Physical Aspects of IMRT Samuel Tung, M.S. Sr. Medical Physicist - - PowerPoint PPT Presentation

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Physical Aspects of IMRT Samuel Tung, M.S. Sr. Medical Physicist - - PowerPoint PPT Presentation

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Physical Aspects of IMRT Samuel Tung, M.S. Sr. Medical Physicist UT MD Anderson Cancer Center 3D/IMRT Comparison IMRT Techniques Conventional Beam modifiers (wedge, partial blocks) Compensators LINAC, Proton therapy

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Physical Aspects of IMRT

Samuel Tung, M.S.

  • Sr. Medical Physicist

UT MD Anderson Cancer Center

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3D/IMRT Comparison

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IMRT Techniques

  • Conventional – Beam modifiers (wedge,

partial blocks)

  • Compensators – LINAC, Proton therapy
  • Computerized MLCs – LINAC
  • Binary MLCs – PEACOCK, Tomotherapy
  • Robot-Controlled – Cyberknife
  • Scanning Beams – Proton therapy (IMPT)
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IMRT Delivery

  • Step and Shoot
  • Sliding Window
  • VMAT
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IMRT Delivery: Step and Shoot

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IMRT Delivery: Sliding Window

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IMRT Delivery : VMAT

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Motivation?

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Benefits of Using IMRT

  • Dose reductions to normal tissue
  • Dose Escalation to target structures
  • Improves target coverage of complex tumor

shapes, e.g. tumor wraps around brainstem or spinal cord

  • Ability to delivers different doses to different

targets

  • Ideal for reducing doses to critical structures
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IMRT Inverse Planning

  • Optimization Process for Fixed Field IMRT
  • Beamlet Based Optimization
  • Direct Aperture Optimization (DAO)
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The Beamlet Model

  • Before an IMRT
  • ptimization, each

beam is defined and divided into a number

  • f smaller beamlets

(pencil beams), usually 5 mm x 5 mm

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The Beamlet Model

  • The corresponding dose

distributions from all beamlets are computed and added together.

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The Beamlet Model

  • Beamlet weights are
  • ptimized to produce

an optimized fluence map or matrix for each beam direction.

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The Beamlet Two-Steps Model

  • Leaf Sequencing: From “ideal” fluence, the

“deliverable” MLC patterns are generated map base on machine characteristics.

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The Beamlet Two-Steps Model

  • The final “full” dose is calculated from all

small beam segments (control points)

  • Requires a large number of segments in
  • rder to simulate the “ideal” map
  • Small field segments cause significant

degradation in the plan quality

  • What you see from “ideal” fluence is

“NOT” what you get from small fields

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NOMOS CORVUS Plan (2002)

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NOMOS CORVUS Plan (2002)

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IMRT Dosimetry - Small Fields

?

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Dose Modeling Problem

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Dose Modeling Problem

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Dose Modeling Problem

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IMRT Planning Process

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The Beamlet Two-Steps Model

  • 1st Generation IMRT was adopted

by nearly all TPS in1990:

  • Corvus (NOMOS) – Sliding Window
  • Pinnacle (ADAC) – Step and Shoot
  • Eclipse (Varian) – Sliding Window
  • Plato (Nucletron)
  • Xio (CMS)
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Direct Aperture Optimization (DAO)

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Direct Aperture Optimization (DAO)

  • Inverse planning technique where both

the beam shapes and the beam weights are optimized at the same time

  • All of the MLC delivery parameters are

included in the optimization (DMPO)

  • Number of beam segments and

minimum MU per segment can be also predefined

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DAO via Simulated Annealing

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1 cm

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DMPO Constraints

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DMPO Summary

  • Plan Quality
  • Total cost function ↓ 50% => Better

normal tissue protection with more uniform dose to all target volumes

  • Treatment delivery
  • Total MU ↓ 40% => Less Tx time
  • Segments ↓ 50% => Less down time
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VMAT / IMAT

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IMAT / VMAT Optimization

  • IMAT treatment planning represents a

particular complex optimization problem. ü The size of the problem ü Dynamic motion ü Motion limitation ü The dose calculation time

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N and n Optimization: An Intermediate Case

Comparison of Dose Conversion Iteration Case #6: 5235 Parameters

0.2 0.4 0.6 0.8 1 2 4 6 8 10 12 14 16 18 20 Dose Conversion Iteration Normalized Total O.V.

N = 5 N = 8 N = 10 N = 12 N = 15

MU as Function of Conversion Iterations Case # 6: 5235 Parameters

0.2 0.4 0.6 0.8 1 5 10 15 20 Dose Conversion Iteration Normalized MU N = 5 N = 8 N = 10 N = 12 N = 15

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HN cases