Improving radiotherapy planning in patients with metallic implants using the iterative metal artifact reduction (iMAR) algorithm

Abstract

Metal artifacts in computed tomography (CT) images can cause important errors in dose calculation. Algorithms developed to reduce these artifacts could improve these images but also introduce a bias in tissues surrounding metallic implants. The purpose of this study is to validate a pre-commercial metal artifact reduction tool for radiotherapy treatment planning. The performance of the iterative metal artifact reduction (iMAR) algorithm developed by Siemens Healthcare is evaluated on a test platform. A calibration phantom constituted of tissue-equivalent plastics is used to estimate the image bias from artifact correction. Patient CT data with metal implants (seven dental fillings, one bilateral hip) are reconstructed using weighted filtered backprojection and corrected using the iMAR algorithm. Radiotherapy treatment plans are calculated and compared on corrected and uncorrected images using the collapsed cone convolution dose algorithm implemented in RayStation (RaySearch). Phantom scans show that iMAR reproduces CT numbers within ±44 HU for tissue equivalent substitutes in 3.6 cm2 circular ROIs. The effect of this CT number difference on megavoltage photon dose calculation is shown to be within ±1% dose error. Comparing patient plans from corrected and uncorrected images, dose differences of up to ±5% are discovered in target volume and organs at risk, depending on the treatment site. The iMAR algorithm resulted in improvement of image quality in metal artifact affected CT images for radiotherapy planning. The technique reduces dose errors significantly while keeping calculated doses in the surrounding tissues within a clinically acceptable level in comparison to ground truth. Future work could aim at the improvement of benchmark methods for a clinical environment.