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-1
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
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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
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Early Cardiac Ablation
• Highly successful procedure (99%)
• Some recurrence of fibrillation
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Early Guidance for Cardiac Ablation
• Real-time 3D feedback with functional
information
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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
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Image Guided Cardiac Ablation
Real Time 3D
Position and
Orientation Trackers
Real-Time
3D
Ultrasound
Pre-acquired
Image
Volume(s)
Coordinates
Images
Surface
Real-Time EP
Registration
Patient-Specific
Anatomy, EP and
Motion Heartbeat
Model
EP Data
Ablation
Probe
Images
Angulation
5D Viewer
Fluoroscope
Images
US Patent
Circa
1998 #6,556,695
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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
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Visualizing Cardiac Ablation
Mapping Patient-Specific Anatomy
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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
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System Interface (1)
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System Interface (2)
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System Interface (3)
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Patient Procedures
• To date, we have shadowed 4 procedures
• Cardiologist uses CARTO XP for guidance
• Mayo system captures all data
• Cardiologist review after procedure
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Patient Procedure Results
RMS error (mm)
Guided
Auto
Both
Surface
4.56003
3.76794
3.66108
Burn
3.30075
3.09241
3.43609
Both
3.96714
3.43955
3.54795
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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)
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Enhancing Visualization through
Refinement
Fusing Intra-operative Data
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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
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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 preoperative data as a scaffold for
intra-operative data
http://100swallows.wordpress.com/2009/05/20/how-to-carve-a-figure-in-marble/
http://www.flickr.com/photos/buildmakecraftbake/3239252403/
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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
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Synthetic experiment
Liu et al, 2011
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Patient Data (offline)
Cameron et al, 2011
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Ongoing Studies
• Explore visualization techniques for
fused models
• Conduct targeting exercises with
cardiologist to evaluate utility
• In phantoms
• In animal model
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Visual Feedback of Ablation
Modeling Thermal Response in LA
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“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.
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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
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Thermal modeling of lesion growth with radiofrequency ablation
devices
Isaac A Chang*1 and Uyen D Nguyen2
BioMedical Engineering OnLine 2004, 3:27 doi:10.1186/1475925X-3-27
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Image derived thermal conductivity
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Tissue ablation and charring models
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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
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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
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