Journal of Biomedical Optics最新文献

{"title":"Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery.","authors":"Jeeseong Hwang, Philip Cheney, Stephen C Kanick, Hanh N D Le, David M McClatchy, Helen Zhang, Nian Liu, Zhan-Qian John Lu, Tae Joon Cho, Kimberly Briggman, David W Allen, Wendy A Wells, Brian W Pogue","doi":"10.1117/1.JBO.29.9.093503","DOIUrl":"10.1117/1.JBO.29.9.093503","url":null,"abstract":"<p><strong>Significance: </strong>Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries.</p><p><strong>Aim: </strong>We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples.</p><p><strong>Approach: </strong>Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the <math><mrow><mi>K</mi></mrow></math>-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes.</p><p><strong>Results: </strong>The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised <math><mrow><mi>K</mi></mrow></math>-means algorithm. The unsupervised <math><mrow><mi>K</mi></mrow></math>-means algorithm, with no <i>a priori</i> information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within <math><mrow><mo><</mo><mn>2</mn><mo>%</mo></mrow></math> residual error margin.</p><p><strong>Conclusions: </strong>Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to <math><mrow><mo><</mo><mn>2</mn><mo>%</mo></mrow></math> residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or va","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"29 9","pages":"093503"},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11075096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140876462","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":"Relationship between depth-wise refractive index and biomechanical properties of human articular cartilage.","authors":"Bilour Khan, Ervin Nippolainen, Fatemeh Shahini, Nonappa, Alexey Popov, Juha Töyräs, Isaac O Afara","doi":"10.1117/1.JBO.29.9.095003","DOIUrl":"10.1117/1.JBO.29.9.095003","url":null,"abstract":"<p><strong>Significance: </strong>Optical properties of biological tissues, such as refractive index (RI), are fundamental properties, intrinsically linked to the tissue's composition and structure. We hypothesize that, as the RI and the functional properties of articular cartilage (AC) are dependent on the tissue's structure and composition, the RI of AC is related to its biomechanical properties.</p><p><strong>Aim: </strong>This study aims to investigate the relationship between RI of human AC and its biomechanical properties.</p><p><strong>Approach: </strong>Human cartilage samples ( <math><mrow><mi>n</mi> <mo>=</mo> <mn>22</mn></mrow> </math> ) were extracted from the right knee joint of three cadaver donors (one female, aged 47 years, and two males, aged 64 and 68 years) obtained from a commercial biobank (Science Care, Phoenix, Arizona, United States). The samples were initially subjected to mechanical indentation testing to determine elastic [equilibrium modulus (EM) and instantaneous modulus (IM)] and dynamic [dynamic modulus (DM)] viscoelastic properties. An Abbemat 3200 automatic one-wavelength refractometer operating at 600 nm was used to measure the RI of the extracted sections. Similarly, Spearman's and Pearson's correlation coefficients were employed for non-normal and normal datasets, respectively, to determine the correlation between the depth-wise RI and biomechanical properties of the cartilage samples as a function of the collagen fibril orientation.</p><p><strong>Results: </strong>A positive correlation with statistically significant relations ( <math><mrow><mi>p</mi> <mo>-</mo> <mtext>values</mtext> <mo><</mo> <mn>0.05</mn></mrow> </math> ) was observed between the RI and the biomechanical properties (EM, IM, and DM) along the tissue depth for each zone, e.g., superficial, middle, and deep zones. Likewise, a lower positive correlation with statistically significant relations ( <math><mrow><mi>p</mi> <mo>-</mo> <mtext>values</mtext> <mo><</mo> <mn>0.05</mn></mrow> </math> ) was also observed for collagen fibril orientation of all zones with the biomechanical properties.</p><p><strong>Conclusions: </strong>The results indicate that, although the RI exhibits different levels of correlation with different biomechanical properties, the relationship varies as a function of the tissue depth. This knowledge paves the way for optically monitoring changes in AC biomechanical properties nondestructively via changes in the RI. Thus, the RI could be a potential biomarker for assessing the mechanical competency of AC, particularly in degenerative diseases, such as osteoarthritis.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"29 9","pages":"095003"},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413650/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288117","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":"Spectral analysis comparison of pushbroom and snapshot hyperspectral cameras for <i>in vivo</i> brain tissues and chromophore identification.","authors":"Alberto Martín-Pérez, Alejandro Martinez de Ternero, Alfonso Lagares, Eduardo Juarez, César Sanz","doi":"10.1117/1.JBO.29.9.093510","DOIUrl":"https://doi.org/10.1117/1.JBO.29.9.093510","url":null,"abstract":"<p><strong>Significance: </strong>Hyperspectral imaging sensors have rapidly advanced, aiding in tumor diagnostics for <i>in vivo</i> brain tumors. Linescan cameras effectively distinguish between pathological and healthy tissue, whereas snapshot cameras offer a potential alternative to reduce acquisition time.</p><p><strong>Aim: </strong>Our research compares linescan and snapshot hyperspectral cameras for <i>in vivo</i> brain tissues and chromophore identification.</p><p><strong>Approach: </strong>We compared a linescan pushbroom camera and a snapshot camera using images from 10 patients with various pathologies. Objective comparisons were made using unnormalized and normalized data for healthy and pathological tissues. We utilized the interquartile range (IQR) for the spectral angle mapping (SAM), the goodness-of-fit coefficient (GFC), and the root mean square error (RMSE) within the 659.95 to 951.42 nm range. In addition, we assessed the ability of both cameras to capture tissue chromophores by analyzing absorbance from reflectance information.</p><p><strong>Results: </strong>The SAM metric indicates reduced dispersion and high similarity between cameras for pathological samples, with a 9.68% IQR for normalized data compared with 2.38% for unnormalized data. This pattern is consistent across GFC and RMSE metrics, regardless of tissue type. Moreover, both cameras could identify absorption peaks of certain chromophores. For instance, using the absorbance measurements of the linescan camera, we obtained SAM values below 0.235 for four peaks, regardless of the tissue and type of data under inspection. These peaks are one for cytochrome b in its oxidized form at <math><mrow><mi>λ</mi> <mo>=</mo> <mn>422</mn> <mtext>  </mtext> <mi>nm</mi></mrow> </math> , two for <math> <mrow> <msub><mrow><mi>HbO</mi></mrow> <mrow><mn>2</mn></mrow> </msub> </mrow> </math> at <math><mrow><mi>λ</mi> <mo>=</mo> <mn>542</mn> <mtext>  </mtext> <mi>nm</mi></mrow> </math> and <math><mrow><mi>λ</mi> <mo>=</mo> <mn>576</mn> <mtext>  </mtext> <mi>nm</mi></mrow> </math> , and one for water at <math><mrow><mi>λ</mi> <mo>=</mo> <mn>976</mn> <mtext>  </mtext> <mi>nm</mi></mrow> </math> .</p><p><strong>Conclusion: </strong>The spectral signatures of the cameras show more similarity with unnormalized data, likely due to snapshot sensor noise, resulting in noisier signatures post-normalization. Comparisons in this study suggest that snapshot cameras might be viable alternatives to linescan cameras for real-time brain tissue identification.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"29 9","pages":"093510"},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11420787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142347391","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":"Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering.","authors":"Thomas T Livecchi, Steven L Jacques, Hrebesh M Subhash, Mark C Pierce","doi":"10.1117/1.JBO.29.9.093507","DOIUrl":"10.1117/1.JBO.29.9.093507","url":null,"abstract":"<p><strong>Significance: </strong>Hyperspectral cameras capture spectral information at each pixel in an image. Acquired spectra can be analyzed to estimate quantities of absorbing and scattering components, but the use of traditional fitting algorithms over megapixel images can be computationally intensive. Deep learning algorithms can be trained to rapidly analyze spectral data and can potentially process hyperspectral camera data in real time.</p><p><strong>Aim: </strong>A hyperspectral camera was used to capture <math><mrow><mn>1216</mn> <mo>×</mo> <mn>1936</mn> <mtext>  pixel</mtext></mrow> </math> wide-field reflectance images of <i>in vivo</i> human tissue at 205 wavelength bands from 420 to 830 nm.</p><p><strong>Approach: </strong>The optical properties of oxyhemoglobin, deoxyhemoglobin, melanin, and scattering were used with multi-layer Monte Carlo models to generate simulated diffuse reflectance spectra for 24,000 random combinations of physiologically relevant tissue components. These spectra were then used to train an artificial neural network (ANN) to predict tissue component concentrations from an input reflectance spectrum.</p><p><strong>Results: </strong>The ANN achieved low root mean square errors in a test set of 6000 independent simulated diffuse reflectance spectra while calculating concentration values more than 4000× faster than a conventional iterative least squares approach.</p><p><strong>Conclusions: </strong><i>In vivo</i> finger occlusion and gingival abrasion studies demonstrate the ability of this approach to rapidly generate high-resolution images of tissue component concentrations from a hyperspectral dataset acquired from human subjects.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"29 9","pages":"093507"},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11378079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154206","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":"Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks.","authors":"Minh Ha Tran, Ling Ma, Hasan Mubarak, Ofelia Gomez, James Yu, Michelle Bryarly, Baowei Fei","doi":"10.1117/1.JBO.29.9.093505","DOIUrl":"10.1117/1.JBO.29.9.093505","url":null,"abstract":"<p><strong>Significance: </strong>Hyperspectral imaging (HSI) is an emerging imaging modality for oncological applications and can improve cancer detection with digital pathology.</p><p><strong>Aim: </strong>The study aims to highlight the increased accuracy and sensitivity of detecting the margin of thyroid carcinoma in hematoxylin and eosin (H&E)-stained histological slides using HSI and data augmentation methods.</p><p><strong>Approach: </strong>Using an automated microscopic imaging system, we captured 2599 hyperspectral images from 65 H&E-stained human thyroid slides. Images were then preprocessed into 153,906 image patches of dimension <math><mrow><mn>250</mn> <mo>×</mo> <mn>250</mn> <mo>×</mo> <mn>84</mn> <mtext>  pixels</mtext></mrow> </math> . We modified the TimeSformer network architecture, which used alternating spectral attention and spatial attention layers. We implemented several data augmentation methods for HSI based on the RandAugment algorithm. We compared the performances of TimeSformer on HSI against the performances of pretrained ConvNext and pretrained vision transformers (ViT) networks on red, green, and blue (RGB) images. Finally, we applied attention unrolling techniques on the trained TimeSformer network to identify the biological features to which the network paid attention.</p><p><strong>Results: </strong>In the testing dataset, TimeSformer achieved an accuracy of 90.87%, a weighted <math> <mrow><msub><mi>F</mi> <mn>1</mn></msub> </mrow> </math> score of 89.79%, a sensitivity of 91.50%, and an area under the receiving operator characteristic curve (AU-ROC) score of 97.04%. Additionally, TimeSformer produced thyroid carcinoma tumor margins with an average Jaccard score of 0.76 mm. Without data augmentation, TimeSformer achieved an accuracy of 88.23%, a weighted <math> <mrow><msub><mi>F</mi> <mn>1</mn></msub> </mrow> </math> score of 86.46%, a sensitivity of 85.53%, and an AU-ROC score of 94.94%. In comparison, the ViT network achieved an 89.98% accuracy, an 88.14% weighted <math> <mrow><msub><mi>F</mi> <mn>1</mn></msub> </mrow> </math> score, an 84.77% sensitivity, and a 96.17% AU-ROC. Our visualization results showed that the network paid attention to biological features.</p><p><strong>Conclusions: </strong>The TimeSformer model trained with hyperspectral histological data consistently outperformed conventional RGB-based models, highlighting the superiority of HSI in this context. Our proposed augmentation methods improved the accuracy, the <math> <mrow><msub><mi>F</mi> <mn>1</mn></msub> </mrow> </math> score, and the sensitivity score.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"29 9","pages":"093505"},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11268383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141758976","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":"All-reflective tethered capsule endoscope for multimodal optical coherence tomography in the esophagus.","authors":"Xavier Attendu, Paul R Bloemen, Niels H Kind, Dirk J Faber, Daniel M de Bruin, Caroline Boudoux, Ton G van Leeuwen","doi":"10.1117/1.JBO.29.9.096003","DOIUrl":"https://doi.org/10.1117/1.JBO.29.9.096003","url":null,"abstract":"<p><strong>Significance: </strong>Esophageal cancer is becoming increasingly prevalent in Western countries. Early detection is crucial for effective treatment. Multimodal imaging combining optical coherence tomography (OCT) with complementary optical imaging techniques may provide enhanced diagnostic capabilities by simultaneously assessing tissue morphology and biochemical content.</p><p><strong>Aim: </strong>We aim to develop a tethered capsule endoscope (TCE) that can accommodate a variety of point-scanning techniques in addition to OCT without requiring design iterations on the optical or mechanical design.</p><p><strong>Approach: </strong>We propose a TCE utilizing exclusively reflective optics to focus and steer light from and to a double-clad fiber. Specifically, we use an ellipsoidal mirror to achieve finite conjugation between the fiber tip and the imaging plane.</p><p><strong>Results: </strong>We demonstrate a functional all-reflective TCE. We first detail the design, fabrication, and assembly steps required to obtain such a device. We then characterize its performance and demonstrate combined OCT at 1300 nm and visible spectroscopic imaging in the 500- to 700-nm range. Finally, we discuss the advantages and limitations of the proposed design.</p><p><strong>Conclusions: </strong>An all-reflective TCE is feasible and allows for achromatic high-quality imaging. Such a device could be utilized as a platform for testing various combinations of modalities to identify the optimal candidates without requiring design iterations.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"29 9","pages":"096003"},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11412323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288116","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 learning–enabled fluorescence imaging for surgical guidance: in silico training for oral cancer depth quantification","authors":"Natalie J. Won, Mandolin Bartling, Josephine La Macchia, Stefanie Markevich, Scott Holtshousen, Arjun Jagota, Christina Negus, Esmat Najjar, Brian C. Wilson, Jonathan C. Irish, Michael J. Daly","doi":"10.1117/1.jbo.30.s1.s13706","DOIUrl":"https://doi.org/10.1117/1.jbo.30.s1.s13706","url":null,"abstract":"SignificanceOral cancer surgery requires accurate margin delineation to balance complete resection with post-operative functionality. Current in vivo fluorescence imaging systems provide two-dimensional margin assessment yet fail to quantify tumor depth prior to resection. Harnessing structured light in combination with deep learning (DL) may provide near real-time three-dimensional margin detection.AimA DL-enabled fluorescence spatial frequency domain imaging (SFDI) system trained with in silico tumor models was developed to quantify the depth of oral tumors.ApproachA convolutional neural network was designed to produce tumor depth and concentration maps from SFDI images. Three in silico representations of oral cancer lesions were developed to train the DL architecture: cylinders, spherical harmonics, and composite spherical harmonics (CSHs). Each model was validated with in silico SFDI images of patient-derived tongue tumors, and the CSH model was further validated with optical phantoms.ResultsThe performance of the CSH model was superior when presented with patient-derived tumors (P-value<0.05). The CSH model could predict depth and concentration within 0.4 mm and 0.4 μg/mL, respectively, for in silico tumors with depths less than 10 mm.ConclusionsA DL-enabled SFDI system trained with in silico CSH demonstrates promise in defining the deep margins of oral tumors.","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"44 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues.","authors":"Wihan Adi, Bryan E Rubio Perez, Yuming Liu, Sydney Runkle, Kevin W Eliceiri, Filiz Yesilkoy","doi":"10.1117/1.JBO.29.9.093511","DOIUrl":"10.1117/1.JBO.29.9.093511","url":null,"abstract":"<p><strong>Significance: </strong>Label-free multimodal imaging methods that can provide complementary structural and chemical information from the same sample are critical for comprehensive tissue analyses. These methods are specifically needed to study the complex tumor-microenvironment where fibrillar collagen's architectural changes are associated with cancer progression. To address this need, we present a multimodal computational imaging method where mid-infrared spectral imaging (MIRSI) is employed with second harmonic generation (SHG) microscopy to identify fibrillar collagen in biological tissues.</p><p><strong>Aim: </strong>To demonstrate a multimodal approach where a morphology-specific contrast mechanism guides an MIRSI method to detect fibrillar collagen based on its chemical signatures.</p><p><strong>Approach: </strong>We trained a supervised machine learning (ML) model using SHG images as ground truth collagen labels to classify fibrillar collagen in biological tissues based on their mid-infrared hyperspectral images. Five human pancreatic tissue samples (sizes are in the order of millimeters) were imaged by both MIRSI and SHG microscopes. In total, 2.8 million MIRSI spectra were used to train a random forest (RF) model. The other 68 million spectra were used to validate the collagen images generated by the RF-MIRSI model in terms of collagen segmentation, orientation, and alignment.</p><p><strong>Results: </strong>Compared with the SHG ground truth, the generated RF-MIRSI collagen images achieved a high average boundary <math><mrow><mi>F</mi></mrow> </math> -score (0.8 at 4-pixel thresholds) in the collagen distribution, high correlation (Pearson's <math><mrow><mi>R</mi></mrow> </math> 0.82) in the collagen orientation, and similarly high correlation (Pearson's <math><mrow><mi>R</mi></mrow> </math> 0.66) in the collagen alignment.</p><p><strong>Conclusions: </strong>We showed the potential of ML-aided label-free mid-infrared hyperspectral imaging for collagen fiber and tumor microenvironment analysis in tumor pathology samples.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"29 9","pages":"093511"},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11448345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371949","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":"Impact of scattering phase function and polarization on the accuracy of diffuse and sub-diffuse spatial frequency domain imaging.","authors":"Alec B Walter, E Duco Jansen","doi":"10.1117/1.JBO.29.9.095001","DOIUrl":"10.1117/1.JBO.29.9.095001","url":null,"abstract":"<p><strong>Significance: </strong>Although spatial frequency domain imaging (SFDI) has been well characterized under diffuse optical conditions, tissue measurements made outside the diffuse regime can provide new diagnostic information. Before such measurements can become clinically relevant, however, the behavior of sub-diffuse SFDI and its effect on the accuracy of derived tissue parameters must be assessed.</p><p><strong>Aim: </strong>We aim to characterize the impact that both the assumed scattering phase function (SPF) and the polarization state of the illumination light source have on the accuracy of SFDI-derived optical properties when operating under diffuse or sub-diffuse conditions, respectively.</p><p><strong>Approach: </strong>Through the use of a set of well-characterized optical phantoms, SFDI accuracy was assessed at four wavelengths (395, 545, 625, and 850 nm) and two different spatial frequencies (0.3 and <math><mrow><mn>1.0</mn> <mtext>  </mtext> <msup><mrow><mi>mm</mi></mrow> <mrow><mo>-</mo> <mn>1</mn></mrow> </msup> </mrow> </math> ), which provided a broad range of diffuse and sub-diffuse conditions, using three different SPFs. To determine the effects of polarization, the SFDI accuracy was assessed using both unpolarized and cross-polarized illumination.</p><p><strong>Results: </strong>It was found that the assumed SPF has a direct and significant impact on the accuracy of the SFDI-derived optical properties, with the best choice of SPF being dictated by the polarization state. As unpolarized SFDI retains the sub-diffuse portion of the signal, optical properties were found to be more accurate when using the full SPF that includes forward and backscattering components. By contrast, cross-polarized SFDI yielded accurate optical properties when using a forward-scattering SPF, matching the behavior of cross-polarization to attenuate the immediate backscattering of sub-diffuse reflectance. Using the correct pairings of SPF and polarization enabled using a reflectance standard, instead of a more subjective phantom, as the reference measurement.</p><p><strong>Conclusions: </strong>These results provide the foundation for a more thorough understanding of SFDI and enable new applications of this technology in which sub-diffuse conditions dominate (e.g., <math> <mrow> <msub><mrow><mi>μ</mi></mrow> <mrow><mi>a</mi></mrow> </msub> <mo>≮</mo> <msubsup><mrow><mi>μ</mi></mrow> <mrow><mi>s</mi></mrow> <mrow><mo>'</mo></mrow> </msubsup> </mrow> </math> ) or high spatial frequencies are required.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"29 9","pages":"095001"},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11379407/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154207","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 detection limit of a fluorescent object in tissue-like medium with background emission in continuous-wave measurements: a phantom study.","authors":"Goro Nishimura, Takahiro Suzuki, Yukio Yamada, Haruki Niwa, Takuji Koike","doi":"10.1117/1.JBO.29.9.097001","DOIUrl":"10.1117/1.JBO.29.9.097001","url":null,"abstract":"<p><strong>Significance: </strong>Although the depth detection limit of fluorescence objects in tissue has been studied, reports with a model including noise statistics for designing the optimum measurement configuration are missing. We demonstrate a variance analysis of the depth detection limit toward clinical applications such as noninvasively assessing the risk of aspiration.</p><p><strong>Aim: </strong>It is essential to analyze how the depth detection limit of the fluorescence object in a strong scattering medium depends on the measurement configuration to optimize the configuration. We aim to evaluate the depth detection limit from theoretical analysis and phantom experiments and discuss the source-detector distance that maximizes this limit.</p><p><strong>Approach: </strong>Experiments for detecting a fluorescent object in a biological tissue-mimicking phantom of ground beef with background emission were conducted using continuous wave fluorescence measurements with a point source-detector scheme. The results were analyzed using a model based on the photon diffusion equations. Then, variance analysis of the signal fluctuation was introduced.</p><p><strong>Results: </strong>The model explained the measured fluorescence intensities and their fluctuations well. The variance analysis showed that the depth detection limit in the presence of ambient light increased with the decrease in the source-detector distance, and the optimum distance was in the range of 10 to 15 mm. The depth detection limit was found to be <math><mrow><mo>∼</mo> <mn>30</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> with this optimum distance for the phantom.</p><p><strong>Conclusions: </strong>The presented analysis provides a guide for the optimum design of the measurement configuration for detecting fluorescence objects in clinical applications.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"29 9","pages":"097001"},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142119932","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}