Journal of Biomedical Optics最新文献

{"title":"Quantitative measurement of tympanic membrane structure and symmetry with optical coherence tomography in normal human subjects.","authors":"Zihan Yang, Marcela Moran Mojica, Wihan Kim, John S Oghalai, Brian E Applegate","doi":"10.1117/1.JBO.30.5.056007","DOIUrl":"https://doi.org/10.1117/1.JBO.30.5.056007","url":null,"abstract":"<p><strong>Significance: </strong>Early detection of ear pathology is essential for preventing hearing loss, yet the sensitivity of otoscopic examinations by primary care providers during annual physicals remains low. Optical coherence tomography (OCT) offers a promising alternative for detailed imaging of the tympanic membrane (TM) and middle ear (ME), providing the potential for early identification of ear disease.</p><p><strong>Aim: </strong>We aim to develop a quantitative method for assessing symmetry between the right and left ears and to establish a baseline for this approach in normal subjects.</p><p><strong>Approach: </strong>Volumetric OCT images were acquired from 12 normal subjects using a custom hand-held OCT otoscope. A volume registration and fusion method was applied to expand the TM field of view, followed by TM thickness measurement and generation of 3D thickness maps. The symmetry between left and right TMs was quantitatively analyzed using the Dice similarity coefficient.</p><p><strong>Results: </strong>The average TM thickness was measured as <math><mrow><mn>73.89</mn> <mo>±</mo> <mn>14.79</mn> <mtext>  </mtext> <mi>μ</mi> <mi>m</mi></mrow> </math> for left ears and <math><mrow><mn>70.72</mn> <mo>±</mo> <mn>11.58</mn> <mtext>  </mtext> <mi>μ</mi> <mi>m</mi></mrow> </math> for right ears, with no statistically significant difference at the 0.05 level. The symmetry analysis revealed a mean similarity coefficient of <math><mrow><mn>0.79</mn> <mo>±</mo> <mn>0.02</mn></mrow> </math> between left and right ears among the 12 normal subjects.</p><p><strong>Conclusions: </strong>OCT imaging enables quantitative assessment of TM thickness and symmetry, offering a baseline for identifying early ear pathologies.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 5","pages":"056007"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12077914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144078302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Speckle pattern analysis with deep learning for low-cost stroke detection: a phantom-based feasibility study.","authors":"Avraham Yosovich, Sergey Agdarov, Yafim Beiderman, Yevgeny Beiderman, Zeev Zalevsky","doi":"10.1117/1.JBO.30.5.056003","DOIUrl":"https://doi.org/10.1117/1.JBO.30.5.056003","url":null,"abstract":"<p><strong>Significance: </strong>Stroke is a leading cause of disability worldwide, necessitating rapid and accurate diagnosis to limit irreversible brain damage. However, many advanced imaging modalities (computerized tomography, magnetic resonance imaging) remain inaccessible in remote or resource-constrained settings due to high costs and logistical barriers.</p><p><strong>Aim: </strong>We aim to evaluate the feasibility of a laser speckle-based technique, coupled with deep learning, for detecting simulated stroke conditions in a tissue phantom. We investigate whether speckle patterns can be leveraged to differentiate healthy from restricted flow states in arteries of varying diameters and depths.</p><p><strong>Approach: </strong>Artificial arteries (3 to 6 mm diameters) were embedded at different depths (0 to 10 mm) within a skin-covered chicken tissue, to mimic blood-flow scenarios ranging from no flow (full occlusion) to high flow. A high-speed camera captured the secondary speckle patterns generated by laser illumination. These video sequences were fed into a three-dimensional convolutional neural network (X3D_M) to classify four distinct flow conditions.</p><p><strong>Results: </strong>The proposed method showed high classification accuracy, reaching 95% to 100% for larger vessels near the surface. Even for smaller or deeper arteries, detection remained robust ( <math><mrow><mo>></mo> <mn>80</mn> <mo>%</mo></mrow> </math> in most conditions). The performance suggests that spatiotemporal features of speckle patterns can reliably distinguish varying blood-flow states.</p><p><strong>Conclusions: </strong>Although tested on a tissue phantom, these findings highlight the potential of combining speckle imaging with deep learning for accessible, rapid stroke detection. Our next steps involve direct <i>in vivo</i> experiments targeting cerebral arteries, acknowledging that additional factors such as the skull's optical properties and the likely need for near-infrared illumination must be addressed before achieving true intracranial applicability. We also note that examining the carotid artery <i>in vivo</i> remains a valuable and practical step, given its superficial location and direct relevance to stroke risk.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 5","pages":"056003"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12058334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144020390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cherenkov light emission in external beam radiation therapy of the larynx.","authors":"Jigar Dubal, Pedro Arce, Chris South, Lucia Florescu","doi":"10.1117/1.JBO.30.5.055002","DOIUrl":"10.1117/1.JBO.30.5.055002","url":null,"abstract":"<p><strong>Significance: </strong>Cherenkov light emitted in the tissue during radiation therapy enables unprecedented approaches to tumor functional imaging for early treatment assessment. Cherenkov light-based tomographic imaging requires image reconstruction algorithms based on internal light sources that, in turn, require knowledge about the characteristics of the Cherenkov light within the patient.</p><p><strong>Aim: </strong>We aim to investigate the spatial and spectral characteristics of Cherenkov light within the patient and at the patient's surface, and the origin within the tissue of light reaching the surface, to provide insight for the development of image reconstruction algorithms for Cherenkov light-based tomographic imaging.</p><p><strong>Approach: </strong>Numerical experiments using clinical patient data and Monte Carlo simulations are performed for the radiation therapy of laryngeal cancer for intensity-modulated radiation therapy and volumetric-modulated arc radiation therapy.</p><p><strong>Results: </strong>The emitted Cherenkov light is concentrated in regions of high delivered dose, with the spatial distribution within the patient and at the patient's surface depending on the treatment type and patient anatomy. The Cherenkov light at the patient's surface is dominant in the near-infrared spectral region. Light emitted within the tumor emerges at the patient's surface on a well-defined radiation beam-independent region. The distribution within the patient of the emitted light that emerges on reduced areas on the patient's surface containing this region is similar to that of the light that emerges across the entire patient's surface.</p><p><strong>Conclusions: </strong>Detailed information about the spectral and spatial characteristics of Cherenkov light is provided. In addition, these results suggest that surface light measurements restricted to smaller areas containing the region where the light emitted in the tumor emerges (that can be determined through simulations prior to the treatment) could enable probing the tumor while being easier to integrate with the radiotherapy system and while the effect of measurement data incompleteness on image reconstruction may not be too strong.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 5","pages":"055002"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120355/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144181646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-assisted diffuse correlation tomography for identifying breast cancer.","authors":"Ruizhi Zhang, Jianju Lu, Wenqi Di, Zhiguo Gui, Shun Wan Chan, Fengbao Yang, Yu Shang","doi":"10.1117/1.JBO.30.5.055001","DOIUrl":"10.1117/1.JBO.30.5.055001","url":null,"abstract":"<p><strong>Significance: </strong>Diffuse correlation tomography (DCT) is an emerging technique for the noninvasive measurement of breast microvascular blood flow, whereas its capability to categorize benign and malignant breast lesions has not been extensively validated thus far, due to the difficulties in instrumentation, image reconstruction algorithms, and appropriate approaches for imaging analyses.</p><p><strong>Aim: </strong>This artificial intelligence (AI)-assisted DCT instrumentation was constructed based on a unique source-detector array and image reconstruction algorithm.</p><p><strong>Approach: </strong>The DCT images of breasts were obtained from 61 females, and AI models were utilized to classify breast lesions. During this process, the blood flow images were either extracted as feature parameters or as global inputs to the AI models.</p><p><strong>Results: </strong>As the validations of DCT instrumentation, the blood flow images obtained from longitudinal monitoring of healthy subjects demonstrated the stability of DCT measurements. For patients with breast diseases, comprehensive analyses yield an AI-assisted classification with excellent performance for distinguishing between benign and malignant breast lesions, at an accuracy of 97%.</p><p><strong>Conclusions: </strong>The AI-assisted DCT reflects functional abnormalities that are associated with cancellous-induced high metabolic demands, thus demonstrating the great potential for early diagnosis and timely therapeutic assessment of breast cancer, e.g., prior to the tumor formation or proliferation of microvascular networks.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 5","pages":"055001"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Depth-resolved imaging in turbid media via Mueller matrix polarimetry.","authors":"Xinxian Zhang, Jiahao Fan, Jiawei Song, Nan Zeng, Honghui He, Valery V Tuchin, Hui Ma","doi":"10.1117/1.JBO.30.5.056009","DOIUrl":"https://doi.org/10.1117/1.JBO.30.5.056009","url":null,"abstract":"<p><strong>Significance: </strong>Polarimetry offers advantages such as high information dimensionality and sensitivity to microstructures. Determining the depth of the tissue is essential for clinical diagnosis and treatment, such as lesion localization, removal, and drug delivery. However, relying solely on polarization techniques for tissue depth measurement remains a subject for further investigation.</p><p><strong>Aim: </strong>We aim to investigate the tissue depth measurement in turbid media using Mueller matrix polarimetry, with a focus on fibrous tissues.</p><p><strong>Approach: </strong>Tissue phantoms are constructed to quantitatively simulate fibrosis at specific depth. By analyzing Mueller matrix measurements across depth gradients, correlations between polarization basic parameters (PBPs) and tissue depth are established using supervised machine learning algorithms.</p><p><strong>Results: </strong>We introduce an approach by combining degree of polarization (DOP)-sensitive PBPs with anisotropy-sensitive PBPs to develop depth-sensitive polarization feature parameters (DSPFPs). The DSPFPs exhibit enhanced sensitivity to depth in shallow layers while preserving accuracy in deeper layers. The effectiveness and robustness of the proposed method are validated through 2D depth-resolved imaging of tissue phantoms.</p><p><strong>Conclusions: </strong>We preliminarily explore the feasibility of depth measurement using Mueller matrix polarimetry, establishing a method for tissue depth assessment while also expanding the applications of polarimetry.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 5","pages":"056009"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081163/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144078296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward handheld optically guided biopsy combining diffuse reflectance spectroscopy and autofluorescence.","authors":"Lotte M de Roode, Simon T Sørensen, Stefan D van der Stel, Lisanne L de Boer, Yineng Wang, Huihui Lu, Stefan Andersson-Engels, Theo J M Ruers, Ray Burke","doi":"10.1117/1.JBO.30.5.057001","DOIUrl":"https://doi.org/10.1117/1.JBO.30.5.057001","url":null,"abstract":"<p><strong>Significance: </strong>We aim to validate the technologies essential for a handheld, optically guided biopsy device designed to enhance the diagnostic yield and accuracy of percutaneous liver biopsy procedures.</p><p><strong>Aim: </strong>We aim to combine diffuse reflectance spectroscopy (DRS) and autofluorescence (AF) spectroscopy in a fiber-optic needle probe using mini spectrometers for the classification of tumor and healthy tissues.</p><p><strong>Approach: </strong>A fiber-optic needle probe combining DRS and AF spectroscopy, incorporating mini spectrometers to facilitate future integration into a biopsy actuator, was designed and built. This custom probe was used to measure healthy liver tissues and colorectal metastases in excised liver segments. A linear discriminant analysis was applied to the DRS and AF data to distinguish tumors from healthy tissues.</p><p><strong>Results: </strong>The miniaturized combined DRS and AF spectroscopy system could accurately distinguish tumors from healthy liver with a sensitivity of 95% and a specificity of 96% from a sample size of <math><mrow><mi>N</mi> <mo>=</mo> <mn>10</mn></mrow> </math> patients and 52 measurements.</p><p><strong>Conclusions: </strong>We demonstrate the feasibility of miniaturizing a combined DRS and AF spectroscopy system for the classification of tumor and healthy tissues. This validation supports the feasibility and further development of a handheld, optically guided biopsy device.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 5","pages":"057001"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144063909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum: Elucidating the effect of tumor and background region-of-interest selection on the performance metrics used to assess fluorescence imaging (Erratum).","authors":"Augustino V Scorzo, Caleb Y Kwon, Rendall R Strawbridge, P Jack Hoopes, David W Roberts, Scott C Davis","doi":"10.1117/1.JBO.30.5.059801","DOIUrl":"https://doi.org/10.1117/1.JBO.30.5.059801","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1117/1.JBO.30.4.046004.].</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 5","pages":"059801"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12101533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144142269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep structural brain imaging via computational three-photon microscopy.","authors":"Lingmei Chen, Mubin He, Lu Yang, Lingxi Zhou, Shuhao Qian, Chuncheng Wang, Rushan Jiang, Zhihua Ding, Jun Qian, Zhiyi Liu","doi":"10.1117/1.JBO.30.4.046002","DOIUrl":"10.1117/1.JBO.30.4.046002","url":null,"abstract":"<p><strong>Significance: </strong>High-resolution optical imaging at significant depths is challenging due to scattering, which impairs image quality in living matter with complex structures. We address the need for improved imaging techniques in deep tissues.</p><p><strong>Aim: </strong>We aim to develop a computational deep three-photon microscopy (3PM) method that enhances image quality without compromising acquisition speed, increasing excitation power, or adding extra optical components.</p><p><strong>Approach: </strong>We introduce a method called low-rank diffusion model (LRDM)-3PM, which utilizes customized aggregation-induced emission nanoprobes and self-supervised deep learning. This approach leverages superficial information from three-dimensional (3D) images to compensate for scattering and structured noise from the imaging system.</p><p><strong>Results: </strong>LRDM-3PM achieves a remarkable signal-to-background ratio above 100 even at depths of 1.5 mm, enabling the imaging of the hippocampus in live mouse brains. It integrates with a multiparametric analysis platform for resolving morpho-structural features of brain vasculature in a completely 3D manner, accurately recognizing distinct brain regions.</p><p><strong>Conclusions: </strong>LRDM-3PM demonstrates the potential for minimally invasive <i>in vivo</i> imaging and analysis, offering a significant advancement in the field of deep tissue imaging by maintaining high-resolution quality at unprecedented depths.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 4","pages":"046002"},"PeriodicalIF":3.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-channel pulse-dye densitometry can enable correction of fluorescent targeted and control agent plasma input function differences for quantitative paired-agent molecular imaging: a simulation study.","authors":"Cody C Rounds, Yichen Feng, Sanjana Pannem, Jovan Brankov, Kimberly S Samkoe, Kenneth M Tichauer","doi":"10.1117/1.JBO.30.4.046001","DOIUrl":"10.1117/1.JBO.30.4.046001","url":null,"abstract":"<p><strong>Significance: </strong>Paired-agent fluorescent molecular imaging approaches involve co-administration of a control (untargeted) imaging agent with a molecularly targeted agent to account for non-specific effects and quantify binding potential (BP)-a parameter proportional to the concentration of the targeted biomolecule. Accurate BP estimation often requires correction for differences in targeted and control agent plasma input functions (PIFs).</p><p><strong>Aim: </strong>We provide a simulation-based evaluation of whether dual-channel pulse dye densitometry (PDD) can be used to measure the PIFs of co-administered targeted and control imaging agents, to enable accurate BP estimation.</p><p><strong>Approach: </strong>Monte-Carlo simulations of light propagation were carried out using the anatomy and optical properties of a finger, as well as experimentally measured PIFs of co-administered anti-epidermal growth factor receptor fluorescent affibody, ABY-029, and IRDye 680LT, a control imaging agent from past mouse experiments. The accuracy of PIF shape estimation from PDD and PIF difference correction was evaluated by assessing BP estimation accuracy in a simulated \"tumor\" tissue.</p><p><strong>Results: </strong>\"Tumor\" BP measurements using deconvolution correction with noise-free PIFs versus PDD-measured PIFs were compared. The relative error in PDD PIF deconvolution BP estimation was <math><mrow><mn>2</mn> <mo>±</mo> <mn>1</mn> <mo>%</mo></mrow> </math> . No statistical difference was found between the estimated BP via deconvolution correction with true PIFs and the estimated BP via the reconstructed PIFs using the proposed PAF-PDD methodology.</p><p><strong>Conclusions: </strong>These results highlight the potential for developing a PDD instrument that can directly measure targeted and control agent PIFs and be used to correct for any PIF differences between agents for more quantitative estimates of BP in paired-agent imaging studies.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 4","pages":"046001"},"PeriodicalIF":3.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Choice of numerical implementation of spatial contrast calculation impacts microcirculation quantitation in laser speckle contrast imaging.","authors":"Marc Chammas, Frédéric Pain","doi":"10.1117/1.JBO.30.4.046006","DOIUrl":"https://doi.org/10.1117/1.JBO.30.4.046006","url":null,"abstract":"<p><strong>Significance: </strong>Laser speckle contrast imaging (LSCI) allows noninvasive imaging of microcirculation. Its scope of clinical applications is growing, yet the literature lacks a comparison of the accuracy of methods used to compute the spatial contrast <math> <mrow><msub><mi>K</mi> <mi>s</mi></msub> </mrow> </math> from which the blood flow index is derived.</p><p><strong>Aim: </strong>We aim to evaluate the impact on flow quantitation of different computational approaches used to derive <math> <mrow><msub><mi>K</mi> <mi>s</mi></msub> </mrow> </math> .</p><p><strong>Approach: </strong>We compare numerical calculation of <math> <mrow><msub><mi>K</mi> <mi>s</mi></msub> </mrow> </math> in Python and ImageJ applied to noise-free simulated data and to experimental data acquired <i>in vivo</i> in anesthetized mice. The estimation of the decorrelation time <math> <mrow><msub><mi>τ</mi> <mi>c</mi></msub> </mrow> </math> , inversely proportional to the blood flow index, is carried out following two approaches: LSCI asymptotic estimation and fitting the multiple exposure speckle imaging (MESI) model to <math> <mrow><msub><mi>K</mi> <mi>s</mi></msub> <mo>(</mo> <mi>T</mi> <mo>)</mo></mrow> </math> .</p><p><strong>Results: </strong>For simulation data, we found variations of up to 58% for the blood flow index in the LSCI approach. Nonlinear fitting of the MESI model was less affected with discrepancies of only a few percent. Considering experimental data, the LSCI approximation led to <math> <mrow><msub><mi>K</mi> <mi>s</mi></msub> </mrow> </math> with relative differences (up to 35%) depending on the calculation methods. The noise and limited exposure time strongly limited the accuracy of the LSCI asymptotic estimation. Adjustment of the MESI model to the data led to consistent values of <math> <mrow><msub><mi>τ</mi> <mi>c</mi></msub> </mrow> </math> in the 0.05 to 1 ms range with significant variations depending on the method used to calculate <math> <mrow><msub><mi>K</mi> <mi>s</mi></msub> </mrow> </math> .</p><p><strong>Conclusions: </strong>Numerical methods used to calculate <math> <mrow><msub><mi>K</mi> <mi>s</mi></msub> </mrow> </math> should be precisely acknowledged and validated against direct calculation to ensure accuracy. <i>Uniform</i> filter approach leads to accurate <math> <mrow><msub><mi>K</mi> <mi>s</mi></msub> </mrow> </math> values and is 100 times more computationally efficient than the <math><mrow><mi>D</mi> <mi>i</mi> <mi>r</mi> <mi>e</mi> <mi>c</mi> <mi>t</mi></mrow> </math> calculation. Other investigated methods lead to various levels of errors in flow index estimation using LSCI. Errors are minimized using larger kernels. MESI derivation of <math> <mrow><msub><mi>τ</mi> <mi>c</mi></msub> </mrow> </math> is not immune but less affected by such methodological biases.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 4","pages":"046006"},"PeriodicalIF":3.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12003051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143993470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}