CycN-Net : A Convolutional Neural Network Specialized for 4D CBCT Images Refinement

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Shaohua Zhi
  • Marc Kachelrieß
  • Fei Pan
  • Xuanqin Mou

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)3054-3064
Journal / PublicationIEEE Transactions on Medical Imaging
Volume40
Issue number11
Online published19 May 2021
Publication statusPublished - Nov 2021

Abstract

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts and noise because the phase-resolved image is an extremely sparse-view CT procedure wherein a few under-sampled projections are used for the reconstruction of each phase. Aiming at improving the overall quality of 4D CBCT images, we proposed two CNN models, named N-Net and CycN-Net, respectively, by fully excavating the inherent property of 4D CBCT. To be specific, the proposed N-Net incorporates the prior image reconstructed from entire projection data based on U-Net to boost the image quality for each phase-resolved image. Based on N-Net, a temporal correlation among the phase-resolved images is also considered by the proposed CycN-Net. Extensive experiments on both XCAT simulation data and real patient 4D CBCT datasets were carried out to verify the feasibility of the proposed CNNs. Both networks can effectively suppress streaking artifacts and noise while restoring the distinct features simultaneously, compared with the existing CNN models and two state-of-the-art iterative algorithms. Moreover, the proposed method is robust in handling complicated tasks of various patient datasets and imaging devices, which implies its excellent generalization ability.

Research Area(s)

  • 4D Cone Beam Computed Tomography (4D CBCT), Artifact reduction, Computed tomography, Correlation, Deep learning, Image reconstruction, Image restoration, Imaging, Logic gates, Prior knowledge, Spatiotemporal resolution, Training

Citation Format(s)

CycN-Net : A Convolutional Neural Network Specialized for 4D CBCT Images Refinement. / Zhi, Shaohua; Kachelrieß, Marc; Pan, Fei; Mou, Xuanqin.

In: IEEE Transactions on Medical Imaging, Vol. 40, No. 11, 11.2021, p. 3054-3064.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review