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A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis

Abass Bahrami1, Alireza Karimian2, Emad Fatemizadeh3

  • 1Faculty of Physics, University of Isfahan, Isfahan, Iran.

Medical Physics
|July 31, 2020
PubMed
Summary

A new efficient convolutional neural network (eCNN) model accurately generates synthetic CT images from MRI, crucial for radiation therapy planning. This deep learning approach requires fewer training subjects and outperforms existing methods like U-Net.

Keywords:
ATLASMRIdeep learningmachine learningpseudo-CT generation

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Artificial Intelligence in Medicine

Background:

  • Multiparametric magnetic resonance imaging (MRI) is valuable in radiation therapy.
  • MRI lacks electron density maps essential for accurate dose calculations.
  • Current MRI-guided treatment planning is thus limited.

Purpose of the Study:

  • To develop a novel deep convolutional neural network (CNN) for generating synthetic computed tomography (sCT) images from MRI.
  • To create an efficient CNN (eCNN) model capable of learning with limited training data.
  • To improve MRI-based radiation treatment planning by providing accurate electron density information.

Main Methods:

  • An efficient CNN (eCNN) model was developed, integrating SegNet and residual network architectures.
  • Maxpooling indices and high-resolution features were incorporated into the decoding layers.
  • A dataset of 15 MRI-CT pairs (1861 images) was used for training and fivefold cross-validation, comparing eCNN against atlas-based methods and U-Net.

Main Results:

  • The eCNN model demonstrated effective learning with only 12 training subjects.
  • eCNN achieved a mean error (ME) of 2.8 ± 10.3 HU and mean absolute error (MAE) of 30.0 ± 10.4 HU.
  • These results were significantly superior to atlas-based and U-Net methods.

Conclusions:

  • The proposed eCNN model converges efficiently with limited training data.
  • eCNN generates accurate synthetic CT images, outperforming U-Net and atlas-based techniques.
  • This advancement enhances MRI-guided radiation therapy planning.