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Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
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MR-based treatment planning in radiation therapy using a deep learning approach.

Fang Liu1, Poonam Yadav2, Andrew M Baschnagel2

  • 1Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.

Journal of Applied Clinical Medical Physics
|March 13, 2019
PubMed
Summary

Deep learning accurately generates pseudo CT images from MRI for brain tumor radiation therapy planning. This automated approach (deepMTP) yields comparable dose distributions to traditional CT-based plans.

Keywords:
MRIMR-only treatment planningbrain tumorconvolutional neural networkdeep learningradiotherapy

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Artificial Intelligence in Medicine

Background:

  • Magnetic Resonance (MR) imaging is crucial for radiotherapy planning.
  • CT-based imaging is the current standard for electron density information.
  • Generating accurate CT images from MR data is a persistent challenge.

Purpose of the Study:

  • To develop and assess the feasibility of deep learning for MR-based treatment planning (deepMTP) in brain tumor radiotherapy.
  • To create a deep learning pipeline for generating pseudo CT images from MR data.
  • To evaluate the accuracy and clinical utility of deepMTP.

Main Methods:

  • A deep convolutional neural network was trained on 40 retrospective 3D T1-weighted head MR images co-registered with kVCT images.
  • The model generated pseudo CT images from MR data.
  • Treatment plans were created using deepMTP-generated pseudo CT and compared to kVCT-based plans in 10 clinical cases.

Main Results:

  • The deepMTP approach generated accurate pseudo CT images with high Dice coefficients for air (0.95), soft tissue (0.94), and bone (0.85).
  • Mean absolute error was 75 ± 23 HU.
  • Dosimetric parameters, including planning target volume (PTV) volume, maximum dose, and V95, showed no significant difference between deepMTP and kVCT-based plans (P > 0.19).

Conclusions:

  • An automated deep learning approach (deepMTP) was developed to generate continuous-valued pseudo CT images from single 3D MR images.
  • deepMTP is feasible for partial brain tumor treatment planning.
  • The method provides comparable dose distributions to kVCT-based volumetric modulated arc therapy plans.