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The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
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Related Experiment Video

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Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
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Quantum-inspired algorithm for radiotherapy planning optimization.

Julia M Pakela1,2, Huan-Hsin Tseng2, Martha M Matuszak2

  • 1Applied Physics Program, University of Michigan, Ann Arbor, MI, USA.

Medical Physics
|October 2, 2019
PubMed
Summary
This summary is machine-generated.

Quantum tunnel annealing (QTA), a new optimization algorithm for intensity-modulated radiotherapy (IMRT), shows faster convergence than simulated annealing (SA). This quantum-inspired method may improve radiation therapy planning efficiency and effectiveness.

Keywords:
IMRTadaptive radiotherapyquantum tunneling optimizationsimulated annealing

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Area of Science:

  • Medical Physics
  • Computational Science
  • Radiotherapy Optimization

Background:

  • Modern radiotherapy treatment planning involves complex, large-scale optimizations.
  • These optimizations must be solved within clinically feasible timeframes.
  • Existing stochastic methods like Simulated Annealing (SA) face challenges with convergence and efficiency.

Purpose of the Study:

  • To develop and test a quantum-inspired stochastic algorithm, Quantum Tunnel Annealing (QTA), for intensity-modulated radiotherapy (IMRT) treatment planning.
  • To evaluate QTA's performance in terms of convergence rates and plan quality compared to SA.
  • To investigate QTA's potential for improving the efficiency and effectiveness of radiotherapy planning.

Main Methods:

  • Two stereotactic body radiation therapy (SBRT) liver cases of variable complexity were used to analyze QTA.
  • Plan quality was assessed using dose-volume histogram objectives and dose distributions.
  • QTA convergence rates were investigated in relation to barrier width, and performance was compared to SA across various parameters including initialization, annealing schedules, and constraint complexity.

Main Results:

  • QTA demonstrated faster convergence rates than SA, up to 46.6% for beamlet intensity optimization and 26.8% for direct aperture optimization (DAO).
  • Both QTA and SA were equally insensitive to initialization, but QTA's convergence was more sensitive to dose-volume constraint complexity.
  • An optimal smoothing technique combining LOG and Savitzky-Golay filters resulted in plans with over 20% fewer monitor units compared to commercial software.

Conclusions:

  • QTA is a characterized stochastic, quantum-inspired optimization algorithm suitable for radiotherapy treatment planning.
  • QTA's tunable barrier-width function allows for faster convergence compared to SA.
  • QTA shows promise for future knowledge-based or adaptive radiation therapy applications.