Multidimensional noise reduction in C-arm cone-beam CT via 2D-based Landweber iteration and 3D-based deep neural networks

Dahim Choi, Juhee Kim, Seung Hoon Chae, Byeongjoon Kim, Jongduk Baek, Andreas Maier, Rebecca Fahrig, Hyun Seok Park, Jang Hwan Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, the necessity of using low-dose CT imaging with reduced noise has come to the forefront due to the risks involved in radiation. In order to acquire a high-resolution image from a low-resolution image which produces a relatively small amount of radiation, various algorithms including deep learning-based methods have been proposed. However, the current techniques have shown limited performance, especially with regard to losing fine details and blurring high-frequency edges. To enhance the previously suggested 2D patch-based denoising model, we have suggested the 3D block-based REDCNN model, employing convolution layers paired with deconvolution layers, shortcuts, and residual mappings. This process allows us to preserve the image structure and diagnostic features of an image, increasing image resolution by smoothing noise. Finally, we applied a bilateral filter in 3D and utilized a 2D-based Landweber iteration method to reduce remaining noise under a certain amplitude and prevent the edges from blurring. As a result, our proposed method effectively reduced Poisson noise level without losing diagnostic features and showed high performance in both qualitative and quantitative evaluation methods compared to ResNet2D, ResNet3D, REDCNN2D, and REDCNN3D.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationPhysics of Medical Imaging
EditorsTaly Gilat Schmidt, Guang-Hong Chen, Hilde Bosmans
PublisherSPIE
ISBN (Electronic)9781510625433
DOIs
Publication statusPublished - 2019 Jan 1
EventMedical Imaging 2019: Physics of Medical Imaging - San Diego, United States
Duration: 2019 Feb 172019 Feb 20

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10948
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Physics of Medical Imaging
CountryUnited States
CitySan Diego
Period19/2/1719/2/20

Fingerprint

Cone-Beam Computed Tomography
Image resolution
Noise abatement
noise reduction
iteration
Noise
Cones
cones
blurring
image resolution
Radiation
Deconvolution
Convolution
radiation
convolution integrals
smoothing
learning
Imaging techniques
Learning
filters

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Choi, D., Kim, J., Chae, S. H., Kim, B., Baek, J., Maier, A., ... Choi, J. H. (2019). Multidimensional noise reduction in C-arm cone-beam CT via 2D-based Landweber iteration and 3D-based deep neural networks. In T. G. Schmidt, G-H. Chen, & H. Bosmans (Eds.), Medical Imaging 2019: Physics of Medical Imaging [1094837] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10948). SPIE. https://doi.org/10.1117/12.2512723
Choi, Dahim ; Kim, Juhee ; Chae, Seung Hoon ; Kim, Byeongjoon ; Baek, Jongduk ; Maier, Andreas ; Fahrig, Rebecca ; Park, Hyun Seok ; Choi, Jang Hwan. / Multidimensional noise reduction in C-arm cone-beam CT via 2D-based Landweber iteration and 3D-based deep neural networks. Medical Imaging 2019: Physics of Medical Imaging. editor / Taly Gilat Schmidt ; Guang-Hong Chen ; Hilde Bosmans. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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Choi, D, Kim, J, Chae, SH, Kim, B, Baek, J, Maier, A, Fahrig, R, Park, HS & Choi, JH 2019, Multidimensional noise reduction in C-arm cone-beam CT via 2D-based Landweber iteration and 3D-based deep neural networks. in TG Schmidt, G-H Chen & H Bosmans (eds), Medical Imaging 2019: Physics of Medical Imaging., 1094837, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10948, SPIE, Medical Imaging 2019: Physics of Medical Imaging, San Diego, United States, 19/2/17. https://doi.org/10.1117/12.2512723

Multidimensional noise reduction in C-arm cone-beam CT via 2D-based Landweber iteration and 3D-based deep neural networks. / Choi, Dahim; Kim, Juhee; Chae, Seung Hoon; Kim, Byeongjoon; Baek, Jongduk; Maier, Andreas; Fahrig, Rebecca; Park, Hyun Seok; Choi, Jang Hwan.

Medical Imaging 2019: Physics of Medical Imaging. ed. / Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans. SPIE, 2019. 1094837 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10948).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Choi D, Kim J, Chae SH, Kim B, Baek J, Maier A et al. Multidimensional noise reduction in C-arm cone-beam CT via 2D-based Landweber iteration and 3D-based deep neural networks. In Schmidt TG, Chen G-H, Bosmans H, editors, Medical Imaging 2019: Physics of Medical Imaging. SPIE. 2019. 1094837. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2512723