Journal of Biomedical Optics最新文献

Significance: Optical coherence tomography (OCT) is widely utilized to investigate brain activities and disorders in anesthetized or restrained rodents. However, anesthesia can alter several physiological parameters, leading to findings that might not fully represent the true physiological state. To advance the understanding of brain function in awake and freely moving animals, the development of wearable OCT probes is crucial.

Aim: We aim to address the challenge of insufficient depth of field (DOF) in wearable OCT probes for brain imaging in freely moving mice, ensuring high lateral resolution while capturing brain vasculature across varying heights.

Approach: We integrated diffractive optical elements (DOEs) capable of generating beams with an extended DOF into a wearable OCT probe. This design effectively overcomes the traditional trade-off between lateral resolution and DOF, enabling the capture of detailed angiographic images in a dynamic and uncontrolled environment.

Results: The enhanced wearable OCT probe achieved a lateral resolution superior to $8\mu m$ within a $450\mu m$ axial range. This setup allowed for high-resolution optical coherence tomography angiography (OCTA) imaging with extended DOF, making it suitable for studying brain vasculature in freely moving mice.

Conclusions: The incorporation of DOEs into the wearable OCT probe represents a significant advancement in wearable biomedical imaging. This technology facilitates the acquisition of high-resolution angiographic images with an extended DOF, thus enhancing the ability to study brain function in awake and naturally behaving animals.

Significance: Maximal safe resection of brain tumors can be performed by neurosurgeons through the use of accurate and practical guidance tools that provide real-time information during surgery. Current established adjuvant intraoperative technologies include neuronavigation guidance, intraoperative imaging (MRI and ultrasound), and 5-ALA for fluorescence-guided surgery.

Aim: We have developed intraoperative Raman spectroscopy as a real-time decision support system for neurosurgical guidance in brain tumors. Using a machine learning model, trained on data from a multicenter clinical study involving 67 patients, the device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. Here, the aim is to assess the generalizability of a predictive model trained with data from this study to other types of brain tumors.

Approach: A method was developed to assess the generalizability of the model, quantifying performance for tumors including astrocytoma, oligodendroglioma and ependymoma, pediatric glioblastoma, and classification of glioblastoma data acquired in the presence of 5-ALA induced fluorescence. Statistical analyses were conducted to assess the impact of vibrational bands beyond contributors identified in our previous research.

Results: A machine learning brain tumor detection model showed a positive predictive value (PPV) of 70% for astrocytoma, 74% for oligodendroglioma, and 100% for ependymoma. Furthermore, the PPV was 100% in classifying spectra from a pediatric glioblastoma and 90% for detecting adult glioblastoma labeled with 5-ALA-induced fluorescence. Univariate statistical analyses applied to individual vibrational bands demonstrated that the inclusion of Raman biomarkers unexploited to date had the potential to improve detectability, setting the stage for future advances.

Conclusions: Developing predictive models relying on the inelastic scattering contrast from a wider pool of Raman bands may improve detection accuracy for astrocytoma and oligodendroglioma. To do so, larger tumor datasets and a higher Raman photon signal-to-noise ratio may be required.

Significance: Laparoscopic surgery is generally unavailable in low- and middle-income countries (LMICs) due to the high cost of installation and lack of qualified personnel to maintain and repair equipment. We developed a low-cost, durable, reusable laparoscopic system, called the KeyScope laparoscope, for use in LMICs. To reliably build and service the KeyScope in LMICs, a portable testing chamber (PTC) is needed to assess image performance.

Aim: A PTC was developed to characterize KeyScope laparoscope performance in LMICs.

Approach: Images of standard resolution, color accuracy, distortion, and depth of field (DOF) targets were captured in both a standard optical bench setup (OBS) and the PTC. Measurements from the OBS and PTC were quantified and compared using standard software (ImageJ and Imatest). To further reduce cost, alternative paper imaging targets were identified and compared with standard glass targets. To improve usability, MATLAB applications (apps) were developed to automate image analysis and reduce cost.

Results: The PTC achieved similar results compared to the OBS for the image quality metrics, distortion and DOF. Further, the PTC presented similar results to the OBS for resolution at 4 to 7 cm working distances and improved resolution at periphery working distances of 3 and 10 cm. Color accuracy values were also improved in the PTC compared with those measured in the OBS. The low-cost resolution, color accuracy, and distortion targets resulted in similar image quality results to the standard image quality target. MATLAB apps produced similar results to Imatest and ImageJ software and decreased the time to complete image quality test analysis.

Conclusion: The low-cost portable design of the PTC will facilitate the translation of the KeyScope by enabling accurate and fast characterization of laparoscopic imaging performance in LMICs.

Significance: The spatial distribution of the photosensitizing drug concentration is an important parameter for predicting the photodynamic therapy (PDT) outcome. Current diffuse fluorescence tomography methods lack accuracy in quantifying drug concentration. The development of accurate methods for monitoring the temporal evolution of the drug distribution in tissue can advance the real-time light dosimetry in PDT of tumors, leading to better treatment outcomes.

Aim: We develop diffuse optical tomography methods based on interstitial fluorescence measurements to accurately reconstruct the spatial distribution of fluorescent photosensitizing drugs in real-time.

Approach: A two-stage reconstruction algorithm is proposed. The capabilities and limitations of this method are studied in various simulated scenarios. For the first time, experimental validation is conducted using the clinical system for interstitial PDT of prostate cancer on prostate tissue-mimicking phantoms with the photosensitizer verteporfin.

Results: The average relative error of the reconstructed fluorophore absorption was less than 10%, whereas the fluorescent inclusion reconstructed volume relative error was less than 35%.

Conclusions: The proposed method can be used to monitor the temporal evolution of the photosensitizing drug concentration in tumor tissue during photodynamic therapy. This is an important step forward in the development of the next generation of real-time light dosimetry algorithms for photodynamic therapy.

Significance: ALA-PpIX and second-window indocyanine green (ICG) have been studied widely for guiding the resection of high-grade gliomas. These agents have different mechanisms of action and uptake characteristics, which can affect their performance as surgical guidance agents. Elucidating these differences in animal models that approach the size and anatomy of the human brain would help guide the use of these agents. Herein, we report on the use of a new pig glioma model and fluorescence cryotomography to evaluate the 3D distributions of both agents throughout the whole brain.

Aim: We aim to assess and compare the 3D spatial distributions of ALA-PpIX and second-window ICG in a glioma-bearing pig brain using fluorescence cryotomography.

Approach: A glioma was induced in the brain of a transgenic Oncopig via adeno-associated virus delivery of Cre-recombinase plasmids. After tumor induction, the pro-drug 5-ALA and ICG were administered to the animal 3 and 24 h prior to brain harvest, respectively. The harvested brain was imaged using fluorescence cryotomography. The fluorescence distributions of both agents were evaluated in 3D in the whole brain using various spatial distribution and contrast performance metrics.

Results: Significant differences in the spatial distributions of both agents were observed. Indocyanine green accumulated within the tumor core, whereas ALA-PpIX appeared more toward the tumor periphery. Both ALA-PpIX and second-window ICG provided elevated tumor-to-background contrast (13 and 23, respectively).

Conclusions: This study is the first to demonstrate the use of a new glioma model and large-specimen fluorescence cryotomography to evaluate and compare imaging agent distribution at high resolution in 3D.

Significance: Personalized photodynamic therapy (PDT) treatment planning requires knowledge of the spatial and temporal co-localization of photons, photosensitizers (PSs), and oxygen. The inter- and intra-subject variability in the photosensitizer concentration can lead to suboptimal outcomes using standard treatment plans.

Aim: We aim to quantify the PS spatial variation in tumors and its effect on PDT treatment planning solutions.

Approach: The spatial variability of two PSs is imaged at various spatial resolutions for an orthotopic rat glioma model and applied in silico to human glioblastoma models to determine the spatial PDT dose, including in organs at risk. An open-source interstitial photodynamic therapy (iPDT) planning tool is applied to these models, deriving the spatial photosensitizer quantification resolution that consistently impacts iPDT source placement and power allocation.

Results: The ex vivo studies revealed a bimodal photosensitizer distribution in the tumor. The concentration of the PS can vary by a factor of 2 between the tumor core and rim, with slight variation within the core but a factor of 5 in the rim. An average sampling volume of $1{\mathrm{mm}}^3$ for photosensitizer quantification will result in significantly different iPDT planning solutions for each case.

Conclusions: Assuming homogeneous photosensitizer distribution results in suboptimal therapeutic outcomes, we highlight the need to predict the photosensitizer distribution before source placement for effective treatment plans.

Significance: Fluorescence sensing within tissue is an effective tool for tissue characterization; however, the modality and geometry of the image acquisition can alter the observed signal.

Aim: We introduce a novel optical fiber-based system capable of measuring two fluorescent contrast agents through 2 cm of tissue with simple passive electronic switching between the excitation light, simultaneously acquiring fluorescence and excitation data. The goal was to quantify indocyanine green (ICG) and protoporphyrin IX (PpIX) within tissue, and the sampling method was compared with wide-field surface imaging to contrast the value of deep sensing versus surface imaging.

Approach: This was achieved by choosing filters for specific wavelengths that were mutually exclusive between ICG and PpIX and coupling these filters to two separate detectors, which allows for direct swapping of the excitation and emission channels by switching the on-time of each excitation laser between 780- and 633-nm wavelengths.

Results: This system was compared with two non-contact surface imaging systems for both ICG and PpIX, which revealed that the fluorescence depth sensing system was superior in its ability to resolve kinetics differences in deeper tissues that would normally be dominated by strong signals from skin and other surface tissues. Specifically, the system was tested using pancreatic adenocarcinoma tumors injected into murine models, which were imaged at several time points throughout tumor growth to its $\sim 6\text{-}\mathrm{mm}$ diameter. This demonstrated the system's capability to track longitudinal changes in ICG and PpIX kinetics that result from tumor growth and development, with larger tumors showing sluggish uptake and clearance of ICG, which was not observable with surface imaging. Similarly, PpIX was quantified, which showed slower kinetics over different time points, and was further compared with the wide-filed imager. These results were further validated through depth measurements in tissue phantoms and model-based interpretation.

Conclusion: This fluorescence depth sensing system can be used to sample the interior blood flow characteristics by ICG sensing of tissue as deep as 20 mm into the tissue with sensitivity to kinetics that are superior to surface imaging and may be combined with other imaging modalities such as ultrasound to provide guided deep fluorescence measurements.

Significance: Imaging flow cytometry allows highly informative multi-point cell analysis for biological assays and medical diagnosis. Rapid processing of the imaged cells during flow allows real-time classification and sorting of the cells. Off-axis holography enables imaging flow cytometry without chemical cell staining but requires digital processing to the optical path delay profile for each frame before the cells can be classified, which slows down the overall processing throughput. We present a method for real-time cell classification via label-free quantitative imaging flow cytometry using digital holography, offering a comprehensive representation of cellular structures, without the need for digital processing before automatic cell classification.

Aim: We aim to develop an automatic cell classification scheme based directly on the off-axis holographic projections of the cells during flow and test it for stain-free imaging flow cytometry of white blood cells.

Approach: After building a dedicated off-axis holographic microscopy system for acquiring white blood cells during flow, we apply deep-learning classification directly in the off-axis hologram space, rather than in the quantitative phase profile space. This way, we simplify computational processes and allow a significant increase in the cell classification throughput. In addition, by utilizing multiple-viewpoint holographic projections of the cells rotated during flow, instead of using a single projection, we obtain better classification results due to the additional cellular information gained.

Results: Our technique demonstrates increasing accuracy with additional viewpoint holographic projections from the optical system, achieving a 7.69% improvement when processing ten interferometric projections compared with a single interferometric projection (regular off-axis hologram). Our technique also outperforms using multiple optical path delay profile projections, requiring off-axis holographic digital preprocessing, by 17.95%, because the holographic projections are analyzed directly without preprocessing and includes the amplitude information as well.

Conclusions: Our cell classification approach has great potential for high-throughput, high-content, label-free imaging flow cytometry for classification of large-scale cellular datasets and real-time cell classification during flow in clinical settings.

Significance: Existing photoacoustic phantoms are unable to mimic complex microvascular structures with varying sizes and distributions. A suitable material with structures that mimic intricate microvascular networks is needed.

Aim: Our aim is to introduce loofah as a natural phantom material with complex fiber networks ranging from 50 to $300\mu m$ , enabling the fabrication of phantoms with controlled optical properties comparable to those of human microvasculature.

Approach: By introducing a controllable chromophore into the loofah material, we controlled its absorption properties. The loofah's vasculature-mimetic capabilities and stability in photoacoustic signal generation were evaluated using co-registered ultrasound, acoustic-resolution photoacoustic microscopy (ARPAM), and optical-resolution photoacoustic microscopy (ORPAM).

Results: ORPAM results confirmed the loofah's ability to control chromophore distribution, leading to consistent and regulated photoacoustic signals. ARPAM results demonstrated that the loofah phantom effectively replicates vascular structures, exhibiting superior performance in mimicking microvascular networks compared with commonly used tissue-mimetic phantoms. The dominant diameter range of the phantom's microvasculature was between 100 and $250\mu m$ , aligning well with the targeted range and facilitating meaningful comparisons with human vascular structures.

Conclusions: The loofah material provides a low-cost and effective method for creating submillimeter microvascular phantoms for photoacoustic imaging. Its exceptional morphology and customizability allow it to be shaped into various vascular network configurations, enhancing the fidelity of phantom imaging and assisting in system calibration and validation. In addition, data obtained from this realistic microvascular phantom can offer greater opportunities for training machine learning models.

Significance: In the last years, time-resolved near-infrared spectroscopy (TD-NIRS) has gained increasing interest as a tool for studying tissue spectroscopy with commercial devices. Although it provides much more information than its continuous wave counterpart, accurate models interpreting the measured raw data in real time are still lacking.

Aim: We introduce an analytical model that can be integrated and used in TD-NIRS data processing software and toolkits in real time. This is based on the so-called sensitivity factors (SFs) of the distributions of time of flight (DTOFs) of photons measured in optically turbid and semi-infinite multilayered media, such as the human head.

Approach: We derived analytical expressions for the SFs that link changes in the absorption coefficient of each layer to changes in the statistical moments of DTOFs acquired in a reflectance configuration. This was later validated with results from Monte Carlo (MC) simulations, which stand as the gold standard in terms of photon migration in biological tissue. Next, we designed a couple of simulated experiments depicting how the analytical SFs can be used to retrieve absorption changes in the particular case of a five-layered medium.

Results: Comparison between theory and simulations in 2-, 5-, and 10-layered media showed very good agreement (in most cases with weighted mean absolute percentage errors below 10%). Moreover, our derivations could be run in a few milliseconds (except for the extreme case of the variance SF in the 10-layered medium), which means a speedup of up to 10,000× with respect to MC simulations, with a much better spatial resolution and without their typically associated stochastic noise.

Conclusions: In summary, our method achieves performances similar to those given by MC simulations, but orders of magnitude faster, which makes it very suitable for its implementation in real-time applications.