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Treatment Planning Optimization for Radiofrequency Ablation of Hepatic Tumors. Hernán Abeledo, Ph.D. Associate Professor Engineering Management and Systems Engineering School of Engineering and Applied Science abeledo@gwu.edu (202) 994-7521
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Treatment Planning Optimization for Radiofrequency Ablation of Hepatic Tumors Hernán Abeledo, Ph.D. Associate Professor Engineering Management and Systems Engineering School of Engineering and Applied Science abeledo@gwu.edu (202) 994-7521 Joint with: Enrique Campos-Nañez & Stella S. Munuo (GWU-SEAS) Kevin Cleary & Filip Banovac (GUMC-ISIS) Partially funded by the GW Institute for Biomedical Engineering
Radiofrequency Ablation of Liver Tumors • Minimally invasive cancer treatment modality (percutaneous, laparoscopic) • Cells killed by heat generated by radiofrequency energy • Treatment alternative for 80% of un-resectable hepatic malignancies • Performed by Interventional Radiologists guided by Ultrasound, CT or MRI • Region treated by a single ablation is approximately a spherical ellipsoid • Probes come in several sizes (up to 5 cm diameter) • Large tumors may require multiple overlapping ablations Figures from [Dodd, Soulen et al. 2000]
Towards Real-time RFA Planning Goal: create a treatment planning tool that computes optimized probe trajectories and ablation placements Updated Tumor & Ablation Data Tracking System relays probe location Tumor Data Optimized Treatment Plan Optimization Module MD OK? Modify model MD ablates No Yes
Research Activities • Develop mathematical models and optimizationalgorithms as part of an image-guided treatment planning system • Objectives and constraintsof optimization module: • Ensure entire tumor plus 1 cm margin are treated • Avoid burns or punctures of other organs, bones, or major vessels • Minimize number of required ablations • Limit number of punctures to liver capsule (e.g., at most 3) • Minimize number of needle insertions • Allow reinsertion of probes through same puncture of liver capsule • Minimize damage to healthy tissue (beyond 1 cm margin)
Optimization Methodology • Image data is discretized into a 3-D grid (~3mm resolution) • Grid points classified as tumor, margin, healthy, organ type, etc. • Integer programming methods used to model and solve problem • Integer programming optimization techniques also used in radiotherapy planning (brachytherapy, Gamma Knife) • RFA provides challenging problems for mathematical optimization