Improved Modeling of Image-Guided Thermal Ablation Procedures Towards Patient-Specific Treatment Planning Applications

Abstract

Microwave ablation (MWA) is a minimally-invasive modality that is playing an increasingly vital role in the treatment of cancer and benign disease. These procedures involve the use of a microwave antenna (or an array of antennas) to deliver energy to raise tissue temperature above a thermal threshold to induce irreversible cell damage. Commonly used clinical MWA systems operate at frequencies of 2450 MHz and 915 MHz. Image-guided percutaneous MWA treatments can be guided by intraoperative ultrasound or computed tomography (CT) fluoroscopy, which allows the physician to deliver treatments precisely. As such, image-guided MWA has received substantial attention for the treatment of cancer in the past decade that is performed in combination with other therapies (radiation therapy) or used as an alternative to other more invasive procedures (surgical resection). There has been increasing interest in improving the efficacy and specificity of MWA through improving energy delivery and application techniques in this domain. This review outlines clinical percutaneous MWA technology detailing concepts related to thermal dosimetry, the physics of microwave heating, modeling of MWA in tissue, and future goals of treatment planning in the context of image-guided MWA procedures. Microwave ablation (MWA) treatment is an important alternative to surgical resection of tumor in cancer treatment; however, the naive planning tools currently available are of limited practical use in real clinical decision making. These geometric planning guidelines are insufficient treatment planning tools for accounting for the level of unpredicted treatment variability seen during MWA procedures. Unanticipated treatment variability may potentially be a cause of cancer recurrence, and it is difficult to measure treatment variability during MWA procedures on patients. Biothermal models have been employed to simulate these types of procedures and aid in quantifying the level of treatment variability seen in the clinically. In general, there are too many variables to measure and include for a patient-specific physically-based simulation. A physically based simulation that accounts for tissue heterogeneities, perfusion, and temperature dependent effects will provide improved predictive accuracy over existing geometric models. Nevertheless, it is still unclear as to which parameters are important, and it we cannot measure their effect on real patients. A potential solution to this problem would be to create a validated physically based model to simulate and explore the effect of a range of different patient specific variables so that we can focus on the most important ones. This will be a critical component in developing MWA planning and