Enhanced treatment planning and navigation for image-guided microwave ablation of hepatic tumors
Collins, Jarrod Alan
Primary and metastatic liver cancers are a considerable and increasing U.S. and global health concern. Patients presenting with hepatic tumors are preferably treated with surgical resection which has a record of long-term patient survival. While resection has proven effective and can be potentially curative, constraints associated with risk and patient suitability narrow the population of eligible patients to only around 10-30%. More local therapies, such as thermal ablation, have received increased indications for use in recent years including the treatment of surgically unresectable malignancies. A combination of recent advances in neoadjuvant care, therapeutic options, and improved patient selection criterion have improved the survival rate of patients receiving primary ablative treatments for hepatic cancers to be comparable to the clinical standard set by surgical resection. As they inherently target internal structures, the efficacy of ablation methods is highly reliant on accurate localization and targeting of subsurface anatomies during a procedure, as inaccurate delivery can lead to incomplete treatment and local recurrence. This dissertation advances image-guided hepatic microwave ablation by developing methods which provide enhanced procedural planning and intraoperative tumor localization. A true multiphysics framework is developed herein which utilizes patient-specific therapeutic predictive modeling and advanced surgical navigation methodologies. The guidance approach applied in this work creates a spatial mapping between information-rich preoperatvely acquired image data and the state of the patient during therapy. This registration is further enhanced by the application of a biomechanical model which corrects for soft-tissue deformation that occurs peri-operatively. This approach allows physicians to effectively navigate to a targeted lesion by using the registered and corrected preoperative image data for intraoperative guidance. Furthermore, this work develops and presents a proof-of-concept for image-data driven patient-specific predictive modeling of microwave ablation. The results demonstrate the feasibility of such a model-based therapeutic and guidance approach and represent a significant advancement towards a more comprehensive model-predictive paradigm for an important image-guided therapeutic process in use today.