title: Shading correction for on-board cone-beam CT in radiation therapy using planning MDCT images.
abstract: OBJECTIVE
  Applications of cone-beam CT (CBCT) to image-guided radiationtherapy (IGRT) are hampered by shading artifacts in the reconstructed images. These artifacts are mainly due to scatter contamination in the projections but also can result from uncorrected beam hardening effects as well as nonlinearities in responses of the amorphous silicon flat panel detectors. While currently, CBCT is mainly used to provide patient geometry information for treatment setup, more demanding applications requiring high-quality CBCT images are under investigation. To tackle these challenges, many CBCT correction algorithms have been proposed; yet, a standard approach still remains unclear. In this work, we propose a shading correction method for CBCT that addresses artifacts from low-frequency projection errors. The method is consistent with the current workflow of radiation therapy.
  
  
  METHODS
  With much smaller inherent scatter signals and more accurate detectors, diagnostic multidetector CT (MDCT) provides high quality CT images that are routinely used for radiation treatment planning. Using the MDCT image as "free" prior information, we first estimate the primary projections in the CBCT scan via forward projection of the spatially registered MDCT data. Since most of the CBCT shading artifacts stem from low-frequency errors in the projections such as scatter, these errors can be accurately estimated by low-pass filtering the difference between the estimated and raw CBCT projections. The error estimates are then subtracted from the raw CBCT projections. Our method is distinct from other published correction methods that use the MDCT image as a prior because it is projection-based and uses limited patient anatomical information from the MDCT image. The merit of CBCT-based treatment monitoring is therefore retained.
  
  
  RESULTS
  The proposed method is evaluated using two phantom studies on tabletop systems. On the Catphan 600 phantom, our approach reduces the reconstruction error from 348 Hounsfield unit (HU) without correction to 4 HU around the object center after correction, and from 375 HU to 17 HU in the high-contrast regions. In the selected regions of interest (ROIs), the average image contrast is increased by a factor of 3.3. When noise suppression is implemented, the proposed correction substantially improves the contrast-to-noise ratio (CNR) and therefore the visibility of low-contrast objects, as seen in a more challenging pelvis phantom study. Besides a significant improvement in image uniformity, a low-contrast object of approximately 25 HU, which is otherwise buried in the shading artifacts, can be clearly identified after the proposed correction due to a CNR increase of 3.1. Compared to a kernel-based scatter correction method coupled with an analytical beam hardening correction, our approach also shows an overall improved performance with some residual artifacts.
  
  
  CONCLUSIONS
  By providing effective shading correction, our approach has the potential to improve the accuracy of more advanced CBCT-based clinical applications for IGRT, such as tumor delineation and dose calculation.
authors.affiliation: Nuclear and Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
authors.forename: Tianye
authors.initials: T
authors.lastname: Niu
authors.forename: Mingshan
authors.initials: M
authors.lastname: Sun
authors.forename: Josh
authors.initials: J
authors.lastname: Star-Lack
authors.forename: Hewei
authors.initials: H
authors.lastname: Gao
authors.forename: Qiyong
authors.initials: Q
authors.lastname: Fan
authors.forename: Lei
authors.initials: L
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D055592:Biophysical Phenomena; D054893:Cone-Beam Computed Tomography / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D010386:Pelvic Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D010388:Pelvis / Q000000981:diagnostic imaging; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000706:statistics & numerical data; D012542:Scattering, Radiation