Optimization of Gamma Knife Radiosurgery - PowerPoint PPT Presentation

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Optimization of Gamma Knife Radiosurgery

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Machine has 201 radiation sources focussed on one location. Very accurate dose delivery ... Dose calculation. Measure dose at distance from shot center ... – PowerPoint PPT presentation

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Title: Optimization of Gamma Knife Radiosurgery


1
Optimization of Gamma Knife Radiosurgery
  • Michael Ferris
  • University of Wisconsin, Computer Sciences
  • David Shepard
  • University of Maryland School of Medicine

2
Overview
  • Details of machine and problem
  • Formulation
  • modeling dose
  • shot / target optimization
  • Results
  • Two-dimensional data
  • Real patient (three-dimensional) data

3
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4
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5
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6
The Leksell Gamma Knife
7
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8
Problem characteristics
  • Machine has 201 radiation sources focussed on one
    location
  • Very accurate dose delivery
  • Benefits of computer solution
  • uniformity of treatment plan
  • better treatment plan
  • faster determination of plan

9
Problem outline
  • Target volume (from MRI or CT)
  • Maximum number of shots to use
  • Which size shots to use
  • Where to place shots
  • How long to deliver shot for
  • Conform to Target (50 isodose curve)
  • Real-time optimization

10
Two-dimensional example
11
Ideal Optimization
12
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13
Dose calculation
  • Measure dose at distance from shot center
  • Fit a nonlinear curve to these measurements
  • Functional form from literature, 6 parameters to
    fit via least-squares

14
8mm shot
15
18mm shot
16
MIP Approach
  • A-priori fix possible shot locations

17
MIP Problem
18
Size Problem
  • Dose(NonTarget) Dose(Rind)
  • Too many shots
  • Generate grid of large shots
  • grid spacing
  • grid offset
  • Small shots randomly placed nr boundary
  • Proportion of each?

19
Features of MIP
  • Large amounts of data/integer variables
  • Shot location on 1mm grid too restrictive
  • Time consuming, even with restrictions and CPLEX
  • but ... have guarantee of global optimality

20
Nonlinear Approach
21
Two-stage approach
  • Approximate via arctan
  • First, solve with approximation, then fix shot
    widths and reoptimize

22
3D slice image
23
Slice 3
24
Axial slice
Manual Computer Optimized
25
Axial slice
Manual Computer Optimized
26
Coronal slice
Manual Computer Optimized
27
Sagittal slice
Manual Computer Optimized
28
Challenges
  • Integration into real system
  • Reduction of optimization time
  • What if scenarios?
  • Improve the objective function
  • Change number of shots
  • Global versus local solutions
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