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An Integrated System for Image-Guided Radiofrequency Ablation (RFA) of Liver Tumors

An Integrated System for Image-Guided Radiofrequency Ablation (RFA) of Liver Tumors. Kevin Cleary, Ziv Yaniv, Georgetown University Noby Hata, Brigham and Women’s Hospital Enrique Campos-Nanez, George Washington University. Liver tumor RFA.

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An Integrated System for Image-Guided Radiofrequency Ablation (RFA) of Liver Tumors

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  1. An Integrated System for Image-Guided Radiofrequency Ablation (RFA) of Liver Tumors Kevin Cleary, Ziv Yaniv, Georgetown University Noby Hata, Brigham and Women’s Hospital Enrique Campos-Nanez, George Washington University

  2. Liver tumor RFA • Liver cancer that cannot be resected due to extent and location of the disease or concurrent medical conditions. • Introduce localized RF energy directly to tumor, typically through expanding metal tines within a small gauge insulated needle. LeVeen probe Before (left) and after (right) treatment (courtesy of Brad Wood, MD, NIH CC)

  3. Overlapping Burns • Large tumors can be treated with overlapping spherical zones • Hard to visualize the overlapping areas • Lack of real-time image guidance requires repeated insertions to hit the target lesion and establish sufficient margins From Dodd, Soulen et al. 2000

  4. Project Overview Goal: Develop an open source workstation for liver RFA planning and treatment based on IGSTK and Slicer. Specific aims: • Develop and evaluate semi-automatic segmentation techniques for the liver, liver vasculature, and liver tumors. [Georgetown, BWH] • Develop a path planning module for evaluating alternative paths to the liver tumor and incorporating multiple overlapping placements as needed for larger tumors. [George Washington University] • Integrate the two capabilities developed above along with electromagnetic tracking of the RFA probe to provide a complete software environment for liver tumor planning, visualization, and execution. [Georgetown, BWH] • Validate the clinical feasibility of the system in a swine animal model. [Georgetown]

  5. Timeline Proposed: Actual:

  6. SegmentationGold Standard Database • Collected approximately 50 CT liver images from Georgetown University Hospital • Followed Health Insurance Portability and Accountability Act (HIPAA) rules for anonymization of data • Medical student segmented 32 livers using ITK-SNAP under supervision of attending interventional radiologist • Next step is to segment tumors

  7. Path Planning • Pre-emptive goal programming approach based on integer programming techniques using a discrete set of data points representing the tumor (uniform sampling): • Minimize needle insertions (trajectories), minimizes the number of punctures to the liver capsule, and the number of needle insertions. • Minimizing Ablations Given needle trajectories. • Minimizing Damage to Healthy Tissue

  8. Path Planning Status • Currently implemented using commercial software Xpress-MP (Fair Issac dash optimization) • Worked well in preliminary swine feasibility studies • Now porting to open source Gnu Linear Programming (GLPK) package • Initial results from porting are extremely slow compared to commercial package • Requires further evaluation

  9. Integration • Initial implementation started at IGT Project Week in December 2008 in Boston • Tracking data is broadcast from IGSTK components using OpenIGTLink protocol • Program resides in IGSTK sandbox and supports FLTK GUI and command line mode

  10. Integration (continued) • Specify configuration using xml files • Supports multiple tools and broadcasting data to multiple computers • Supports the use of a Dynamic Reference Frame. If specified all broadcasted transformations will be relative to the DRF (DRF transformation is not sent) • Will present at SPIE Medical Imaging 2009 in Orlando in February <?xml version="1.0" encoding="ISO-8859-1"?> <tracking_system type = "polaris vicra"> <refresh_rate> 20 </refresh_rate> <serial_communication> <com_port>4</com_port> <baud_rate>115200</baud_rate> <data_bits>8</data_bits> <parity>N</parity> <stop_bits>1</stop_bits> <hand_shake>0</hand_shake> </serial_communication> <tool usage = "reference"> <name> large reference </name> …

  11. Next steps • Goal is to create a Slicer3 workflow based interface for navigated RFA (Nav-RFA) • Requirements document developed by Georgetown and sent to BWH • Slicer will be configured with the following modules • Data: manage scene graph • EM Segmenter : potentially used for automatic segmentation (maybe not in the initial setup) • Editor: manual segmentation and modification of automated segmentation. • Fiducials : marking the fiducials. • Linear registration: fiducial based registration. • OpenIGTLink: tracking • This will create a skeleton end to end workflow where we can fill in individual pieces and improve as we go

  12. Summary • Goal is to develop an integrated system for RFA of liver tumors • Progress is good but vigilance needs to continue • Open source implementation of optimization is one concern

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