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Reducing axial truncation artifacts in iterative cone-beam CT for radiation therapy using a priori preconditioned information

Journal Article


Abstract


  • Purpose: Cone-beam computed tomography (CBCT) is increasingly utilized in radiation therapy for image guidance and adaptive applications. While iterative reconstruction algorithms have been shown to outperform traditional filtered back-projection methods in improving image quality and reducing imaging dose, they cannot handle data truncation in the axial view, which frequently occurs in the full-fan partial-trajectory acquisition mode. This proof-of-concept study presents a novel approach on truncation artifact reduction by utilizing a priori preconditioned information as the initial input for the iterative algorithm. Methods: Projections containing axial truncation were used for image reconstruction in extended axial field-of-view (AFOV) using the conjugate gradient least-squares (CGLS) algorithm. A priori information in the form of a planning fan-beam CT (FBCT) was repositioned in the expected CBCT imaging geometry, then further processed to dampen high-density features and convolved with a cubic Gaussian kernel to ensure differentiability for the gradient descent method. Anatomical and positional differences between the estimated and the actual imaging object were introduced to verify the efficacy of the proposed method. Results: Extending the reconstruction AFOV alone could partially reduce truncation artifact. Using a priori information directly resulted in ghosting artifact when there were anatomical and positional differences between the estimated and the actual imaging object. Using a priori preconditioned information was shown to effectively reduce truncation artifact and recover peripheral information. Conclusions: Using a priori preconditioned information can effectively alleviate truncation artifact and assist recovery of peripheral information in iterative CBCT reconstruction.

Publication Date


  • 2021

Citation


  • Cai, M., Byrne, M., Archibald-Heeren, B., Metcalfe, P., Rosenfeld, A., & Wang, Y. (2021). Reducing axial truncation artifacts in iterative cone-beam CT for radiation therapy using a priori preconditioned information. Medical Physics, 48(11), 7089-7098. doi:10.1002/mp.15248

Scopus Eid


  • 2-s2.0-85116744226

Web Of Science Accession Number


Start Page


  • 7089

End Page


  • 7098

Volume


  • 48

Issue


  • 11

Abstract


  • Purpose: Cone-beam computed tomography (CBCT) is increasingly utilized in radiation therapy for image guidance and adaptive applications. While iterative reconstruction algorithms have been shown to outperform traditional filtered back-projection methods in improving image quality and reducing imaging dose, they cannot handle data truncation in the axial view, which frequently occurs in the full-fan partial-trajectory acquisition mode. This proof-of-concept study presents a novel approach on truncation artifact reduction by utilizing a priori preconditioned information as the initial input for the iterative algorithm. Methods: Projections containing axial truncation were used for image reconstruction in extended axial field-of-view (AFOV) using the conjugate gradient least-squares (CGLS) algorithm. A priori information in the form of a planning fan-beam CT (FBCT) was repositioned in the expected CBCT imaging geometry, then further processed to dampen high-density features and convolved with a cubic Gaussian kernel to ensure differentiability for the gradient descent method. Anatomical and positional differences between the estimated and the actual imaging object were introduced to verify the efficacy of the proposed method. Results: Extending the reconstruction AFOV alone could partially reduce truncation artifact. Using a priori information directly resulted in ghosting artifact when there were anatomical and positional differences between the estimated and the actual imaging object. Using a priori preconditioned information was shown to effectively reduce truncation artifact and recover peripheral information. Conclusions: Using a priori preconditioned information can effectively alleviate truncation artifact and assist recovery of peripheral information in iterative CBCT reconstruction.

Publication Date


  • 2021

Citation


  • Cai, M., Byrne, M., Archibald-Heeren, B., Metcalfe, P., Rosenfeld, A., & Wang, Y. (2021). Reducing axial truncation artifacts in iterative cone-beam CT for radiation therapy using a priori preconditioned information. Medical Physics, 48(11), 7089-7098. doi:10.1002/mp.15248

Scopus Eid


  • 2-s2.0-85116744226

Web Of Science Accession Number


Start Page


  • 7089

End Page


  • 7098

Volume


  • 48

Issue


  • 11