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How bone mis‐segmentation affects MRI‐based sCT dosimetry in head and neck radiotherapy: A CT‐referenced study

Aug 2026 · Journal of Applied Clinical Medical Physics · Vol 27 · 0 citations · 33 references
Medicine

Abstract

Abstract Background Magnetic resonance imaging (MRI) is commonly used for head‐and‐neck (H&N) delineation in radiotherapy (RT) owing to the lack of contrast in computed tomography (CT) images. MRI‐based dosimetry is not yet fully developed for the H&N area due to the inter‐patient variability and the difficulty of accurately quantifying cortical bone based on MRI measure. Purpose Despite the difficulty of cortical bone segmentation with nearby air area, most papers in the literature show dosimetry results very close to the reference CT scan dosimetry. This study evaluated the impact of bone segmentation on dosimetry. Methods A home‐made phantom was used to evaluate the Hounsfield unit (HU) values of different tissue at diverse scanner energies. Different dosimetry plans were considered and compared with the reference CT‐based RT plan used for the treatment: CT images with cortical bone voxels assigned to the density of water and three based on MR images using a density assignment (DA) method for synthetic CT (sCT) generation with various bone attribution. Images from 24 patients were compared using mean absolute error (MAE). The generated dosimetry parameters were compared on the basis of dose–volume histogram values and 3%/3 mm gamma pass rates. Results Among the sCT, the best MAE were found for the DA sCT without bone. The mean of the median dose difference for the planning tumor volume (PTV) was less than 2% for all sCT. Mean gamma pass rate for the MR‐based sCTs were comparable. Conclusions The DA of the bone can be of interest if correctly segmented, but in the absence of certainty, an overestimation will lead to significant errors while an underestimation gives results close to the reference. The relevance of very precise sCT can be questioned if it requires too many resources and overlooks patients with artifacts.

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