Journal of Biomedical Optics最新文献

Significance: Optical coherence tomography (OCT) has emerged as the standard of care for diagnosing and monitoring the treatment of various ocular disorders due to its noninvasive nature and in vivo volumetric acquisition capability. Despite its widespread applications in ophthalmology, motion artifacts remain a challenge in OCT imaging, adversely impacting image quality. While several multivolume registration algorithms have been developed to address this issue, they are often designed to cater to one specific OCT system or acquisition protocol.

Aim: We aim to generate an OCT volume free of motion artifacts using a system-agnostic registration algorithm that is independent of system specifications or protocol.

Approach: We developed a B-scan registration algorithm that removes motion and corrects for both translational eye movements and rotational angle differences between volumes. Tests were carried out on various datasets obtained from two different types of custom-built OCT systems and one commercially available system to determine the reliability of the proposed algorithm. Additionally, different system specifications were used, with variations in axial resolution, lateral resolution, signal-to-noise ratio, and real-time motion tracking. The accuracy of this method has further been evaluated through mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM).

Results: The results demonstrate improvements in the overall contrast of the images, facilitating detailed visualization of retinal vasculatures in both superficial and deep vasculature plexus. Finer features of the inner and outer retina, such as photoreceptors and other pathology-specific features, are discernible after multivolume registration and averaging. Quantitative analyses affirm that increasing the number of averaged registered volumes will decrease MSE and increase MS-SSIM as compared to the reference volume.

Conclusions: The multivolume registered data obtained from this algorithm offers significantly improved visualization of the retinal microvascular network as well as retinal morphological features. Furthermore, we have validated that the versatility of our methodology extends beyond specific OCT modalities, thereby enhancing the clinical utility of OCT for the diagnosis and monitoring of ocular pathologies.

Significance: The emergence of label-free microscopy techniques has significantly improved our ability to precisely characterize biochemical targets, enabling non-invasive visualization of cellular organelles and tissue organization. However, understanding each label-free method with respect to the specific benefits, drawbacks, and varied sensitivities under measurement conditions across different types of specimens remains a challenge.

Aim: We link all of these disparate label-free optical interactions together and compare the detection sensitivity within the framework of statistical estimation theory.

Approach: To achieve this goal, we introduce a comprehensive unified framework for evaluating the bounds for signal detection with label-free microscopy methods, including second-harmonic generation, third-harmonic generation, coherent anti-Stokes Raman scattering, coherent Stokes Raman scattering, stimulated Raman loss, stimulated Raman gain, stimulated emission, impulsive stimulated Raman scattering, transient absorption, and photothermal effect. A general model for signal generation induced by optical scattering is developed.

Results: Based on this model, the information obtained is quantitatively analyzed using Fisher information, and the fundamental constraints on estimation precision are evaluated through the Cramér-Rao lower bound, offering guidance for optimal experimental design and interpretation.

Conclusions: We provide valuable insights for researchers seeking to leverage label-free techniques for non-invasive imaging applications for biomedical research and clinical practice.

Significance: Near-infrared spectroscopy (NIRS) is a non-invasive optical method that measures changes in hemoglobin concentration and oxygenation. The measured light intensity is susceptible to reduced signal quality due to the presence of melanin.

Aim: We quantify the influence of melanin concentration on NIRS measurements taken with a frequency-domain near-infrared spectroscopy system using 690 and 830 nm.

Approach: Using a forehead NIRS probe, we measured 35 healthy participants and investigated the correlation between melanin concentration indices, which were determined using a colorimeter, and several key metrics from the NIRS signal. These metrics include signal-to-noise ratio (SNR), two measurements of oxygen saturation (arterial oxygen saturation, ${\mathrm{SpO}}_2$ , and tissue oxygen saturation, ${\mathrm{StO}}_2$ ), and optical properties represented by the absorption coefficient ( ${\mu }_a$ ) and the reduced scattering coefficient ( ${\mu }_s^{\text{'}}$ ).

Results: We found a significant negative correlation between the melanin index and the SNR estimated in oxy-hemoglobin signals ( $r_s=-0.489$ , $p=0.006$ ) and ${\mathrm{SpO}}_2$ levels ( $r_s=-0.413$ , $p=0.023$ ). However, no significant changes were observed in the optical properties and ${\mathrm{StO}}_2$ ( $r_s=-0.146$ , $p=0.44$ ).

Conclusions: We found that estimated SNR and ${\mathrm{SpO}}_2$ values show a significant decline and dependence on the melanin index, whereas ${\mathrm{StO}}_2$ and optical properties do not show any correlation with the melanin index.

Significance: Of patients with early-stage breast cancer, 60% to 75% undergo breast-conserving surgery. Of those, 20% or more need a second surgery because of an incomplete tumor resection only discovered days after surgery. An intraoperative imaging technology allowing cancer detection on the margins of breast specimens could reduce re-excision procedure rates and improve patient survival.

Aim: We aimed to develop an experimental protocol using hyperspectral line-scanning Raman spectroscopy to image fresh breast specimens from cancer patients. Our objective was to determine whether macroscopic specimen images could be produced to distinguish invasive breast cancer from normal tissue structures.

Approach: A hyperspectral inelastic scattering imaging instrument was used to interrogate eight specimens from six patients undergoing breast cancer surgery. Machine learning models trained with a different system to distinguish cancer from normal breast structures were used to produce tissue maps with a field-of-view of $1{\mathrm{cm}}^2$ classifying each pixel as either cancer, adipose, or other normal tissues. The predictive model results were compared with spatially correlated histology maps of the specimens.

Results: A total of eight specimens from six patients were imaged. Four of the hyperspectral images were associated with specimens containing cancer cells that were correctly identified by the new ex vivo pathology technique. The images associated with the remaining four specimens had no histologically detectable cancer cells, and this was also correctly predicted by the instrument.

Conclusions: We showed the potential of hyperspectral Raman imaging as an intraoperative breast cancer margin assessment technique that could help surgeons improve cosmesis and reduce the number of repeat procedures in breast cancer surgery.

Significance: The accurate correlation between optical measurements and pathology relies on precise image registration, often hindered by deformations in histology images. We investigate an automated multi-modal image registration method using deep learning to align breast specimen images with corresponding histology images.

Aim: We aim to explore the effectiveness of an automated image registration technique based on deep learning principles for aligning breast specimen images with histology images acquired through different modalities, addressing challenges posed by intensity variations and structural differences.

Approach: Unsupervised and supervised learning approaches, employing the VoxelMorph model, were examined using a dataset featuring manually registered images as ground truth.

Results: Evaluation metrics, including Dice scores and mutual information, demonstrate that the unsupervised model exceeds the supervised (and manual) approaches significantly, achieving superior image alignment. The findings highlight the efficacy of automated registration in enhancing the validation of optical technologies by reducing human errors associated with manual registration processes.

Conclusions: This automated registration technique offers promising potential to enhance the validation of optical technologies by minimizing human-induced errors and inconsistencies associated with manual image registration processes, thereby improving the accuracy of correlating optical measurements with pathology labels.

Significance: Digital holographic microscopy (DHM) is a label-free microscopy technique that provides time-resolved quantitative phase imaging (QPI) by measuring the optical path delay of light induced by transparent biological samples. DHM has been utilized for various biomedical applications, such as cancer research and sperm cell assessment, as well as for in vitro drug or toxicity testing. Its lensless version, digital lensless holographic microscopy (DLHM), is an emerging technology that offers size-reduced, lightweight, and cost-effective imaging systems. These features make DLHM applicable, for example, in limited resource laboratories, remote areas, and point-of-care applications.

Aim: In addition to the abovementioned advantages, in-line arrangements for DLHM also include the limitation of the twin-image presence, which can restrict accurate QPI. We therefore propose a compact lensless common-path interferometric off-axis approach that is capable of quantitative imaging of fast-moving biological specimens, such as living cells in flow.

Approach: We suggest lensless spatially multiplexed interferometric microscopy (LESSMIM) as a lens-free variant of the previously reported spatially multiplexed interferometric microscopy (SMIM) concept. LESSMIM comprises a common-path interferometric architecture that is based on a single diffraction grating to achieve digital off-axis holography. From a series of single-shot off-axis holograms, twin-image free and time-resolved QPI is achieved by commonly used methods for Fourier filtering-based reconstruction, aberration compensation, and numerical propagation.

Results: Initially, the LESSMIM concept is experimentally demonstrated by results from a resolution test chart and investigations on temporal stability. Then, the accuracy of QPI and capabilities for imaging of living adherent cell cultures is characterized. Finally, utilizing a microfluidic channel, the cytometry of suspended cells in flow is evaluated.

Conclusions: LESSMIM overcomes several limitations of in-line DLHM and provides fast time-resolved QPI in a compact optical arrangement. In summary, LESSMIM represents a promising technique with potential biomedical applications for fast imaging such as in imaging flow cytometry or sperm cell analysis.

Significance: Damage to the cardiac conduction system remains one of the most significant risks associated with surgical interventions to correct congenital heart disease. This work demonstrates how light-scattering spectroscopy (LSS) can be used to non-destructively characterize cardiac tissue regions.

Aim: To present an approach for associating tissue composition information with location-specific LSS data and further evaluate an LSS and machine learning system as a method for non-destructive tissue characterization.

Approach: A custom LSS probe was used to gather spectral data from locations across 14 excised human pediatric nodal tissue samples (8 sinus nodes, 6 atrioventricular nodes). The LSS spectra were used to train linear and neural-network-based regressor models to predict tissue composition characteristics derived from the 3D models.

Results: Nodal tissue region nuclear densities were reported. A linear model trained to regress nuclear density from spectra achieved a prediction r-squared of 0.64 and a concordance correlation coefficient of 0.78.

Conclusions: These methods build on previous studies suggesting that LSS measurements combined with machine learning signal processing can provide clinically relevant cardiac tissue composition.

Significance: Biomanufacturing utilizes modified microbial systems to sustainably produce commercially important biomolecules for use in agricultural, energy, food, material, and pharmaceutical industries. However, technological challenges related to non-destructive and high-throughput metabolite screening need to be addressed to fully unlock the potential of synthetic biology and sustainable biomanufacturing.

Aim: This perspective outlines current analytical screening tools used in industrial cell strain development programs and introduces label-free vibrational spectro-microscopy as an alternative contrast mechanism.

Approach: We provide an overview of the analytical instrumentation currently used in the "test" portion of the design, build, test, and learn cycle of synthetic biology. We then highlight recent progress in Raman scattering and infrared absorption imaging techniques, which have enabled improved molecular specificity and sensitivity.

Results: Recent developments in high-resolution chemical imaging methods allow for greater throughput without compromising the image contrast. We provide a roadmap of future work needed to support integration with microfluidics for rapid screening at the single-cell level.

Conclusions: Quantifying the net expression of metabolites allows for the identification of cells with metabolic pathways that result in increased biomolecule production, which is essential for improving the yield and reducing the cost of industrial biomanufacturing. Technological advancements in vibrational microscopy instrumentation will greatly benefit biofoundries as a complementary approach for non-destructive cell screening.

Significance: Accurate cell segmentation and classification in three-dimensional (3D) images are vital for studying live cell behavior and drug responses in 3D tissue culture. Evaluating diverse cell populations in 3D cell culture over time necessitates non-toxic staining methods, as specific fluorescent tags may not be suitable, and immunofluorescence staining can be cytotoxic for prolonged live cell cultures.

Aim: We aim to perform machine learning-based cell classification within a live heterogeneous cell culture population grown in a 3D tissue culture relying only on reflectance, transmittance, and nuclei counterstained images obtained by confocal microscopy.

Approach: In this study, we employed a supervised convolutional neural network (CNN) to classify tumor cells and fibroblasts within 3D-grown spheroids. These cells are first segmented using the marker-controlled watershed image processing method. Training data included nuclei counterstaining, reflectance, and transmitted light images, with stained fibroblast and tumor cells as ground-truth labels.

Results: Our results demonstrate the successful marker-controlled watershed segmentation of 84% of spheroid cells into single cells. We achieved a median accuracy of 67% (95% confidence interval of the median is 65-71%) in identifying cell types. We also recapitulate the original 3D images using the CNN-classified cells to visualize the original 3D-stained image's cell distribution.

Conclusion: This study introduces a non-invasive toxicity-free approach to 3D cell culture evaluation, combining machine learning with confocal microscopy, opening avenues for advanced cell studies.

Significance: Hematogenous metastasis is mediated by circulating tumor cells (CTCs) and CTC clusters (CTCCs). We recently developed "diffuse in vivo flow cytometry" (DiFC) to detect fluorescent protein (FP) expressing CTCs in small animals. Extending DiFC to allow detection of two FPs simultaneously would allow concurrent study of different CTC sub-populations or heterogeneous CTCCs in the same animal.

Aim: The goal of this work was to develop and validate a two-color DiFC system capable of non-invasively detecting circulating cells expressing two distinct FPs.

Approach: A DiFC instrument was designed and built to detect cells expressing either green FP (GFP) or tdTomato. We tested the instrument in tissue-mimicking flow phantoms in vitro and in multiple myeloma bearing mice in vivo.

Results: In phantoms, we could accurately differentiate GFP+ and tdTomato+ CTCs and CTCCs. In tumor-bearing mice, CTC numbers expressing both FPs increased during disease. Most CTCCs (86.5%) expressed single FPs with the remainder both FPs. These data were supported by whole-body hyperspectral fluorescence cryo-imaging of the mice.

Conclusions: We showed that two-color DiFC can detect two populations of CTCs and CTCCs concurrently. This instrument could allow study of tumor development and response to therapies for different sub-populations in the same animal.