Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy

Cone/ring artifact correction and multiple GPU implementation

Hao Yan, Xiaoyu Wang, Feng Shi, Ti Bai, Michael Folkerts, Laura Cervino, Steve B. Jiang, Xun Jia

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Purpose: Compressed sensing (CS)-based iterative reconstruction (IR) techniques are able to reconstruct cone-beam CT (CBCT) images from undersampled noisy data, allowing for imaging dose reduction. However, there are a few practical concerns preventing the clinical implementation of these techniques. On the image quality side, data truncation along the superiorinferior direction under the cone-beam geometry produces severe cone artifacts in the reconstructed images. Ring artifacts are also seen in the half-fan scan mode. On the reconstruction efficiency side, the long computation time hinders clinical use in image-guided radiation therapy (IGRT). 2014 American Association of Physicists in Medicin.

Methods: Image quality improvement methods are proposed to mitigate the cone and ring image artifacts in IR. The basic idea is to use weighting factors in the IR data fidelity term to improve projection data consistency with the reconstructed volume. In order to improve the computational efficiency, a multiple graphics processing units (GPUs)-based CS-IR system was developed. The parallelization scheme, detailed analyses of computation time at each step, their relationship with image resolution, and the acceleration factors were studied. The whole system was evaluated in various phantom and patient cases.

Results: Ring artifacts can be mitigated by properly designing a weighting factor as a function of the spatial location on the detector. As for the cone artifact, without applying a correction method, it contaminated 13 out of 80 slices in a head-neck case (full-fan). Contamination was even more severe in a pelvis case under half-fan mode, where 36 out of 80 slices were affected, leading to poorer soft tissue delineation and reduced superiorinferior coverage. The proposed method effectively corrects those contaminated slices with mean intensity differences compared to FDK results decreasing from ∼497 and ∼293 HU to ∼39 and ∼27 HU for the full-fan and half-fan ases, respectively. In terms of efficiency boost, an overall 3.1Xspeedup factor has been achieved with four GPU cards compared to a single GPU-based reconstruction. The total computation time is ∼30 s for typical clinical cases.

Conclusions: The authors have developed a low-dose CBCT IR system for IGRT. By incorporating data consistency-based weighting factors in the IR model, cone/ring artifacts can be mitigated. A boost in computational efficiency is achieved by multi-GPU implementation.

Original languageEnglish (US)
Article number111912
JournalMedical Physics
Volume41
Issue number11
DOIs
StatePublished - Nov 1 2014

Fingerprint

Image-Guided Radiotherapy
Artifacts
Cone-Beam Computed Tomography
Quality Improvement
Pelvis
Neck
Head

Keywords

  • artifact
  • CBCT
  • GPU
  • iterative reconstruction
  • low-dose

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy : Cone/ring artifact correction and multiple GPU implementation. / Yan, Hao; Wang, Xiaoyu; Shi, Feng; Bai, Ti; Folkerts, Michael; Cervino, Laura; Jiang, Steve B.; Jia, Xun.

In: Medical Physics, Vol. 41, No. 11, 111912, 01.11.2014.

Research output: Contribution to journalArticle

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abstract = "Purpose: Compressed sensing (CS)-based iterative reconstruction (IR) techniques are able to reconstruct cone-beam CT (CBCT) images from undersampled noisy data, allowing for imaging dose reduction. However, there are a few practical concerns preventing the clinical implementation of these techniques. On the image quality side, data truncation along the superiorinferior direction under the cone-beam geometry produces severe cone artifacts in the reconstructed images. Ring artifacts are also seen in the half-fan scan mode. On the reconstruction efficiency side, the long computation time hinders clinical use in image-guided radiation therapy (IGRT). 2014 American Association of Physicists in Medicin.Methods: Image quality improvement methods are proposed to mitigate the cone and ring image artifacts in IR. The basic idea is to use weighting factors in the IR data fidelity term to improve projection data consistency with the reconstructed volume. In order to improve the computational efficiency, a multiple graphics processing units (GPUs)-based CS-IR system was developed. The parallelization scheme, detailed analyses of computation time at each step, their relationship with image resolution, and the acceleration factors were studied. The whole system was evaluated in various phantom and patient cases.Results: Ring artifacts can be mitigated by properly designing a weighting factor as a function of the spatial location on the detector. As for the cone artifact, without applying a correction method, it contaminated 13 out of 80 slices in a head-neck case (full-fan). Contamination was even more severe in a pelvis case under half-fan mode, where 36 out of 80 slices were affected, leading to poorer soft tissue delineation and reduced superiorinferior coverage. The proposed method effectively corrects those contaminated slices with mean intensity differences compared to FDK results decreasing from ∼497 and ∼293 HU to ∼39 and ∼27 HU for the full-fan and half-fan ases, respectively. In terms of efficiency boost, an overall 3.1Xspeedup factor has been achieved with four GPU cards compared to a single GPU-based reconstruction. The total computation time is ∼30 s for typical clinical cases.Conclusions: The authors have developed a low-dose CBCT IR system for IGRT. By incorporating data consistency-based weighting factors in the IR model, cone/ring artifacts can be mitigated. A boost in computational efficiency is achieved by multi-GPU implementation.",
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