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U-net-based deformation vector field estimation for motion-compensated 4D-CBCT reconstruction.

Xiaokun Huang1, You Zhang1, Liyuan Chen1

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Convolutional neural network (CNN) models fine-tune deformation vector fields (DVFs) for improved four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction. These CNN-based methods enhance image quality and accuracy in lung imaging, offering faster processing times.

Keywords:
4D-CBCTSMEIRU-netconvolutional neural networklungmotion estimation

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Imaging

Background:

  • Four-dimensional cone-beam computed tomography (4D-CBCT) quality is limited by insufficient projections per respiratory phase.
  • Simultaneous motion estimation and image reconstruction (SMEIR) uses deformation vector fields (DVFs) to improve 4D-CBCT.
  • Standard 2D-3D deformation struggles to accurately capture intra-lung motion due to subtle intensity variations.

Purpose of the Study:

  • Develop and evaluate convolutional neural network (CNN) based methods to fine-tune 2D-3D deformation DVFs.
  • Enhance the efficiency and accuracy of 4D-CBCT reconstruction using CNN-driven DVF optimization.
  • Improve the quality of lung 4D-CBCT images by refining motion models.

Main Methods:

  • Two U-net based CNN architectures (U-net-3C and U-net-4C) were developed for DVF fine-tuning.
  • U-net-4C incorporated a reference phase CBCT image for patient-specific lung properties.
  • Fine-tuned DVFs were re-integrated into the SMEIR workflow for final 4D-CBCT reconstruction.
  • Methods were validated on 11 lung patient cases and the SPARE challenge dataset.

Main Results:

  • CNN-based methods (SMEIR-U-net-3C, SMEIR-U-net-4C) significantly improved 4D-CBCT quality metrics (RMSE, UQI, NCC) compared to original SMEIR and SMEIR-Bio.
  • Residual DVF errors were comparable across SMEIR-U-net-3C (3.88±3.12 mm), SMEIR-U-net-4C (3.71±2.90 mm), and SMEIR-Bio (3.75±3.40 mm), outperforming SMEIR (5.73±4.61 mm).
  • SMEIR-U-net-4C showed slightly superior reconstruction and DVF estimation accuracy.
  • SPARE dataset showed UQI values of 0.96 for SMEIR-U-net-3C, 0.97 for SMEIR-U-net-4C, 0.96 for SMEIR-Bio, and 0.94 for SMEIR.

Conclusions:

  • CNN-based models provide fast (~10s) and accurate fine-tuning of DVFs.
  • These methods significantly enhance the efficiency and accuracy of 4D-CBCT reconstruction.
  • The proposed CNN approaches offer a valuable improvement for lung 4D-CBCT imaging.