Concept of a fully-implantable system to monitor tumor recurrence.

Current treatment for glioblastoma includes tumor resection followed by radiation, chemotherapy, and periodic post-operative examinations. Despite combination therapies, patients face a poor prognosis and eventual recurrence, which often occurs at the resection site. With standard MRI imaging surveillance, histologic changes may be overlooked or misinterpreted, leading to erroneous conclusions about the course of adjuvant therapy and subsequent interventions. To address these challenges, we propose an implantable system for accurate continuous recurrence monitoring that employs optical sensing of fluorescently labeled cancer cells and is implanted in the resection cavity during the final stage of tumor resection. We demonstrate the feasibility of the sensing principle using miniaturized system components, optical tissue phantoms, and porcine brain tissue in a series of experimental trials. Subsequently, the system electronics are extended to include circuitry for wireless energy transfer and power management and verified through electromagnetic field, circuit simulations and test of an evaluation board. Finally, a holistic conceptual system design is presented and visualized. This novel approach to monitor glioblastoma patients is intended to early detect recurrent cancerous tissue and enable personalization and optimization of therapy thus potentially improving overall prognosis.

Proximal detection of guide wire perforation using feature extraction from bispectral audio signal analysis combined with machine learning.

Artery perforation during a vascular catheterization procedure is a potentially life threatening event. It is of particular importance for the surgeons to be aware of hidden or non-obvious events. To minimize the impact it is crucial for the surgeon to detect such a perforation very early. We propose a novel approach to identify perforations based on the acquisition and analysis of audio signals on the outside proximal end of a guide wire. The signals were acquired using a stethoscope equipped with a microphone and attached to the proximal end of the guide wire via a 3D printed adapter. Bispectral analysis was employed to extract acoustic signatures in the signal and several features were extracted from the bispectrum of the signal. Finally, three machine learning algorithms - K-nearest Neighbor, Support Vector Machine (SVM), and Artificial Neural Network (ANN)- were used to classify a signal as a perforation or as an artifact. The bispectrum-based features resulted in valuable features allowing a perforation to be clearly identifiable from other occurring events. A perforation leaves a clear audio signal trace in the time-frequency domain. The recordings were classified as perforation, friction or guide wire bump using SVM with 97% (polykernel) and 98.62% (RBF) accuracy, k-nearest Neighbor an accuracy of 98.28% and ANN with accuracy of 98.73% was obtained. The presented approach shows that interactions starting at the tip of a guide wire can be picked up at its proximal end providing a valuable additional information that could be used during a guide wire procedure.

Novel clinical device tracking and tissue event characterization using proximally placed audio signal acquisition and processing.

We propose a new and complementary approach to image guidance for monitoring medical interventional devices (MID) with human tissue interaction and surgery augmentation by acquiring acoustic emission data from the proximal end of the MID outside the patient to extract dynamical characteristics of the interaction between the distal tip and the tissue touched or penetrated by the MID. We conducted phantom based experiments (n = 955) to show dynamic tool/tissue interaction during tissue needle passage (a) and vessel perforation caused by guide wire artery perforation (b). We use time-varying auto-regressive (TV-AR) modelling to characterize the dynamic changes and time-varying maximal energy pole (TV-MEP) to compute subsequent analysis of MID/tissue interaction characterization patterns. Qualitative and quantitative analysis showed that the TV-AR spectrum and the TV-MEP indicated the time instants of the needle path through different phantom objects (a) and clearly showed a perforation versus other generated artefacts (b). We demonstrated that audio signals acquired from the proximal part of an MID could provide valuable additional information to surgeons during minimally invasive procedures.