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©2011 MFMER | slide-1 5-D Image Guided Cardiac Ablation Therapy David R Holmes, III, Ph.D. Biomedical Imaging Resource 4th NCIGT and NIH Image Guided Therapy Workshop October 13, 2011
©2011 MFMER | slide-2 Acknowledgments Richard Robb, Ph.D. Douglas Packer, M.D. Maryam Rettmann, Ph.D. Fellows David Kwartowitz, Ph.D. Jiquin Liu, Ph.D. Cristian Linte, Ph.D. Staff Jon Camp Bruce Cameron Sue Johnson
©2011 MFMER | slide-3 Introduction “If I can see it, I can fix it” Thor Sundt to Rich Robb (1972) Visualizing Cardiac Ablation How can we best leverage the available information to visualize an ablation procedure? Enhancing Visualization through Refinement Visual Feedback of Ablation
©2011 MFMER | slide-4 Early Cardiac Ablation Highly successful procedure (99%) Some recurrence of fibrillation
©2011 MFMER | slide-5 Early Guidance for Cardiac Ablation Real-time 3D feedback with functional information
©2011 MFMER | slide-6 Goal of high-dimensional/multi-modal imaging in RF cardiac ablation Immerse the clinician in the patient To see all of the data in the real-world context of the patient
©2011 MFMER | slide-7 Image Guided Cardiac Ablation Circa 1998 US Patent #6,556,695
©2011 MFMER | slide-8 Components of Multi-modal Image guidance Registration Needed for integration of signals Context (Anatomy) Needed to guide procedure Parametric Mapping Needed for presentation of data Real-time Feedback Needed to faithfully represent the patient on the table
©2011 MFMER | slide-9 Visualizing Cardiac Ablation Mapping Patient-Specific Anatomy
©2011 MFMER | slide-10 Image Guided Cardiac Ablation System Biosense System Single PC, Dual Display, Magnetic Field Generator, Catheter Panel Prototype System 4 Computational Servers, High Performance GUI, Dual Display with HUD, Digital Data Acquisition, Video Data Card, High-speed comm. network Phantom Exp. System Validation Phantom Dynamic Respirator Phantom Biosense Catheters Rettmann et al, 2006
©2011 MFMER | slide-11 System Interface (1)
©2011 MFMER | slide-12 System Interface (2)
©2011 MFMER | slide-13 System Interface (3)
©2011 MFMER | slide-14 Patient Procedures To date, we have shadowed 4 procedures Cardiologist uses CARTO XP for guidance Mayo system captures all data Cardiologist review after procedure
©2011 MFMER | slide-15 Patient Procedure Results RMS error (mm) GuidedAutoBoth Surface Burn Both
©2011 MFMER | slide-16 Ongoing Studies Direct guidance with Mayo mapping system Recording engineering metrics Registration accuracy Repeatability and targeting Recording clinical metrics Total procedure time Time to burn Patient success rates (eventually)
©2011 MFMER | slide-17 Enhancing Visualization through Refinement Fusing Intra-operative Data
©2011 MFMER | slide-18 Lessons Learned Cardiologist “likes” the models, but doesn’t necessarily trust them Image data anatomically faithful, but temporally inaccurate The electro-anatomical map is temporally accurate, but low fidelity ICE is temporally accurate and “high” resolution, but lacks the full 3D context Data must be consistent to be trustworthy
©2011 MFMER | slide-19 Changing the way we look at the data Stone Carving approach Sampled points serve as the rough model Enhance with high-resolution data Paper Mache approach Use the high-resolution pre- operative data as a scaffold for intra-operative data
©2011 MFMER | slide-20 Updating Patient Models Current 3D Model Real-time Data Gross Registration (Tracking) Local Registration (Projection) Fuse Current Model with Local Features Liu et al, 2011
©2011 MFMER | slide-21 Synthetic experiment Liu et al, 2011
©2011 MFMER | slide-22 Patient Data (offline) Cameron et al, 2011
©2011 MFMER | slide-23 Ongoing Studies Explore visualization techniques for fused models Conduct targeting exercises with cardiologist to evaluate utility In phantoms In animal model
©2011 MFMER | slide-24 Visual Feedback of Ablation Modeling Thermal Response in LA
©2011 MFMER | slide-25 “Unsuccessful Ablation” 30-50% patients have recurrence Incomplete Isolation of PV Temporary stunning masks true ablation Inadequate visual feedback Current ablation therapy guidance Provide remedial representation of burn pattern No information about the ablation Temperature distribution, lesion size/pattern etc.
©2011 MFMER | slide-26 Approach Enhance ablation therapy guidance by modeling thermal interaction using available data High-resolution CT (pre-operative) as volumetric model Intra-cardiac Echo (ICE) for local geometry RF parameters from generator Approximate tissue response to ablation using simplified thermal model
©2011 MFMER | slide-27 Thermal modeling of lesion growth with radiofrequency ablation devices Isaac A Chang*1 and Uyen D Nguyen2 BioMedical Engineering OnLine 2004, 3:27 doi: / X-3-27
©2011 MFMER | slide-28 Image derived thermal conductivity
©2011 MFMER | slide-29 Tissue ablation and charring models
©2011 MFMER | slide-30 Ongoing Studies Ex-vivo studies underway to estimate tissue parameters and validate model In-vivo animal studies in early 2012 Retrospectively analyze clinical cases to determine predictability of the model
©2011 MFMER | slide-31 Concluding Remarks “If I can see it ….” is necessary, but not sufficient. “If I can see if and believe it…” Thus, we put specific emphasis on: what the clinician wants. what we can learn from the data. What we can validate
©2011 MFMER | slide-32
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