Journal of Biomedical Optics最新文献

Significance: Accurate identification between pathologic (e.g., tumors) and healthy brain tissue is a critical need in neurosurgery. However, conventional surgical adjuncts have significant limitations toward achieving this goal (e.g., image guidance based on pre-operative imaging becomes inaccurate up to 3 cm as surgery proceeds). Hyperspectral imaging (HSI) has emerged as a potential powerful surgical adjunct to enable surgeons to accurately distinguish pathologic from normal tissues.

Aim: We review HSI techniques in neurosurgery; categorize, explain, and summarize their technical and clinical details; and present some promising directions for future work.

Approach: We performed a literature search on HSI methods in neurosurgery focusing on their hardware and implementation details; classification, estimation, and band selection methods; publicly available labeled and unlabeled data; image processing and augmented reality visualization systems; and clinical study conclusions.

Results: We present a detailed review of HSI results in neurosurgery with a discussion of over 25 imaging systems, 45 clinical studies, and 60 computational methods. We first provide a short overview of HSI and the main branches of neurosurgery. Then, we describe in detail the imaging systems, computational methods, and clinical results for HSI using reflectance or fluorescence. Clinical implementations of HSI yield promising results in estimating perfusion and mapping brain function, classifying tumors and healthy tissues (e.g., in fluorescence-guided tumor surgery, detecting infiltrating margins not visible with conventional systems), and detecting epileptogenic regions. Finally, we discuss the advantages and disadvantages of HSI approaches and interesting research directions as a means to encourage future development.

Conclusions: We describe a number of HSI applications across every major branch of neurosurgery. We believe these results demonstrate the potential of HSI as a powerful neurosurgical adjunct as more work continues to enable rapid acquisition with smaller footprints, greater spectral and spatial resolutions, and improved detection.

Significance: I explore hyperspectral imaging, a rapid and noninvasive technique with significant potential in biometrics and medical diagnosis. Personal identification was performed using cross-sectional hyperspectral images of palms, offering a simpler and more robust method than conventional vascular pattern identification methods.

Aim: I aim to demonstrate the potential of local cross-sectional hyperspectral palm images to identify individuals with high accuracy.

Approach: Hyperspectral imaging of palms, artificial intelligence (AI)-based region of interest (ROI) detection, feature vector extraction, and dimensionality reduction were utilized to validate personal identification accuracy using the area under the curve (AUC) and equal error rate (EER).

Results: The feature vectors extracted by the proposed method demonstrated higher intra-cluster similarity when the clustering data were reduced through uniform manifold approximation and projection compared with principal component analysis and $t$ -distributed stochastic neighbor embedding. A maximum AUC of 0.98 and an EER of 0.04% were observed.

Conclusions: I proposed a biometric method using cross-sectional hyperspectral imaging of human palms. The procedure includes AI-based ROI detection, feature extraction, dimension reduction, and intra- and inter-subject matching using Euclidean distances as a discriminant function. The proposed method has the potential to identify individuals with high accuracy.

Significance: Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. These areas can be assessed by monitoring cerebral hemodynamics and metabolism. Robust quantification of these biomarkers is complicated to perform during neurosurgery due to the critical context of the operating room. In actual devices, the inhomogeneities of the optical properties of the exposed brain cortex are poorly taken into consideration, which introduce quantification errors of biomarkers of brain functionality. Moreover, the best choice of spectral configuration is still based on an empirical approach.

Aim: We propose a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This simulator can provide realistic modeling of the cerebral cortex and the identification of the optimal wavelengths to monitor cerebral hemodynamics (oxygenated ${\mathrm{HbO}}_2$ and deoxygenated hemoglobin Hb) and metabolism (oxidized state of cytochromes $b$ and $c$ and cytochrome-c-oxidase oxCytb, oxCytc, and oxCCO).

Approach: The digital instrument simulator is computed with white Monte Carlo simulations of a volume created from a real image of exposed cortex. We developed an optimization procedure based on a genetic algorithm to identify the best wavelength combinations in the visible and near-infrared range to quantify concentration changes in ${\mathrm{HbO}}_2$ , Hb, oxCCO, and the oxidized state of cytochrome $b$ and $c$ (oxCytb and oxCytc).

Results: The digital instrument allows the modeling of intensity maps collected by a camera sensor as well as images of path length to take into account the inhomogeneities of the optical properties. The optimization procedure helps to identify the best wavelength combination of 18 wavelengths that reduces the quantification errors in ${\mathrm{HbO}}_2$ , Hb, and oxCCO by 47%, 57%, and 57%, respectively, compared with the gold standard of 121 wavelengths between 780 and 900 nm. The optimization procedure does not help to resolve changes in cytochrome $b$ and $c$ in a significant way but helps to better resolve oxCCO changes.

Conclusions: We proposed a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This digital instrument simulator and this optimization framework could be used to optimize the design of hyperspectral imaging devices.

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: 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: 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: 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: Standardization of fluorescence molecular imaging (FMI) is critical for ensuring quality control in guiding surgical procedures. To accurately evaluate system performance, two metrics, the signal-to-noise ratio (SNR) and contrast, are widely employed. However, there is currently no consensus on how these metrics can be computed.

Aim: We aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems.

Approach: We quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system.

Results: We show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to $\sim 35\mathrm{dB}$ (SNR), $\sim 8.65a.u$ . (contrast), and $\sim 0.67a.u$ . (BM score).

Conclusions: The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.

Significance: Intraoperative molecular imaging (IMI) enables the detection and visualization of cancer tissue using targeted radioactive or fluorescent tracers. While IMI research has rapidly expanded, including the recent Food and Drug Administration approval of a targeted fluorophore, the limits of detection have not been well-defined.

Aim: The ability of widely available handheld intraoperative tools (Neoprobe and SPY-PHI) to measure gamma decay and fluorescence intensity from IMI tracers was assessed while varying characteristics of both the signal source and the intervening tissue or gelatin phantoms.

Approach: Gamma decay signal and fluorescence from tracer-bearing tumors (TBTs) and modifiable tumor-like inclusions (TLIs) were measured through increasing thicknesses of porcine tissue and gelatin in custom 3D-printed molds. TBTs buried beneath porcine tissue were used to simulate IMI-guided tumor resection.

Results: Gamma decay from TBTs and TLIs was detected through significantly thicker tissue and gelatin than fluorescence, with at least 5% of the maximum signal observed through up to 5 and 0.5 cm, respectively, depending on the overlying tissue type or gelatin.

Conclusions: We developed novel systems that can be fine-tuned to simulate variable tumor characteristics and tissue environments. These were used to evaluate the detection of fluorescent and gamma signals from IMI tracers and simulate IMI surgery.

Significance: Shortwave-infrared (SWIR) imaging is reported to yield better contrast in fluorescence-guided surgery than near-infrared (NIR) imaging, due to a reduction in scattering. This benefit of SWIR was shown in animal studies, however not yet in clinical studies with patient samples.

Aim: We investigate the potential benefit of SWIR to NIR imaging in clinical samples containing cetuximab-IRDye800CW in fluorescence-guided surgery.

Approach: The potential of the epidermal growth factor-targeted NIR dye cetuximab-IRDye800CW in the shortwave range was examined by recording the absorption and emission spectrum. An ex vivo comparison of NIR and SWIR images using clinical tumor samples of patients with penile squamous cell carcinoma (PSCC) and head and neck squamous cell carcinoma (HNSCC) containing cetuximab-IRDye800CW was performed. The comparison was based on the tumor-to-background ratio and an adapted contrast-to-noise ratio (aCNR) using the standard of care pathology tissue assessment as the golden standard.

Results: Based on the emission spectrum, cetuximab-IRDye800CW can be detected in the SWIR range. In clinical PSCC samples, overall SWIR imaging was found to perform similarly to NIR imaging (NIR imaging is better than SWIR in the 2/7 criteria examined, and SWIR is better than NIR in the 3/7 criteria). However, when inspecting HNSCC data, NIR is better than SWIR in nearly all (5/7) examined criteria. This difference seems to originate from background autofluorescence overwhelming the off-peak SWIR fluorescence signal in HNSCC tissue.

Conclusion: SWIR imaging using the targeted tracer cetuximab-IRDye800CW currently does not provide additional benefit over NIR imaging in ex vivo clinical samples. Background fluorescence in the SWIR region, resulting in a higher background signal, limits SWIR imaging in HNSCC samples. However, SWIR shows potential in increasing the contrast of tumor borders in PSCC samples, as shown by a higher aCNR over a line.