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

Jonathan Scharff Nielsen,Koen Van Leemput,Jens Morgenthaler Edmund

doi:10.1088/1361-6560/ab5b70

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.

Highlights

Even after multiplying the standard deviations by a factor 10 for a more conservative estimate, the error on the reference means in our experiments should be in the tens of Hounsfield Units (HU), which is an order of magnitude smaller than the variations between metal artifact reduction (MAR) algorithms that we present
As we saw in section 2.2.1, the kernel regression MAR (kerMAR) image is an intermediate between the extreme special cases of pCT and filtered back projection (FBP), and corresponds to a non-trivial blending of the FBP and magnetic resonance (MR)-based prediction
Similar results occurred in the spinal cord, where similar inaccuracies led to an apparent introduction of MR features in the pCT, which were successfully suppressed in the kerMAR

Summary

Introduction

Background Medical x-ray computed tomography (CT) images of patients with metal implants often display major corruption from streak artifacts (Bal and Spies 2006, Jäkel and Reiss 2007), which affects both the visual quality of the images and the quantitative CT value accuracy The latter is a potential hazard in radiotherapy (RT), where the CT values are used in treatment planning to provide electron density and relative stopping power estimates (Andersson et al 2014, Giantsoudi et al 2017). This is of particular concern in head-and-neck RT, where dental implants and fillings occur frequently and are simultaneously close to both the tumor site and critical organs. Other contributions are more model-independent, such as the photon starvation in the metal projections that leads to noise artifacts (Nuyts et al 1998, Gjesteby et al 2016)

Methods

Results

Conclusion