TY - JOUR
T1 - MR-based CT metal artifact reduction using Bayesian modelling
AU - Nielsen, Jonathan Scharff
AU - Edmund, Jens Morgenthaler
AU - Van Leemput, Koen
PY - 2019
Y1 - 2019
N2 - Metal artifact reduction (MAR) algorithms reduce the errors caused by metal implants in xray computed tomography (CT) images and are an important part of error management in radiotherapy (RT). A promising MAR approach is to leverage the information in magnetic resonance (MR) images that are acquired for organ or tumor delineation. This is however complicated by the ambiguous relationship between CT values and conventional-sequence MR intensities as well as potential co-registration issues. In order to address these issues, this paper proposes a self-tuning Bayesian model for MR-based MAR that combines knowledge of the MR image intensities in local spatial neighborhoods with the information in an initial, corrupted CT reconstructed using filtered back projection. We demonstrate the potential of the resulting model in three widely-used MAR scenarios: image inpainting, sinogram inpainting and model-based iterative reconstruction. Comparing to conventional alternatives in a retrospective study on nine head-and-neck patients with CT and T1-weighted MR scans, we find improvements in terms of image quality and quantitative CT value accuracy within each scenario. We conclude that the proposed model provides a versatile way to use the anatomical information in a co-acquired MR scan to boost the performance of MAR algorithms.
AB - Metal artifact reduction (MAR) algorithms reduce the errors caused by metal implants in xray computed tomography (CT) images and are an important part of error management in radiotherapy (RT). A promising MAR approach is to leverage the information in magnetic resonance (MR) images that are acquired for organ or tumor delineation. This is however complicated by the ambiguous relationship between CT values and conventional-sequence MR intensities as well as potential co-registration issues. In order to address these issues, this paper proposes a self-tuning Bayesian model for MR-based MAR that combines knowledge of the MR image intensities in local spatial neighborhoods with the information in an initial, corrupted CT reconstructed using filtered back projection. We demonstrate the potential of the resulting model in three widely-used MAR scenarios: image inpainting, sinogram inpainting and model-based iterative reconstruction. Comparing to conventional alternatives in a retrospective study on nine head-and-neck patients with CT and T1-weighted MR scans, we find improvements in terms of image quality and quantitative CT value accuracy within each scenario. We conclude that the proposed model provides a versatile way to use the anatomical information in a co-acquired MR scan to boost the performance of MAR algorithms.
U2 - 10.1088/1361-6560/ab5b70
DO - 10.1088/1361-6560/ab5b70
M3 - Journal article
C2 - 31766033
SN - 0031-9155
VL - 64
JO - Physics in Medicine and Biology
JF - Physics in Medicine and Biology
M1 - 245012
ER -