Tissue differentiation using model-based optical sensor systems
The primary goal of all new interventional procedures in surgery is to combine minimal invasiveness and high effectiveness with short treatment duration and low complication rates. A central aspect in oncology is the differentiation between malignant target structures and the surrounding tissue during surgery. Frozen section diagnostics is considered the gold standard of intraoperative tissue differentiation. During this procedure, the tissue is taken to a laboratory immediately after resection, cut in layers, stained, and examined histopathologically. The surgery usually has to be interrupted until the result is available. In addition, therapy (tumor resection) and diagnosis (histological examination) are separated both spatially and temporally.
The fusion of novel, multimodal sensor systems using machine learning methods yields a high potential that exceeds that of the individual sensors. The multimodal sensor systems are intended to support surgeons in making decisions between resection and tissue preservation as a supplement to frozen section diagnostics. Under this multimodal approach, sensor systems are to be developed and used. Due to the changed tissue characteristics, this should enable a fast and intraoperative assessment of the tissue.
In order to enable maximal functional preservation for the patient, a small volume identification of the tumor borders is necessary. The central topic of research project A1 is the discrimination of tissue boundaries based on elastic parameters using optical methods. This approach is based on the fact that tumor tissue has a different morphology than healthy tissue. The increased growth of tumor tissue leads to a denser but less organized structure of the malignant tissue and to changes in the extracellular matrix (ECM). The increased density of the extracellular matrix leads to changes in the elastic parameters of the tissue. This serves as an approach to tissue differentiation, as the elasticity is quantifiable.
Optical detection of changed mechanical and structural properties of the target tissue
The goal of this research project is the development of multimodal optical sensor systems, which enable the identification of deeper functional tissue structures. In this context, depth-resolved optical sensor principles that have already been successfully tested conceptually are to be evaluated with respect to miniaturization and, if necessary, combined in order to record a detailed response of the tissue to an applied force. Furthermore, tissue differentiation can be supported by high-resolution optical detection of cell and tissue structures as a complementary tumor detection feature (hypervascularization and closer meshed ECM).
Methods and solution approaches
The measurement of elastic parameters using optical coherence tomography (OCT) has already been demonstrated by many research groups in the biomedical field. The foundation of the measurements is an exact recording of the movement of the tissue in response to an applied force. In the project further measurement methods, such as confocal methods or digital holography, will be investigated and evaluated. The elastic parameters will be compared to and verified with theoretical simulation models. The aim of the research project is to develop a sensitive multimodal miniaturized probe for endoscopic use, which allows for determining the elastic parameters of the tissue at hand.