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Treatment Planning Optimization for Radiofrequency Ablation of Hepatic Tumors

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

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  1. 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

  2. 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]

  3. 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

  4. 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)

  5. 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

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