Computational Modeling and Real-Time Control of Patient ...dddas.ices.utexas.edu/fuentes/mediaDir/imri2008.pdf · Computational Modeling and Real-Time Control of Patient-Speciﬁc

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


Current WorkSummary

Outline

1 IntroductionCollaborators and ColleaguesMain Ideas

2 OverviewWorkflowGoverning Equations

3 Current WorkIn-Vivo Experiments



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



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



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



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



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.


http://www.itk.org


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



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



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



Current WorkSummary

In-Vivo Experiments

In-Vivo Results: Histology

(a) (b) (c)

(f)(e)

(d)

(g)

Prostatefixed informalin andslicedcongruent topost-treatmentMR images



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



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"



Current WorkSummary

Closing Remarks

Questions

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Documents

Computational Modeling and Real-Time Control of Patient ...dddas.ices.utexas.edu/fuentes/mediaDir/imri2008.pdf · Computational Modeling and Real-Time Control of Patient-Speciﬁc