Journal of Biomedical Optics最新文献

Significance: Conventionally, spectral photoacoustic imaging (sPAI) to assess tissue oxygenation ( ${\mathrm{sO}}_2$ ) uses optical wavelengths in the first near-infrared (NIR-I) window. This limits the maximum photoacoustic imaging depth due to the high spectral coloring of biological tissues and has been a major barrier to the clinical translation of the technique.

Aim: We demonstrate the second near-infrared (NIR-II) tissue optical window (950 to 1400 nm) for the assessment of blood and tissue ${\mathrm{sO}}_2$ .

Approach: The NIR-II PA spectra of oxygenated and deoxygenated hemoglobin were first characterized using a phantom. Optimal wavelengths to minimize spectral coloring were identified. The resulting NIR-II PA imaging methods were then validated in vivo by measuring kidney ${\mathrm{sO}}_2$ in adult female rats.

Results: sPAI of whole blood, in a phantom, and of blood in kidneys in vivo produced PA spectra proportional to wavelength-dependent optical absorption. Using the NIR-II wavelengths for spectral unmixing resulted in a $\sim 50\%$ decrease in the error of the estimated blood ${\mathrm{sO}}_2$ , compared with conventional NIR-I wavelengths. In vivo measurements of kidney ${\mathrm{sO}}_2$ validated these findings, with a similar 50% reduction in error when using NIR-II wavelengths versus NIR-I wavelengths at larger illumination depths.

Conclusions: sPAI using NIR-II wavelengths improved the accuracy of tissue ${\mathrm{sO}}_2$ measurements. This is likely due to reduced scattering, which reduces the attenuation and, therefore, the impact of spectral coloring in this wavelength range. Combined with the increased safe skin exposure fluence limits in this wavelength range, these results demonstrate the potential to use NIR-II wavelengths for quantitative sPAI of ${\mathrm{sO}}_2$ from deep heterogeneous tissues.

Significance: Near-infrared autofluorescence (NIRAF) utilizes the natural autofluorescence of parathyroid glands (PGs) to improve their identification during thyroid surgeries, reducing the risk of inadvertent removal and subsequent complications such as hypoparathyroidism. This study evaluates NIRAF's effectiveness in real-world surgical settings, highlighting its potential to enhance surgical outcomes and patient safety.

Aim: We evaluate the effectiveness of NIRAF in detecting PGs during thyroidectomy and central neck dissection and investigate autofluorescence characteristics in both fresh and paraffin-embedded tissues.

Approach: We included 101 patients diagnosed with papillary thyroid cancer who underwent surgeries in 2022 and 2023. We assessed NIRAF's ability to locate PGs, confirmed via parathyroid hormone assays, and involved both junior and senior surgeons. We measured the accuracy, speed, and agreement levels of each method and analyzed autofluorescence persistence and variation over 10 years, alongside the expression of calcium-sensing receptor (CaSR) and vitamin D.

Results: NIRAF demonstrated a sensitivity of 89.5% and a negative predictive value of 89.1%. However, its specificity and positive predictive value (PPV) were 61.2% and 62.3%, respectively, which are considered lower. The kappa statistic indicated moderate to substantial agreement (kappa = 0.478; $P<0.001$ ). Senior surgeons achieved high specificity (86.2%) and PPV (85.3%), with substantial agreement (kappa = 0.847; $P<0.001$ ). In contrast, junior surgeons displayed the lowest kappa statistic among the groups, indicating minimal agreement (kappa = 0.381; $P<0.001$ ). Common errors in NIRAF included interference from brown fat and eschar. In addition, paraffin-embedded samples retained stable autofluorescence over 10 years, showing no significant correlation with CaSR and vitamin D levels.

Conclusions: NIRAF is useful for PG identification in thyroid and neck surgeries, enhancing efficiency and reducing inadvertent PG removals. The stability of autofluorescence in paraffin samples suggests its long-term viability, with false positives providing insights for further improvements in NIRAF technology.

Significance: Machine learning models for the direct extraction of tissue parameters from hyperspectral images have been extensively researched recently, as they represent a faster alternative to the well-known iterative methods such as inverse Monte Carlo and inverse adding-doubling (IAD).

Aim: We aim to develop a Bayesian neural network model for robust prediction of physiological parameters from hyperspectral images.

Approach: We propose a two-component system for extracting physiological parameters from hyperspectral images. First, our system models the relationship between the measured spectra and the tissue parameters as a distribution rather than a point estimate and is thus able to generate multiple possible solutions. Second, the proposed tissue parameters are then refined using the neural network that approximates the biological tissue model.

Results: The proposed model was tested on simulated and in vivo data. It outperformed current models with an overall mean absolute error of 0.0141 and can be used as a faster alternative to the IAD algorithm.

Conclusions: Results suggest that Bayesian neural networks coupled with the approximation of a biological tissue model can be used to reliably and accurately extract tissue properties from hyperspectral images on the fly.

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.