Magnetic resonance-based computed tomography metal artifact reduction using Bayesian modelling

Abstract

Metal artifact reduction (MAR) algorithms reduce the errors caused by metal implants in x-ray computed tomography (CT) images and are an important part of error management in radiotherapy. A promising MAR approach is to leverage the information in magnetic resonance (MR) images that can be 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. Compared 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.

The application of metal artifact reduction methods on computed tomography scans for radiotherapy applications: A literature review

“…These algorithms rely on several different principles. Some approaches are based on image inpainting or sinogram inpainting 72–75 ; some others require the acquisition of additional tilted CT scans, 76 propose novel image acquisition, and reconstruction methods 77,78 ; or require the use of magnetic resonance imaging (MRI) 79,80 or megavoltage CT (MVCT) 81,82 …”

“…Based on the intensity values of pixels on the paired MRI slice and HU values from the artifact‐free CT slice, HU values of pixels on CT art slice are calculated. Nielsen et al evaluated their proposed MR‐based MAR algorithm (kerMAR) 80 to reduce the metal artifacts 84 . kerMAR requires aligned CT and MRI scans of the same anatomy.…”

“…The density correction methods which are available in the TPSs can also be used to reduce the metal artifacts on CT scans for RT applications, see | 13 based on image inpainting or sinogram inpainting [72][73][74][75] ; some others require the acquisition of additional tilted CT scans, 76 propose novel image acquisition, and reconstruction methods 77,78 ; or require the use of magnetic resonance imaging (MRI) 79,80 or megavoltage CT (MVCT). 81,82 For the metal deletion technique (MDT) 75 (Fig.…”

Combined clustered scan-based metal artifact reduction algorithm (CCS-MAR) for ultrasound-guided cardiac radioablation

“…Research-based MAR algorithms have also been evaluated for radiotherapy applications. In particular the metal deletion technique (MDT) [ 41 ], MAR with hardware adaptation [ 42 ], and ker MAR [ 43 ] have been investigated. It was shown that these algorithms can be used to reduce the errors on photon and proton range calculation during treatment planning [ 42 , 44 , 45 ].…”

Effectiveness of orthopedic metal artifact reduction among patients undergoing computed tomography at a tertiary setting – A cross-sectional study

“…Multiple contributions amplified in the presence of metal are the main reason for artifact origin that can be from incorrect assumptions in the CT reconstruction model that is related to the image coefficients arising from X-ray projection (sinogram). 3 Diagnostic ability and accurate distinction of tissue types in the image get affected due to errors in CT numbers. Due to this, dose calculation errors during radiation therapy for cancer treatment get affected as accurate tumor localization, and characterization of surrounding tissues get hindered, affecting treatment success.…”