul J Campagnola","doi":"10.34133/2021/3973857","DOIUrl":"10.34133/2021/3973857","url":null,"abstract":"<p><p>Second harmonic generation (SHG) and third harmonic generation (THG) microscopies have emerged as powerful imaging modalities to examine structural properties of a wide range of biological tissues. Although SHG and THG arise from very different contrast mechanisms, the two are complimentary and can often be collected simultaneously using a modified multiphoton microscope. In this review, we discuss the needed instrumentation for these modalities as well as the underlying theoretical principles of SHG and THG in tissue and describe how these can be leveraged to extract unique structural information. We provide an overview of recent advances showing how SHG microscopy has been used to evaluate collagen alterations in the extracellular matrix and how this has been used to advance our knowledge of cancers, fibroses, and the cornea, as well as in tissue engineering applications. Specific examples using polarization-resolved approaches and machine learning algorithms are highlighted. Similarly, we review how THG has enabled developmental biology and skin cancer studies due to its sensitivity to changes in refractive index, which are ubiquitous in all cell and tissue assemblies. Lastly, we offer perspectives and outlooks on future directions of SHG and THG microscopies and present unresolved questions, especially in terms of overall miniaturization and the development of microendoscopy instrumentation.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2021 ","pages":"3973857"},"PeriodicalIF":0.0,"publicationDate":"2021-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioresorbable Multilayer Photonic Cavities as Temporary Implants for Tether-Free Measurements of Regional Tissue Temperatures.","authors":"Wubin Bai,&nbsp;Masahiro Irie,&nbsp;Zhonghe Liu,&nbsp;Haiwen Luan,&nbsp;Daniel Franklin,&nbsp;Khizar Nandoliya,&nbsp;Hexia Guo,&nbsp;Hao Zang,&nbsp;Yang Weng,&nbsp;Di Lu,&nbsp;Di Wu,&nbsp;Yixin Wu,&nbsp;Joseph Song,&nbsp;Mengdi Han,&nbsp;Enming Song,&nbsp;Yiyuan Yang,&nbsp;Xuexian Chen,&nbsp;Hangbo Zhao,&nbsp;Wei Lu,&nbsp;Giuditta Monti,&nbsp;Iwona Stepien,&nbsp;Irawati Kandela,&nbsp;Chad R Haney,&nbsp;Changsheng Wu,&nbsp;Sang Min Won,&nbsp;Hanjun Ryu,&nbsp;Alina Rwei,&nbsp;Haixu Shen,&nbsp;Jihye Kim,&nbsp;Hong-Joon Yoon,&nbsp;Wei Ouyang,&nbsp;Yihan Liu,&nbsp;Emily Suen,&nbsp;Huang-Yu Chen,&nbsp;Jerry Okina,&nbsp;Jushen Liang,&nbsp;Yonggang Huang,&nbsp;Guillermo A Ameer,&nbsp;Weidong Zhou,&nbsp;John A Rogers","doi":"10.34133/2021/8653218","DOIUrl":"10.34133/2021/8653218","url":null,"abstract":"<p><p><i>Objective and Impact Statement</i>. Real-time monitoring of the temperatures of regional tissue microenvironments can serve as the diagnostic basis for treating various health conditions and diseases. <i>Introduction</i>. Traditional thermal sensors allow measurements at surfaces or at near-surface regions of the skin or of certain body cavities. Evaluations at depth require implanted devices connected to external readout electronics via physical interfaces that lead to risks for infection and movement constraints for the patient. Also, surgical extraction procedures after a period of need can introduce additional risks and costs. <i>Methods</i>. Here, we report a wireless, bioresorbable class of temperature sensor that exploits multilayer photonic cavities, for continuous optical measurements of regional, deep-tissue microenvironments over a timeframe of interest followed by complete clearance via natural body processes. <i>Results</i>. The designs decouple the influence of detection angle from temperature on the reflection spectra, to enable high accuracy in sensing, as supported by in vitro experiments and optical simulations. Studies with devices implanted into subcutaneous tissues of both awake, freely moving and asleep animal models illustrate the applicability of this technology for in vivo measurements. <i>Conclusion</i>. The results demonstrate the use of bioresorbable materials in advanced photonic structures with unique capabilities in tracking of thermal signatures of tissue microenvironments, with potential relevance to human healthcare.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2021 ","pages":"8653218"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in photoacoustic tomography.","authors":"Lei Li,&nbsp;Lihong V Wang","doi":"10.34133/2021/9823268","DOIUrl":"https://doi.org/10.34133/2021/9823268","url":null,"abstract":"<p><p>Photoacoustic tomography (PAT) that integrates the molecular contrast of optical imaging with the high spatial resolution of ultrasound imaging in deep tissue has widespread applications in basic biological science, preclinical research and clinical trials. Recently, tremendous progress has been made in PAT regarding technical innovations, preclinical applications, and clinical translations. Here, we selectively review the recent progresses and advances in PAT, including the development of advanced PAT systems for small-animal and human imaging, newly engineered optical probes for molecular imaging, broad-spectrum PAT for label-free imaging of biological tissues, high-throughput snapshot photoacoustic topography, and integration of machine learning for image reconstruction and processing. We envision that PAT will have further technical developments and more impactful applications in biomedicine.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2021 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10085577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9301089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Neurons to Cognition: Technologies for Precise Recording of Neural Activity Underlying Behavior.","authors":"Richard H Roth,&nbsp;Jun B Ding","doi":"10.34133/2020/7190517","DOIUrl":"https://doi.org/10.34133/2020/7190517","url":null,"abstract":"<p><p>Understanding how brain activity encodes information and controls behavior is a long-standing question in neuroscience. This complex problem requires converging efforts from neuroscience and engineering, including technological solutions to perform high-precision and large-scale recordings of neuronal activity <i>in vivo</i> as well as unbiased methods to reliably measure and quantify behavior. Thanks to advances in genetics, molecular biology, engineering, and neuroscience, in recent decades, a variety of optical imaging and electrophysiological approaches for recording neuronal activity in awake animals have been developed and widely applied in the field. Moreover, sophisticated computer vision and machine learning algorithms have been developed to analyze animal behavior. In this review, we provide an overview of the current state of technology for neuronal recordings with a focus on optical and electrophysiological methods in rodents. In addition, we discuss areas that future technological development will need to cover in order to further our understanding of the neural activity underlying behavior.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2020 ","pages":"7190517"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning.","authors":"Waleed Tahir, Sreekanth Kura, Jiabei Zhu, Xiaojun Cheng, Rafat Damseh, Fetsum Tadesse, Alex Seibel, Blaire S Lee, Frédéric Lesage, Sava Sakadžic, David A Boas, Lei Tian","doi":"10.34133/2020/8620932","DOIUrl":"10.34133/2020/8620932","url":null,"abstract":"<p><p><i>Objective and Impact Statement</i>. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. <i>Introduction</i>. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. <i>Methods</i>. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network's output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to <math><mn>808</mn><mo>×</mo><mn>808</mn><mo>×</mo><mn>702</mn><mtext> </mtext><mi>μ</mi><mtext>m</mtext></math>. <i>Results</i>. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. <i>Conclusion</i>. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2020 ","pages":"8620932"},"PeriodicalIF":5.0,"publicationDate":"2020-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional Photoacoustic and Ultrasonic Assessment of Osteoporosis: A Clinical Feasibility Study.","authors":"Ting Feng,&nbsp;Yunhao Zhu,&nbsp;Richard Morris,&nbsp;Kenneth M Kozloff,&nbsp;Xueding Wang","doi":"10.34133/2020/1081540","DOIUrl":"10.34133/2020/1081540","url":null,"abstract":"<p><p><i>Objective and Impact Statement</i>. To study the feasibility of combined functional photoacoustic (PA) and quantitative ultrasound (US) for diagnosis of osteoporosis <i>in vivo</i> based on the detection of chemical and microarchitecture (BMA) information in calcaneus bone. <i>Introduction</i>. Clinically available X-ray or US technologies for the diagnosis of osteoporosis do not report important parameters such as chemical information and BMA. With unique advantages, including good sensitivity to molecular and metabolic properties, PA bone assessment techniques hold a great potential for clinical translation. <i>Methods</i>. By performing multiwavelength PA measurements, the chemical information in the human calcaneus bone, including mineral, lipid, oxygenated-hemoglobin, and deoxygenated-hemoglobin, were assessed. In parallel, by performing PA spectrum analysis, the BMA as an important bone physical property was quantified. An unpaired <math><mi>t</mi></math>-test and a two-way ANOVA test were conducted to compare the outcomes from the two subject groups. <i>Results</i>. Multiwavelength PA measurement is capable of assessing the relative contents of several chemical components in the trabecular bone <i>in vivo</i>, including both minerals and organic materials such as oxygenated-hemoglobin, deoxygenated-hemoglobin, and lipid, which are relevant to metabolic activities and bone health. In addition, PA measurements of BMA show good correlations (<math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math> up to 0.65) with DEXA. Both the chemical and microarchitectural measurements from PA techniques can differentiate the two subject groups. <i>Conclusion</i>. The results from this initial clinical study suggest that PA techniques, by providing additional chemical and microarchitecture information relevant to bone health, may lead to accurate and early diagnosis, as well as sensitive monitoring of the treatment of osteoporosis.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2020 ","pages":"1081540"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Terahertz Imaging and Spectroscopy in Cancer Diagnostics: A Technical Review.","authors":"Yan Peng, Chenjun Shi, Xu Wu, Yiming Zhu, Songlin Zhuang","doi":"10.34133/2020/2547609","DOIUrl":"10.34133/2020/2547609","url":null,"abstract":"<p><p>Terahertz (THz) waves are electromagnetic waves with frequency in the range from 0.1 to 10 THz. THz waves have great potential in the biomedical field, especially in cancer diagnosis, because they exhibit low ionization energy and can be used to discern most biomolecules based on their spectral fingerprints. In this paper, we review the recent progress in two applications of THz waves in cancer diagnosis: imaging and spectroscopy. THz imaging is expected to help researchers and doctors attain a direct intuitive understanding of a cancerous area. THz spectroscopy is an efficient tool for component analysis of tissue samples to identify cancer biomarkers. Additionally, the advantages and disadvantages of the developed technologies for cancer diagnosis are discussed. Furthermore, auxiliary techniques that have been used to enhance the spectral signal-to-noise ratio (SNR) are also reviewed.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2020 ","pages":"2547609"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging Advances to Transform Histopathology Using Virtual Staining.","authors":"Yair Rivenson,&nbsp;Kevin de Haan,&nbsp;W Dean Wallace,&nbsp;Aydogan Ozcan","doi":"10.34133/2020/9647163","DOIUrl":"10.34133/2020/9647163","url":null,"abstract":"<p><p>In an age where digitization is widespread in clinical and preclinical workflows, pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides. Over the last decade, new high throughput digital scanning microscopes have ushered in the era of digital pathology that, along with recent advances in machine vision, have opened up new possibilities for Computer-Aided-Diagnoses. Despite these advances, the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly reimbursable step have challenged its widespread adoption. Here, we discuss how emerging virtual staining technologies and machine learning can help to disrupt the standard histopathology workflow and create new avenues for the diagnostic paradigm that will benefit patients and healthcare systems alike via digital pathology.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2020 ","pages":"9647163"},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning","authors":"Waleed Tahir, Sreekanth Kura, Jiabei Zhu, Xiaojun Cheng, R. Damseh, Fetsum Tadesse, Alex J. Seibel, Blaire S. Lee, F. Lesage, Sava Sakadžié, D. Boas, L. Tian","doi":"10.1101/2020.08.09.243394","DOIUrl":"https://doi.org/10.1101/2020.08.09.243394","url":null,"abstract":"Objective and Impact Statement Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems, or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems, and is able to segment large-scale angiograms. Methods We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and a total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in-vivo angiograms from mouse brains of dimensions up to 808×808×702 μm. Results To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope, and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72856218","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}

{"title":"Dual-Modality X-Ray-Induced Radiation Acoustic and Ultrasound Imaging for Real-Time Monitoring of Radiotherapy.","authors":"Wei Zhang,&nbsp;Ibrahim Oraiqat,&nbsp;Hao Lei,&nbsp;Paul L Carson,&nbsp;Issam Ei Naqa,&nbsp;Xueding Wang","doi":"10.34133/2020/9853609","DOIUrl":"10.34133/2020/9853609","url":null,"abstract":"<p><p><i>Objective</i>. The goal is to increase the precision of radiation delivery during radiotherapy by tracking the movements of the tumor and other surrounding normal tissues due to respiratory and other body motions. <i>Introduction</i>. This work presents the recent advancement of X-ray-induced radiation acoustic imaging (xRAI) technology and the evaluation of its feasibility for real-time monitoring of geometric and morphological misalignments of the X-ray field with respect to the target tissue by combining xRAI with established ultrasound (US) imaging, thereby improving radiotherapy tumor eradication and limiting treatment side effects. <i>Methods</i>. An integrated xRAI and B-mode US dual-modality system was established based on a clinic-ready research US platform. The performance of this dual-modality imaging system was evaluated via experiments on phantoms and ex <i>vivo</i> and <i>in vivo</i> rabbit liver models. <i>Results</i>. This system can alternatively switch between the xRAI and the US modes, with spatial resolutions of 1.1 mm and 0.37 mm, respectively. 300 times signal averaging was required for xRAI to reach a satisfactory signal-to-noise ratio, and a frame rate of 1.1 Hz was achieved with a clinical linear accelerator. The US imaging frame rate was 22 Hz, which is sufficient for real-time monitoring of the displacement of the target due to internal body motion. <i>Conclusion</i>. Our developed xRAI, in combination with US imaging, allows for mapping of the dose deposition in biological samples <i>in vivo</i>, in real-time, during radiotherapy. <i>Impact Statement</i>. The US-based image-guided radiotherapy system presented in this work holds great potential for personalized cancer treatment and better outcomes.</p>","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2020 ","pages":"9853609"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}