Li, Xingde

Real-time intraoperative delineation of cancer and non-cancer brain tissues, especially in the eloquent cortex, is critical for thorough cancer resection, lengthening survival, and improving quality of life. Prior studies have established that thresholding optical attenuation values reveals cancer regions with high sensitivity and specificity. However, threshold of a single value disregards local information important to making more robust predictions. Hence, we propose deep convolutional neural networks (CNNs) trained on labeled OCT images and co-occurrence matrix features extracted from these images to synergize attenuation characteristics and texture features. Specifically, we adapt a deep ensemble model trained on 5,831 examples in a training dataset of 7 patients. We obtain 93.31% sensitivity and 97.04% specificity on a holdout set of 4 patients without the need for beam profile normalization using a reference phantom. The segmentation maps produced by parsing the OCT volume and tiling the outputs of our model are in excellent agreement with attenuation mapping-based methods. Our new approach for this important application has considerable implications for clinical translation.

OCT-based quantitative tissue optical properties imaging is a promising technique for intraoperative brain cancer assessment. The attenuation coefficient analysis relies on the depth-dependent OCT intensity profile, thus sensitive to tissue surface positions relative to the imaging beam focus. However, it is almost impossible to maintain a steady tissue surface during intraoperative imaging due to the patient’s arterial pulsation and breathing, the operator’s motion, and the complex tissue surface geometry of the surgical cavity. In this work, we developed an intraoperative OCT imaging probe with a surface-tracking function to minimize the quantification errors in optical attenuation due to the tissue surface position variations. A compact OCT imaging probe was designed and engineered to have a long working distance of ∼ 41 mm and a large field of view of 4 × 4 mm2 while keeping the probe diameter small (9 mm) to maximize clinical versatility. A piezo-based linear motor was integrated with the imaging probe and controlled based upon real-time feedback of tissue surface position inferred from OCT images. A GPU-assisted parallel processing algorithm was implemented, enabling detection and tracking of tissue surface in real-time and successfully suppressing more than 90% of the typical physiologically induced motion range. The surface-tracking intraoperative OCT imaging probe could maintain a steady beam focus inside the target tissue regardless of the surface geometry or physiological motions and enabled to obtain tissue optical attenuation reliably for assessing brain cancer margins in challenging intraoperative settings.

A dual-function microneedle for rapid, high-resolution, and quantitative 3D deep-brain imaging and power-efficient tissue ablation.

Abstract 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.