European Conference on Biomedical Optics最新文献

{"title":"Multi-modal functional sensorless adaptive optics for small animal retinal imaging","authors":"Jun Song, Yusi Miao, Joanne A. Matsubara, M. Sarunic, M. Ju","doi":"10.1117/12.2670968","DOIUrl":"https://doi.org/10.1117/12.2670968","url":null,"abstract":"The proposed preclinical study investigates pathological characteristics of retinal diseases such as Age-related Macular Degeneration (AMD) with transgenic small animal models using a multi-modal functional small animal retinal imaging system. For characterizing the animal models, we visualize the melanin concentration, lipofuscin accumulation, and choriocapillaris using a single imaging system. The system implements Polarization-Sensitive Optical Coherence Tomography (PS-OCT), fluorescence Scanning Laser Ophthalmoscope (fSLO), and Sensorless Adaptive Optics (SAO) for the visualizations of pathological features. As preliminary data, we acquired three different mice models and visualized the outer retinal thickness and melanin concentration. The newly developing system is expected to provide multilateral perspectives for further studies in AMD, enabling vision scientists to investigate the correlations between melanin, lipofuscin, and choriocapillaris for the root cause of AMD.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"55 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129156512","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":"Two-fold resolution increase and all-depth linearization using a neural network","authors":"Krzysztof A. Maliszewski, Sylwia M. Kolenderska","doi":"10.1117/12.2668845","DOIUrl":"https://doi.org/10.1117/12.2668845","url":null,"abstract":"A neural network is proposed as a much better performing alternative to Fourier transformation. It processes raw OCT spectra into A-scans with twice better nominal axial resolution which remains intact at all depths even for an uncalibrated spectrometer and uncompensated chromatic dispersion.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129809299","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":"Deep learning in photoacoustic tomography utilizing variational autoencoders","authors":"Teemu Sahlström, T. Tarvainen","doi":"10.1117/12.2670860","DOIUrl":"https://doi.org/10.1117/12.2670860","url":null,"abstract":"In Photoacoustic Tomography (PAT), the aim is to estimate the initial pressure distribution based on measured ultrasound data. While several approaches utilizing deep learning for PAT have been proposed, many of these do not provide estimates on the reliability of the reconstruction. In this work, we propose a deep learning approach for the Bayesian inverse problem for PAT based on the uncertainty quantification variational autoencoder. The approach enables simultaneous image reconstruction and reliability estimation.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"7 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123224217","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":"Advances in large area robotically assisted OCT (LARA-OCT): towards drive-by continuous motion imaging","authors":"M. Göb, S. Lotz, Linh Ha-Wissel, Sazgar Burhan, Sven Böttger, F. Ernst, J. Hundt, R. Huber","doi":"10.1117/12.2670950","DOIUrl":"https://doi.org/10.1117/12.2670950","url":null,"abstract":"Optical coherence tomography is a powerful imaging technique to visualize and localize depth-dependent tissue structure to differentiate between healthy and pathological conditions. However, conventional OCT systems are only capable of detecting small areas. To overcome this limitation, we have developed a large area robotically assisted OCT (LARA-OCT) system for automatic acquisition of large OCT images. Using mosaic pattern acquisition and subsequent stitching, we previously demonstrated initial in vivo OCT skin images beyond 10 cm². To improve acquisition speed and reduce dead times, we here demonstrate and analyze LARA-OCT with a new drive-by continuous motion imaging protocol.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"31 17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124171905","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":"Speculative/philosophical modeling of measurement for comprehending modern OCT imaging","authors":"Y. Yasuno","doi":"10.1117/12.2670898","DOIUrl":"https://doi.org/10.1117/12.2670898","url":null,"abstract":"Modern conception of Optical Coherence Tomography (OCT) measurement covers a wide range of measurement acts from photo-detection to Artificial-Intelligence (AI) based diagnosis. Here the measurement targets may include the structure, optical properties, tissue (by image processing and segmentation), and disease (by computer-aided diagnosis). Despite recent rapid expansion of the measurement targets, there is not theoretical (speculative) model to comprehend such wide spectrum of the measurement acts. Here we present a new speculative (theoretical) modeling of optical measurement, so-called “epistemological metrology model.” We use this model to critically understand several modern extensions of Optical Coherence Tomography (OCT), such as computer-aided diagnosis, Degree-of-Polarization-Uniformity (DOPU) imaging, and Attenuation-Coefficient (AC) imaging.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128524216","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":"Bias-Sensitive 128x128 hand-held TOBE ultrasound probe based on electrostrictive PMN-PT for photoacoustic applications (Withdrawal Notice)","authors":"Mohammad Rahim Sobhani, M. Ghavami, R. Zemp","doi":"10.1117/12.2671017","DOIUrl":"https://doi.org/10.1117/12.2671017","url":null,"abstract":"Publisher's Note: This paper, originally published on 11 August 2023, was withdrawn on 21 August 2023 per author request.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126858197","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":"Speed detection to suppress motion artifacts (MA) in laser speckle contrast imaging (LSCI)","authors":"Ata Chizari, Mirjam J Schaap, Tom Knop, M. Seyger, W. Steenbergen","doi":"10.1117/12.2672211","DOIUrl":"https://doi.org/10.1117/12.2672211","url":null,"abstract":"Laser Speckle Contrast Imaging (LSCI) is an optical technique for noninvasive assessment of microcirculatory blood flow. LSCI has a broad application in medicine including dermatology. Since laser speckles are the basis for this imaging modality, any external motions during a measurement from both patient and operator affect the blood flow images. This challenge is called Motion Artefacts (MA). Here, we propose a complete procedure for analysis of speckles, that is, pre-segmentation, segmentation, motion detection, spatial alignment, perfusion map calculation and MA suppression. The Handheld Perfusion Imager (HAPI) operated in both handheld and mounted schemes, has been used for measurements on 14 psoriasis subjects. The advantage of HAPI is use of a single monochromatic camera for both speckle imaging and motion detection. We make use of the black marker dots (made by the clinical investigator to determine visual psoriasis lesion boundary) for calculation of two-dimensional displacements of HAPI during each measurement (i.e. on-surface displacements). These on-surface displacements are integrated to translate each speckle image back to the initial position at the start of the measurement (i.e. spatial alignment). Furthermore, in handheld measurements, MA corrected blood flow maps (also called perfusion maps) are formed by extrapolation of a linear fit from local perfusion versus detected speed to the zero speed, that is, a value ideally always lower than the local mean perfusion. We show that our MA suppression technique makes handheld perfusion maps more similar to the associated mounted perfusion maps in term image histograms and mean values.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124382481","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":"Stereoscopic visualization of real-time intraoperative four-dimensional optical coherence tomography in ophthalmology","authors":"F. Kapeller, P. Matten, A. Britten, Michael Sommersberger, J. Nienhaus, K. L. Huber, W. Drexler, R. Leitgeb, A. Pollreisz, T. Schmoll","doi":"10.1117/12.2670857","DOIUrl":"https://doi.org/10.1117/12.2670857","url":null,"abstract":"Four-dimensional microscope integrated optical coherence tomography (4D-miOCT) has been proposed as an alternative to conventional white-light microscopy during ophthalmic surgical interventions. Its real-time visualization capability of 3D data constitutes one of the most promising visualization techniques for many surgical use cases in ophthalmology. In this work, we conducted a comprehensive user study for optimal visualization with the highest performance use of the 4D-miOCT data, comparing an autostereoscopic light field tablet to a 3D TV. With the feedback collected as part of a user study, we are able to further optimize how we display 4D-miOCT data to surgeons.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134325757","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":"Practical volumetric speckle reduction in OCT using deep learning","authors":"B. R. Chintada, Sebastián Ruiz-Lopera, R. Restrepo, M. Villiger, B. Bouma, N. Uribe-Patarroyo","doi":"10.1117/12.2670781","DOIUrl":"https://doi.org/10.1117/12.2670781","url":null,"abstract":"Speckle reduction has been an active topic of interest in the Optical Coherence Tomography (OCT) community and several techniques have been developed ranging from hardware-based methods, conventional image-processing and deep-learning based methods. The main goal of speckle reduction is to improve the diagnostic utility of OCT images by enhancing the image quality, thereby enhancing the visual interpretation of anatomical structures. We have previously introduced a probabilistic despeckling method based on non-local means for OCT—Tomographic Non-local-means despeckling (TNode). We demonstrated that this method efficiently suppresses speckle contrast while preserving tissue structures with dimensions approaching the system resolution. Despite the merits of this method, it is computationally very expensive: processing a typical retinal OCT volume takes a few hours. A much faster version of TNode with close to real-time performance, while keeping with the open source nature of TNode, could find much greater use in the OCT community. Deep learning despeckling methods have been proposed in OCT, including variants of conditional Generative Adversarial Networks (cGAN) and convolutional neural networks CNN. However, most of these methods have used B-scan compounding as a ground truth, which presents significant limitations in terms of speckle reduced tomograms with preservation of resolution. In addition, all these methods have focused on speckle suppression of individual B-scans, and their performance on volumetric tomograms is unclear: the expectation is that three-dimensional manipulations of these processed tomograms (i.e., en face projections) will contain artifacts due to the B-scan-wise processing, disrupting the continuity of tissue structures along the slow-scan axis. In addition, speckle suppression based on individual B-scans cannot provide the neural network with information on volumetric structures in the training data, and thus is expected to perform poorly on small structures. Indeed, most deep-learning despeckling works have focused on image quality metrics based on demonstrating strong speckle suppression, rather than focusing on preservation of contrast and small tissue structures. To overcome these problems, we propose an entire workflow to enable the wide-spread use of deep-learning speckle suppression in OCT: the ground-truth is generated using volumetric TNode despeckling, and the neural network uses a new cGAN that receives OCT partial volumes as inputs to utilize the three-dimensional structural information for speckle reduction. Because of its reliance on TNode for generating ground-truth data, this hybrid deep-learning–TNode (DL-TNode) framework will be made available to the OCT community to enable easy training and implementation in a multitude of OCT systems without relying on specialty-acquired training data.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134112662","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":"Multimodal optical coherence microscopy, mechano-microscopy, and fluorescence microscopy for three-dimensional characterization of multicellular spheroids","authors":"Alireza Mowla, Matt S. Hepburn, Jiayue Li, L. Hirvonen, Danielle Vahala, Sebastian E Amos, Samuel Maher, Yu Suk Choi, B. Kennedy","doi":"10.1117/12.2670830","DOIUrl":"https://doi.org/10.1117/12.2670830","url":null,"abstract":"Multicellular spheroids are a powerful model to study biochemical and biophysical interactions between cancer cells during growth and progression. However, little is known about how the biomechanics of the three-dimensional (3-D) microenvironment control cancer cell behaviors due to the lack of enabling technologies that can measure 3-D subcellular-scale elasticity and co-register it with the morphology and function of cells in a 3-D microenvironment. Here, we propose a multimodal imaging system that integrates an optical coherence microscopy-based subcellular mechano-microscopy system with a multi-channel confocal fluorescence microscopy system. Using this multimodal imaging system, we scan non-metastatic MCF7 breast cancer cell spheroids encapsulated in gelatin methacryloyl (GelMA) hydrogels and co-register 3-D intra-spheroid elasticity with subcellular structures, such as nuclei and cell membranes.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130113821","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}