Department of Health Technology, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark.
Radiotherapy Research Unit, Department of Oncology, Gentofte and Herlev Hospital, University of Copenhagen, 2730, Herlev, Denmark.
Med Phys. 2019 Oct;46(10):4314-4323. doi: 10.1002/mp.13729. Epub 2019 Aug 10.
We investigated the impact on computed tomography (CT) image quality and photon, electron, and proton head-and-neck (H&N) radiotherapy (RT) dose calculations of three CT metal artifact reduction (MAR) approaches: A CT-based algorithm (oMAR Philips Healthcare), manual water override, and our recently presented, Magnetic Resonance (MR)-based kerMAR algorithm. We considered the following three hypotheses: I: Manual water override improves MAR over the CT- and MR-based alternatives; II: The automatic algorithms (oMAR and kerMAR) improve MAR over the uncorrected CT; III: kerMAR improves MAR over oMAR.
We included a veal shank phantom with/without six metal inserts and nine H&N RT patients with dental implants. We quantified the MAR capabilities by the reduction of outliers in the CT value distribution in regions of interest, and the change in particle range and photon depth at maximum dose.
Water override provided apparent image improvements in the soft tissue region but insignificantly or negatively influenced the dose calculations. We however found significant improvements in image quality and particle range impact, compared to the uncorrected CT, when using oMAR and kerMAR. kerMAR in turn provided superior improvements in terms of high intensity streak suppression compared to oMAR, again with associated impacts on the particle range estimates.
We found no benefits of the water override compared to the rest, and tentatively reject hypothesis I. We however found improvements in the automatic algorithms, and thus support for hypothesis II, and found the MR-based kerMAR to improve upon oMAR, supporting hypothesis III.
我们研究了三种 CT 金属伪影减少（MAR）方法对 CT 图像质量和光子、电子和质子头颈部（H＆N）放疗（RT）剂量计算的影响：基于 CT 的算法（飞利浦医疗保健 oMAR）、手动水覆盖和我们最近提出的基于磁共振（MR）的 kerMAR 算法。我们考虑了以下三个假设：I：手动水覆盖优于基于 CT 和基于 MR 的替代方案；II：自动算法（oMAR 和 kerMAR）改善了未经校正的 CT 的 MAR；III：kerMAR 改善了 oMAR 的 MAR。
我们纳入了一个带有/不带有六个金属插入物的小牛胫骨体模和九个带有牙种植体的 H＆N RT 患者。我们通过感兴趣区域中 CT 值分布中的异常值减少和粒子射程和光子最大剂量深度的变化来量化 MAR 能力。
水覆盖在软组织区域提供了明显的图像改善，但对剂量计算的影响不大或为负。然而，与未经校正的 CT 相比，当使用 oMAR 和 kerMAR 时，我们发现图像质量和粒子射程影响有显著改善。kerMAR 与 oMAR 相比，在高强度条纹抑制方面提供了更好的改善，同样对粒子射程估计也有影响。
与其他方法相比，我们没有发现水覆盖的优势，因此暂时否定假设 I。然而，我们发现自动算法有所改进，因此支持假设 II，并且发现基于 MR 的 kerMAR 优于 oMAR，支持假设 III。
