Purpose: In glioma surgery, it is critical to maximize tumor resection without compromising adjacent noncancerous brain tissue. Optical coherence tomography (OCT) is a noninvasive, label-free, real-time, high-resolution imaging modality that has been explored for glioma infiltration detection. Here, we report a novel artificial intelligence (AI)-assisted method for automated, real-time, in situ detection of glioma infiltration at high spatial resolution. Experimental Design: Volumetric OCT datasets were intraoperatively obtained from resected brain tissue specimens of 21 patients with glioma tumors of different stages and labeled as either noncancerous or glioma-infiltrated on the basis of histopathology evaluation of the tissue specimens (gold standard). Labeled OCT images from 12 patients were used as the training dataset to develop the AI-assisted OCT-based method for automated detection of glioma-infiltrated brain tissue. Unlabeled OCT images from the other 9 patients were used as the validation dataset to quantify the method detection performance. Results: Our method achieved excellent levels of sensitivity (∼100%) and specificity (∼85%) for detecting glioma-infiltrated tissue with high spatial resolution (16 μm laterally) and processing speed (∼100,020 OCT A-lines/second). Conclusions: Previous methods for OCT-based detection of glioma-infiltrated brain tissue rely on estimating the tissue optical attenuation coefficient from the OCT signal, which requires sacrificing spatial resolution to increase signal quality, and performing systematic calibration procedures using tissue phantoms. By overcoming these major challenges, our AI-assisted method will enable implementing practical OCT-guided surgical tools for continuous, real-time, and accurate intraoperative detection of glioma-infiltrated brain tissue, facilitating maximal glioma resection and superior surgical outcomes for patients with glioma.

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer.

In some applications of biomedical imaging, a linear mixture model can represent the constitutive elements (end-members) and their contributions (abundances) per pixel of the image. In this work, the extended blind end-member and abundance extraction (EBEAE) methodology is mathematically formulated to address the blind linear unmixing (BLU) problem subject to positivity constraints in optical measurements. The EBEAE algorithm is based on a constrained quadratic optimization and an alternated least-squares strategy to jointly estimate end-members and their abundances. In our proposal, a local approach is used to estimate the abundances of each end-member by maximizing their entropy, and a global technique is adopted to iteratively identify the end-members by reducing the similarity among them. All the cost functions are normalized, and four initialization approaches are suggested for the end-members matrix. Synthetic datasets are used first for the EBEAE validation at different noise types and levels, and its performance is compared to state-of-the-art algorithms in BLU. In a second stage, three experimental biomedical imaging applications are addressed with EBEAE: m-FLIM for chemometric analysis in oral cavity samples, OCT for macrophages identification in post-mortem artery samples, and hyper-spectral images for in-vivo brain tissue classification and tumor identification. In our evaluations, EBEAE was able to provide a quantitative analysis of the samples with none or minimal a priori information.

Significance: Raman spectroscopy is an important tool in understanding chemical components of various materials. However, the excessive weight and energy consumption of a conventional CCD-based spectrometer forbids its applications in space-based instruments, including unmanned aircraft vehicles (UAVs) and Mars/Moon rovers. In this article, we present a lightweight and energy-efficient hyperspectral Raman measurement system. This technique allows UAVs and space vehicles to carry Raman spectrometers without consuming excessive energy.

Frequency domain (FD) fluorescence lifetime imaging (FLIM) involves the excitation of the sample of interest with a modulated light source and digitization of the fluorescence emission for further analysis. Traditional FD-FLIM systems use heterodyne or homodyne detection, where the excitation light source and detector are modulated at specific frequency(s). More recently, FD-FLIM systems that use reflection of the light source as a trigger or phase reference for lifetime calculations have been developed. These detection schemes, however, require extra components that increase the cost and complexity of the FD-FLIM system. Here, we report a novel FD-FLIM detection scheme whereby the light source modulation and emission digitization are implemented using Field Programmable Gate Arrays (FPGAs), and fixed gain avalanche photodiodes are used for fluorescence detection. The reported FD-FLIM system was designed for probing nanosecond lifetime fluorophores (2–10 ns) at three emission bands simultaneously. The system utilizes a 375 nm diode laser for excitation at multiple simultaneous modulation frequencies (between 1 MHz and 83 MHz, bandwidth limited intentionally by using a lowpass filter) and three fixed gain avalanche photodiodes for simultaneous detection of three emission bands: 405/20 nm, 440/40 nm, and 525/50 nm (center/FWHM). Real-time computation of the modulation and phase lifetimes is simply performed by direct application of the discrete Fourier transform (max. of 10 frequencies) to the digitized fluorescence emission signals. The accuracy and sensitivity of this novel FD-FLIM detection scheme was demonstrated by imaging standard fluorophores and ex vivo unfixed human coronary artery tissue samples.