使用多序列磁共振成像（MRI）合成计算机断层扫描（CT）以用于MRI单独放射治疗的方法进行研究。我们提出了一种自适应多序列融合网络（AMSF-Net），从多个MRI序列中利用像素和上下文相关性来合成CT，分别使用元素和补丁融合。元素和补丁融合特征空间被组合，并选择最具代表性的特征进行建模。最后，应用密集连接卷积解码器来利用所选特征产生合成的CT图像。这项研究包括90名患者的T1加权MRI、T2加权MRI和CT数据。与其他三种现有的多序列学习模型相比，AMSF-Net将平均绝对误差（MAE）从52.88-57.23降低到49.15 HU，将峰值信噪比（PSNR）从24.82-25.32提高到25.63 dB，将结构相似性指数测量（SSIM）从0.857-0.869提高到0.878，将骨骼Dice系数从0.886-0.896提高到0.903。改进在双尾配对t检验中具有统计学意义。此外，相比基准模型，AMSF-Net降低了五个危及器官、四种正常组织和肿瘤与真实CT的强度差异。对于相应器官的平均强度值，腮腺和脊髓的MAE的减少超过了8％和16％。进一步的定性评估证实，AMSF-Net展示了合成骨骼和小器官（如眼晶体）的优越结构图像质量。所提出的方法可以提高合成CT的强度和结构图像质量，并具有临床应用的潜力。Copyright © 2023 Elsevier Ltd. All rights reserved.

To investigate a method using multi-sequence magnetic resonance imaging (MRI) to synthesize computed tomography (CT) for MRI-only radiation therapy.We proposed an adaptive multi-sequence fusion network (AMSF-Net) to exploit both voxel- and context-wise cross-sequence correlations from multiple MRI sequences to synthesize CT using element- and patch-wise fusions, respectively. The element- and patch-wise fusion feature spaces were combined, and the most representative features were selected for modeling. Finally, a densely connected convolutional decoder was applied to utilize the selected features to produce synthetic CT images.This study includes a total number of 90 patients' T1-weighted MRI, T2-weighted MRI and CT data. The AMSF-Net reduced the average mean absolute error (MAE) from 52.88-57.23 to 49.15 HU, increased the peak signal-to-noise ratio (PSNR) from 24.82-25.32 to 25.63 dB, increased the structural similarity index measure (SSIM) from 0.857-0.869 to 0.878, and increased the dice coefficient of bone from 0.886-0.896 to 0.903 compared to the other three existing multi-sequence learning models. The improvements were statistically significant according to two-tailed paired t-test. In addition, AMSF-Net reduced the intensity difference with real CT in five organs at risk, four types of normal tissue and tumor compared with the baseline models. The MAE decreases in parotid and spinal cord were over 8% and 16% with reference to the mean intensity value of the corresponding organ, respectively. Further, the qualitative evaluations confirmed that AMSF-Net exhibited superior structural image quality of synthesized bone and small organs such as the eye lens.The proposed method can improve the intensity and structural image quality of synthetic CT and has potential for use in clinical applications.Copyright © 2023 Elsevier Ltd. All rights reserved.