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CT-CBCT deformable registration using weakly-supervised artifact-suppression transfer learning network.

Dingshu Tian1,2, Guangyao Sun3,4, Huaqing Zheng4,5

  • 1University of Science and Technology of China, Hefei 230026, People's Republic of China.

Physics in Medicine and Biology
|July 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new two-stage neural network to improve computed tomography-cone beam computed tomography (CT-CBCT) deformable registration for adaptive radiotherapy by suppressing scattering artifacts in CBCT images.

Keywords:
CT-CBCTartifact suppressiondeformable registrationtransfer learning networkweakly supervised

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Artificial Intelligence in Medicine

Background:

  • Deformable registration of CT and CBCT is crucial for adaptive radiotherapy, enabling tumor tracking and organ protection.
  • Current neural network registration methods are sensitive to scattering artifacts in CBCT, leading to reduced accuracy.
  • Artifacts in CBCT inconsistently affect pixel gray values, causing significant registration errors.

Purpose of the Study:

  • To develop a novel method for CT-CBCT deformable registration that effectively suppresses CBCT artifacts.
  • To improve the accuracy and reliability of registration for adaptive radiotherapy applications.
  • To leverage transfer learning and a two-stage network for enhanced artifact handling.

Main Methods:

  • A histogram analysis identified that artifacts are more prominent in the region of disinterest.
  • A weakly supervised, two-stage transfer-learning network was proposed.
  • The first stage employed a pre-training network for artifact suppression in the region of disinterest.
  • The second stage utilized a convolutional neural network for registration of artifact-suppressed CBCT and CT.

Main Results:

  • Comparative tests on thoracic CT-CBCT data demonstrated significant improvements in registration accuracy after artifact suppression.
  • The proposed method outperformed existing algorithms that did not incorporate artifact suppression.
  • The rationality and accuracy of the registration were validated.

Conclusions:

  • The proposed multi-stage neural network effectively suppresses artifacts in CBCT images.
  • The integration of a pre-training technique and attention mechanism further enhances registration performance.
  • This method offers a promising solution for accurate CT-CBCT deformable registration in adaptive radiotherapy.