title: 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.
abstract: OBJECTIVE
  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 superior-inferior 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).
  
  
  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 superior-inferior 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 cases, respectively. In terms of efficiency boost, an overall 3.1 × speedup 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.
authors.affiliation: Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390.
authors.forename: Hao
authors.initials: H
authors.lastname: Yan
authors.affiliation: Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037.
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390.
authors.forename: Feng
authors.initials: F
authors.lastname: Shi
authors.affiliation: Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390 and Institute of Image Processing and Pattern Recognition, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
authors.forename: Ti
authors.initials: T
authors.lastname: Bai
authors.affiliation: Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390 and Department of Physics, University of California San Diego, La Jolla, California 92037.
authors.forename: Michael
authors.initials: M
authors.lastname: Folkerts
authors.affiliation: Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037.
authors.forename: Laura
authors.initials: L
authors.lastname: Cervino
authors.affiliation: Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390.
authors.forename: Steve B
authors.initials: SB
authors.lastname: Jiang
authors.affiliation: Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390.
authors.forename: Xun
authors.initials: X
authors.lastname: Jia
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003196:Computer Graphics; D054893:Cone-Beam Computed Tomography; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D061089:Radiotherapy, Image-Guided; D013997:Time Factors