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Toward Real Time Optimal Control in Laser Surgery for Cancer

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Toward Real Time Optimal Control in Laser Surgery for Cancer Powered By Docstoc
					Real-Time Prediction
and Control
Yusheng Feng, Ph.D
Computational Bioengineering and Nanomechanics Lab
SiViRT Computation Center




External Advisory Board Review Meeting
March 5th, 2010
Real-Time Defined
   Real time can refer to events simulated by a
    computer at the same speed that they would
    occur in real life.
    http://www.webopedia.com
   real-time computing (RTC), or "reactive
    computing", is the study of hard ware and
    software systems that are subject to a “real-
    time constraint” – i.e. operational deadlines
    from event to system response.
    http://www.wikipedia.com
Project Team
                                             Computational Cancer
                                            Research and Real-time
                                            Surgical Control
Yusheng Feng, Ph.D   Gerald Dodd III, M.D
UTSA (Team Lead)     UC Denver



                                             Computational Neuroscience
                                             and Real-time Cellular Control

Allan Coop, Ph.D     Hugo Cornelis, Ph.D
UTHSCSA              UTHSCSA
                                             Unmanned Aerial Vehicle
                                            (UVA) and Real-Time Flight
             C-J Qian, Ph.D
                                            Control
             UTSA
OUTLINE

 Why we are here?
 Where we were?

 Where we are?

 Where we are going?
SiViRT = Si + Vi + RT

Three Components:
• Imaging
• Real-Time Computing
• Uncertainty Quantification
Computational Cancer
Research and Real Time
Surgical Control
Cancer Treatment

              Surgery
             Radiation
              Chemo-,
               Gene-,
Diagnosis    Immuno-       Prognosis
                Viro
  And         Thermo-          And
             Therapies
Treatment                  Outcome
 Planning       And        Prediction

              Image-
               Guided
            Intervention

                           7
    Basic Issues
   Medical Imaging and data processing
   Mathematical Modeling of Laser-Tissue Interaction
   Bioheat transfer in tissue and cellular response
   Mesh generation and numerical simulation
   Cell Damage and tissue Characterization
   Inverse problem and parallel computing
   Treatment planning and outcome prediction
   Real-time surgical monitoring and control
   Application of nano-technology with laser therapy
   Model validation: in vitro and in vivo experimentation
MRTI-Guided Laser Surgery RT Control
                                            Optimal
     MRI                                    Control

    Scan


                                                            hp-adaptive
                 980-nm Diode Laser                               finite
                                                               element
                                                                  patch




   Data
                                      Imaging process (co-
   Acquisition
                                      registration, segmentation,
                                      lofting) and mesh generation.
Mathematical Models
    Bio-Heat Transfer Model

    Laser Tissue Interaction Model

    Nanoshell Model and Effective Tissue
     Properties

    Cell Damage Model

    Heat Shock Protein Models
Laser-Tissue Interaction
   Ablation: With enough photon energy,
    laser can be used to ionize molecules in
    biological tissue. There exists a threshold
    at which the absorbed energy is high
    enough to cause decomposition of tissue.
   Diffusion Theory          Monte Carlo Method
    – Beer-Lambert’s law       – Easy to implement
    – Inexpensive              – Very expensive
    – Less accurate            – Not suitable for
                                 heterogeneous media
Cell Damage and Heat Shock Protein
   Damage can be determined by
    Arrhenius law
                             Ea
                                  T t
             ln Co C   A e              dt
                        0

   Heat shock proteins (HSP) assist in
    refolding and repairing other             Green Fluorescence HSP27
                                              Red Fluorescence HSP70
    denatured proteins, and facilitating
    synthesis of new proteins in response
    to damage.
   HSP Empirical Model: >1).
              H(t,T) Aexp( t            t )
      Major Problems with Arrhenius Law
              for Tissue Damage
      Experimental data
                                                May need two sets of
                                                 parameters,
          Arrhenius model
                                   Ea / RT
              C(T,t) e      At e
                                                Cannot predict “shoulder”
                                                 effect,
                 T=46oC                         Model parameters are
                                                 extremely large and very
Cv%
                                                 sensitive to measurement
                                                 data,
                                                It is not so clear what is
                                                 the biophysical and
                                                 physiological meaning of
                                                 model parameters
          Heating Time (min)
Results for Human Prostate PC3 Cells

             T=44oC                                               T=50oC




                          Experimental data
                                                                                         E / RT
    T=56oC                                                                       At e
                         Arrhenius Model               C(T,t) e
                                                                                ( H T S ) /T
                           New Model*                                      e
                                                     C (T , t )                    ( H T S ) /T
                                                                        1 e
                      * Feng, Oden, and Rylander, “A Two State model for cell damage: Theory and
                                                   its validation in vitro” J. Biomech Eng., 2008
Biological Quantities of Interest

    How to measure effectiveness of Cancer
     Treatment?
      Cell Damage Fraction Index.

    How to quantify cancer recurrence?
      Heat Shock Protein Expression.

    Control parameter: Temperature.
Objective of Optimization
Nonlinear Transient Bio-Heat Transfer Equation
    Parameters Estimation
   Heterogeneous and nonlinear thermal
    properties: ki(x, t, T), wi(x, t, T)
   Heterogeneous tissue optical properties:
     a(x), s(x), …
   Laser parameters: , P, location, …


     Note: A domain was         ki(x, t, T)
                                              wi(x, t, T)
     discretized into 3700
     elements to characterize
     heterogeneity.
Optimal Control
     Parallel Implementation
 One master processor
 One communication processor


                                 }
 Optimization processors: 60
 Calibration Processors: 60         190 CPU
 Visualization Processors: 70
 Simulation/real time ratio is 10:1
    (e.g. 1 sec ~ 10 sec real time)
 Typical laser surgery time is 3 ~ 5 min.
 Processors numbers and allocation is
  dynamically controlled.
Results
    Remarks

   Imaged guided real time predictive control
   Capability to characterize heterogeneous and
    nonlinear tissue properties on the fly
   Feasibility demonstrated in canine
    experiment
   Computational framework can be
    generalized to other applications
Computational Neuroscience
And Real-Time Cellular Control

Goal:
   Developing neurological pathways of

computational models and real systems
CBI Simulator Framework
CBI Simulator Framework (Cont’d)
                 Software & Hardware Validation
                       Experiment Design


We have designed and implemented a RTXI plugin module that controls the


magnitude of a single channel conductance.

   The conductance magnitude is read from a pre-generated data file.

   The standard model of conductance in an equivalent circuit is used to
    calculate the magnitude of current injected into a real cell (Iinj) based on
    the supplied conductance value (g), the recorded membrane potential (Vm)
    and channel current reversal potential (Er) of a real cell.
UAV and Real-Time
Control

CJ Qian, Ph.D.
UTSA
Activities

Two major research directions
   Research team on UAV: Hardware
    (undergraduate students), control
    algorithms (graduate students)

   Research team on real-time estimation and
    control over the network
Outcomes
   Development of fast convergent estimators/controllers with
    applications to real-time estimation and control of PVTOL
    aircraft




                  Fast convergence of our estimated states (dashed)
                  to the real states(solid)
Future Plan

   Unified Real-Time Computational
    Framework
   Integrated SW/HW Control System
     Thank You!


Questions and Comments?

				
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posted:8/1/2011
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