Generation of synthetic computed tomography (sCT) for magnetic resonance imaging (MRI)-only radiotherapy is emerging as a promising direction because it can eliminate the registration error and simplify clinical workflow. The goal of this study was to generate accurate sCT from standard T1-weighted MRI for brain patients. CT and MRI data of twelve patients with brain tumors were retrospectively collected. Linear mixed-effects (LME) regression models were fitted between CT and T1-weighted MRI intensities for different segments in the brain. The whole brain sCTs were generated by combining predicted segments together. Mean absolute error (MAE) between real CTs and sCTs across all patients was 71.1 ± 5.5 Hounsfield Unit (HU). Average differences in the HU values were 1.7 ± 7.1 HU (GM), 0.9 ± 5.1 HU (WM), -24.7 ± 8.0 HU (CSF), 76.4 ± 17.8 HU (bone), 20.9 ± 20.4 HU (fat), -69.4 ± 28.3 HU (air). A simple regression technique has been devised that is capable of producing accurate HU maps from standard T1-weighted MRI, and exceptionally low MAE values indicate accurate prediction of sCTs. Improvement is needed in segmenting MRI using a more automatic approach.

中文翻译：

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使用线性混合效应回归模型从 T1 加权 MRI 生成合成 CT 的可行性

生成用于仅磁共振成像 (MRI) 放射治疗的合成计算机断层扫描 (sCT) 正在成为一个有前途的方向，因为它可以消除配准错误并简化临床工作流程。本研究的目标是从标准 T1 加权 MRI 为脑部患者生成准确的 sCT。回顾性收集了 12 名脑肿瘤患者的 CT 和 MRI 数据。线性混合效应 (LME) 回归模型适用于大脑不同节段的 CT 和 T1 加权 MRI 强度。全脑 sCT 是通过将预测的片段组合在一起生成的。所有患者的真实 CT 和 sCT 之间的平均绝对误差 (MAE) 为 71.1 ± 5.5 Hounsfield Unit (HU)。HU 值的平均差异为 1.7 ± 7.1 HU (GM)、0.9 ± 5.1 HU (WM)、-24.7 ± 8.0 HU (CSF)、76.4 ± 17.8 HU (骨)、20.9 ± 20.4 HU（脂肪），-69.4 ± 28.3 HU（空气）。已经设计了一种简单的回归技术，能够从标准的 T1 加权 MRI 生成准确的 HU 图，异常低的 MAE 值表明对 sCT 的准确预测。使用更自动的方法分割 MRI 需要改进。