目的 针对图像合成配准算法中鲁棒性差及合成图像特征信息不足导致配准精度不高的问题，提出了基于残差密集相对平均条件生成对抗网络（residual dense-relativistic average conditional generative adversarial network，RD-RaCGAN）的多模态脑部图像配准方法。方法 相对平均生成对抗网络中的相对平均鉴别器能够增强模型稳定性，条件生成对抗网络加入条件变量能提高生成数据质量，结合两种网络特点，利用残差密集块充分提取深层网络特征的能力，构建RD-RaCGAN合成模型。然后，待配准的参考CT（computed tomography）和浮动MR（magnetic resonance）图像通过已训练好的RD-RaCGAN合成模型双向合成对应的参考MR和浮动CT图像。采用区域自适应配准算法，从参考CT和浮动CT图像中选取骨骼信息的关键点，从浮动MR和参考MR图像中选取软组织信息的关键点，通过提取的关键点指导形变场的估计。从浮动CT图像到参考CT图像估计一个形变场。类似地，从浮动MR图像到参考MR图像估计一个形变场。另外，采用分层对称的思想进一步优化两个形变场，当两个形变场之间的差异达到最小时，将两个形变场融合得到最终的形变场，并将形变场作用于浮动图像完成配准。结果 实验结果表明，与其他6种图像合成方法相比，本文模型合成的目标图像在视觉效果和客观评价指标上均优于其他方法。对比Powell优化的MI（mutual information）法、ANTs-SyN（advanced normalization toolbox-symmetric normalization）、D.Demons（diffeomorphic demons）、Cue-Aware Net（cue-aware deep regression network）和I-SI（intensity and spatial information）的图像配准方法，归一化互信息分别提高了43.71%、12.87%、10.59%、0.47%、5.59%，均方根误差均值分别下降了39.80%、38.67%、15.68%、4.38%、2.61%。结论 本文提出的多模态脑部图像配准方法具有很强的鲁棒性，能够稳定、准确地完成图像配准任务。

Objective Multimodal medical image registration is a key step in medical image analysis and processing as it complements the information from different modality images and provides doctors with a variety of information about diseased tissues or organs. This method enables doctors to make accurate diagnosis and treatment plans. Image registration based on image synthesis is the main method for achieving high-precision registration. A high-quality composite image indicates good registration effect. However, current image-based registration algorithm has poor robustness in synthetic models and provides an insufficient representation of synthetic image feature information, resulting in low registration accuracy. In recent years, owing to the success of deep learning in many fields, medical image registration based on deep learning has become a focus of research. The synthetic model is trained according to the modal type of the image to be registered, and the synthetic model bidirectional synthetic image is used to guide the subsequent registration. Anatomical information is employed to guide the registration and improve the accuracy of multimodal image registration. Therefore, a multimodal brain image registration method based on residual dense relative average conditional generative adversarial network (RD-RaCGAN) is proposed in this study. Method First, the RD-RaCGAN image synthesis model is constructed by combining the advantages of the relative average discriminator in the relativistic average generative adversarial network, which can enhance the model stability, and the advantages of the conditional generative adversarial network, which can improve the quality of the generated data, and also the ability of residual dense blocks to fully extract the characteristics of the deep network. Residual dense blocks are utilized as core components for building a generator. The purpose of this generator is to capture the law of sample distribution and generate a target image with specific significance, that is, to input a floating magnetic resonance (MR) or reference computed tomography (CT) image and generate the corresponding synthetic CT or synthetic MR image. The convolution neural network is used as a relative average discriminator, which correctly distinguishes an image generated by the generator from the real image. The generator and relative average discriminator perform confrontational training. First, the generator is fixed to train the relative average discriminator.Then, the relative average discriminator is fixed to train the generator, and the loop training is subsequently continued. During training, the least square function optimization generator and relative average discriminator, which are more stable and less saturated than the cross entropy function are selected. The ability of the generator and the relative average discriminator is enhanced, and the image generated by the generator can be falsified. At this point, the synthetic model training is completed. Subsequently, the CT image and MR image to be registered are bidirectionally synthesized into the corresponding reference MR image and floating CT image through the RD-RaCGAN synthesis model that has been trained. Four images obtained by bidirectional synthesis are registered by a region-adaptive registration algorithm. Specifically, the key points of the bone information are selected from the reference CT image and the floating CT image.The key points of the soft tissue information are selected from the floating MR image and the reference MR image, and the estimation of the deformation field is guided by the extracted key points. In other words, one deformation field is estimated from the floating CT image to the reference CT image, and a deformation field is estimated from the floating MR image to the reference MR image. At the same time, the idea of hierarchical symmetry is adopted to further guide the registration. The key points in the image are gradually increased when the reference and floating images are close to each other.Moreover, anatomical information is used to optimize the two deformation fields continuously until the difference between the two deformation fields reaches a minimum. The two deformation fields are fused to form the deformation field between the reference CT image and floating MR image. Finally, the deformation field is applied to the floating image to complete registration. Given that the synthesis of a target image from two images to be registered through the synthesis model requires time, the algorithm efficiency in this study is slightly lower than that of D.Demons(diffeomorphic demons) and ANTs-SyN(advanced normalization toolbox-symmetric normalization). Result Given that the quality of the synthesized image directly affects registration accuracy, three sets of contrast experiments are designed to verify the effect of the algorithm in this study. Different algorithms are by MR synthesis CT, and different algorithms are compared by CT synthesis MR, and comparison of the effect of different registration algorithms. The experimental results show that the target image synthesized by the synthesis model in this study is superior to those obtained by the other methods in terms of visual effect and objective evaluation index. The target image synthesized by RD-RaCGAN is similar to the real image and has less noise than the target images generated by the other synthetic methods. As can be seen from the bones of the synthesized brain image and the area near the air interface, the synthetic model in this work visually shows realistic texture details. Compared with the Powell-optimized MI(mutual information) method, ANTs-SyN, D.Demons, Cue-Aware Net(cue-aware deep regression network), and I-SI(intensity and spatial information) image registration methods, the normalized mutual information increased by 43.71%,12.87%,10.59%,0.47%,and 5.59%, respectively. In addition, the mean square root error decreased by 39.80%, 38.67%,15.68%,4.38%, and 2.61%, respectively. The results obtained by the registration algorithm in this study are close to the reference image. The registration effect diagram that the difference between the registration image and the reference image obtained by the algorithm in this study is smaller than that obtained by the other three methods. Small difference between the two images means good registration effect. Conclusion This study proposes a multimodal brain image registration method based on RD-RaCGAN, which solves the problem of the poor robustness of the model synthesis algorithm based on image synthesis, leading to the inaccuracy of the synthetic image and the poor registration effect.