Description
The activities in this area include: a) the design and testing of devices for ablation at RF, microwaves and optical frequencies; b) an integrated system for non-invasive real-time thermal monitoring during hyperthermia for cancer treatment.
In hyperthermia, the current challenge is the real-time, non-invasive reconstruction of temperature in the anatomical regions where the EM field is focused. This is an example of the more general class of problems where a quantity of interest is obtained from scarce indirect measurements. Multiphysics simulations and scarce indirect measurements coexist and complement each other in the proposed framework, aiming at non-invasive temperature real-time cross scan in hyperthermia for cancer. Overall, the approach envisions a radically new technology to address a problem often found in real-world applications. One is interested in reconstructing the inner working of a system from scarce and indirect observations of the system itself. Numerical modeling and machine learning are the two natural fields of research dealing with this problem. Despite advances in these two fields, solutions to such problem remain slow and difficult. Difficulties in solving the problem come from two main sources. Unavoidable uncertainties affect the modelling aspect. Scarcity of data prevents a general data-driven learning model to be trained properly. We propose to address these two issues with a novel framework. Currently, thermometry in hyperthermia is performed with invasive catheters and lacks spatial resolution. We devise a new use of non-invasive devices to get temperature information (e.g., radiometers) for low-cost, accurate and reliable in-treatment thermometry. It will overcome current limitations hindering cancer hyperthermia, which stem from the inability to reliably predict and control temperature inside the patient.