computational modeling and real-time control of patient...
TRANSCRIPT
IntroductionOverview
Current WorkSummary
Computational Modeling andReal-Time Control of
Patient-Specific Laser Treatment of Cancer
J. Tinsley Oden David Fuentes
Institute for Computational Engineering and SciencesThe University of Texas at Austin
Seventh Interventional MRI SymposiumSeptember 12 - 13, 2008
Baltimore Marriott Waterfront Hotel
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
Outline
1 IntroductionCollaborators and ColleaguesMain Ideas
2 OverviewWorkflowGoverning Equations
3 Current WorkIn-Vivo Experiments
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
Collaborators and ColleaguesMain Ideas
Collaborators and Colleagues
Institute for Computational Engineering and SciencesJ. T. Oden, I. Babuška, J. C. Browne, C. Bajaj, J. Bass,L. Demkowicz, Y. Feng, A. Hawkins, B. Kwon,S. Prudhomme, C. Simmons, J. Sweet, Y. Zhang
Department of Biomedical EngineeringK. R. Diller, M. N. Rylander, S. Koshnevis, A. Song
Department of Imaging Physics, M.D. Anderson CancerCenter
D. Fuentes, J. Hazle, L. Bidaut, A. Elliott, A. Shetty,R. J. Stafford
Acknowledgment:NSF grant CNS-0540033, Frederica DaremaBioTex Inc., R. McNichols
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
Collaborators and ColleaguesMain Ideas
Main Ideas
Computer guided laser treatment as a minimally invasivealternative to standard treatment of cancerSimple Idea: Subject all cells, including cancer cells, totemperatures outside normothermia range may damageand destroy cells
Hyperthermia temperature ranges: 50◦C for 2 minsHeat source provided by diffusing interstitial laser fiber
Real-Time thermal imaging provides guidance andincreases fidelity of real-time computational predictionUse patient specific model of bioheat transfer to optimizethe treatmentTarget disease: Tissue with a well-defined cancerousregion or tumor
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
WorkflowGoverning Equations
Cyber Infrastructure and Work Flow
Amira/Cubit/LBIE
Hp3D
Houston: Surgery/Visualization Client
AVS/Volume Rover
Image processing
and Mesh generation
Feedback Control
MRTI Data Transfer
computations
hp adaptive FEM
MRI & MRTI Scans
AustinHouston
Compute
Server
Data
Server
Server
Visualization
Data Acquisition
GeometryExtraction
MeshGeneration
LaserParameterOptimization
Registration
Data Transfer
Patient SpecificCalibration
Data Filtering
Predictions
Visualizations
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
WorkflowGoverning Equations
Optimization
The main problems in which we are interested inis the real-time solution of the followingproblems
calibration of the model coefficientsTemperature distribution measured by in-vivo MRTI
optimal control of the laserTemperature/HSP/Damage-Based optimizations
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
In-Vivo Experiments
In-Vivo Results: Registration
Rectum
ProstateFEM mesh
Laser tip
Laser fiber
DICOM coordinates of laser tip
Currently have capabilities for rigid body registrationUsing ITK www.itk.org for Registration.
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
In-Vivo Experiments
In-Vivo Results: TreatmentPost-Registration Control System Stages
1 Data Acquisition for Calibration2 Time Lag for Calibration Computations3 Time Lag for Optimal Temp/Damage/HSP
Computations4 Optimal Control with fail-safe laser shutoff
1 2 3 4
t0 t1 t2 t3 tf0
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
In-Vivo Experiments
In-Vivo Results: Treatment
5mm
(a) (b)
(c)
(d)
(e)
(f)
(g)
power history
30mm
cutline
laser tip
240mm
prostate mesh
Animation Linux / Windows
1.2cmdiametertreatmentobjective
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
In-Vivo Experiments
In-Vivo Results: Laser Control
0
2
4
6
8
10
12
14
16
0 200 400 600 800 1000 1200
pow
er[W
atts
]
seconds
power history
visualase powerhp3d power
Treatment Data: Linux / Windows
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
In-Vivo Experiments
In-Vivo Results: Histology
(a) (b) (c)
(f)(e)
(d)
(g)
Prostatefixed informalin andslicedcongruent topost-treatmentMR images
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
Closing Remarks
Major Challenges and Future Work
Algorithms to Exploit Peta-Computer ArchitecturesReal-Time Heterogeneous Calibration and Adaptivity
Develop Abstract Platform to Facilitate Variety ofThermal Therapies
Cryo-ablation, Microwave, Radiofrequency, High IntensityFocused Ultrasound, Nano-Particle Mediated
Stochastic Models for Cancer Treatment SimulationUncertainty Quantification
Biological Based Treatment OptimizationTumor Growth Models (Constitutive Laws)Cellular and Tissue Damage ModelsHeat Shock Protein Expression Models
Imaging to Mesh Generation Pipeline
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
Closing Remarks
Concluding Comments
We hope that this work makes a small step toward the timeat which computer modeling and simulation interacting withmedical technologies can dramatically improve cancertherapies and enhance and prolong the life of cancerpatients.
American Cancer Society "Global Cancer Facts and Figures, 2007"
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery
IntroductionOverview
Current WorkSummary
Closing Remarks
Questions
dddas.ices.utexas.edu
J. Tinsley Oden, David Fuentes Remote Laser Cancer Surgery