Quantitative assessment of hemodynamic and structural characteristics of in vivo brain tissue using total diffuse reflectance spectrum measured in a non-contact fashion

Wireless, battery-free optoelectronic systems as subdermal implants for local tissue oximetry

Monitoring regional tissue oxygenation in animal models and potentially in human subjects can yield insights into the underlying mechanisms of local O2-mediated physiological processes and provide diagnostic and therapeutic guidance for relevant disease states. Existing technologies for tissue oxygenation assessments involve some combination of disadvantages in requirements for physical tethers, anesthetics, and special apparatus, often with confounding effects on the natural behaviors of test subjects. This work introduces an entirely wireless and fully implantable platform incorporating (i) microscale optoelectronics for continuous sensing of local hemoglobin dynamics and (ii) advanced designs in continuous, wireless power delivery and data output for tether-free operation. These features support in vivo, highly localized tissue oximetry at sites of interest, including deep brain regions of mice, on untethered, awake animal models. The results create many opportunities for studying various O2-mediated processes in naturally behaving subjects, with implications in biomedical research and clinical practice.

Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain

The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.