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Generating Pareto Optimal Dose Distributions for Radiation Therapy Treatment Planning

  • Dan NguyenEmail author
  • Azar Sadeghnejad Barkousaraie
  • Chenyang Shen
  • Xun Jia
  • Steve Jiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Radiotherapy treatment planning currently requires many trial-and-error iterations between the planner and treatment planning system, as well as between the planner and physician for discussion/consultation. The physician’s preferences for a particular patient cannot be easily quantified and precisely conveyed to the planner. In this study we present a real-time volumetric Pareto surface dose generation deep learning neural network that can be used after segmentation by the physician, adding a tangible and quantifiable endpoint to portray to the planner. From 70 prostate patients, we first generated 84,000 intensity modulated radiation therapy plans (1,200 plans per patient) sampling the Pareto surface, representing various tradeoffs between the planning target volume (PTV) and the organs-at-risk (OAR), including bladder, rectum, left femur, right femur, and body. We divided the data to 10 test patients and 60 training/validation patients. We then trained a hierarchically densely connected convolutional U-net (HD U-net), to take the PTV and avoidance map representing OARs masks and weights, and predict the optimized plan. The HD U-net is capable of accurately predicting the 3D Pareto optimal dose distributions, with average [mean, max] dose errors of [3.4%, 7.7%](PTV), [1.6%, 5.6%](bladder), [3.7%, 4.2%](rectum), [3.2%, 8.0%](left femur), [2.9%, 7.7%](right femur), and [0.04%, 5.4%](body) of the prescription dose. The PTV dose coverage prediction was also very similar, with errors of 1.3% (D98) and 2.0% (D99). Homogeneity was also similar, differing by 0.06 on average. The neural network can predict the dose within 1.7 s. Clinically, the optimization and dose calculation is much slower, taking 5–10 min.

Keywords

Radiation therapy treatment planning Intensity modulation Pareto surface Dose distribution Deep learning U-net Neural network 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dan Nguyen
    • 1
    Email author
  • Azar Sadeghnejad Barkousaraie
    • 1
  • Chenyang Shen
    • 1
  • Xun Jia
    • 1
  • Steve Jiang
    • 1
  1. 1.Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation OncologyUT Southwestern Medical CenterDallasUSA

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