基于优化循环生成对抗网络的医学图像合成方法

Medical Image Synthesis Based on Optimized Cycle-Generative Adversarial Networks

放射治疗计划系统需要CT图像准确计算剂量分布,但有时临床只能获得MR图像。图像合成能有效利用现有图像合成新模态图像,从而增强图像信息。针对MR图像生成CT图像问题,综合循环一致生成对抗网络不成对数据可训练合成新模态图像的特点,以及密集连接网络的特征复用和优化信息流传播的优点,提出融合密集连接的循环生成对抗网络模型,改善输入信息的消失和梯度信息稀释,合成更可信的CT图像。在18个病人的数据集上训练和验证模型,优化后的循环生成对抗网络与原方法相比,平均绝对误差降低了3.91%,结构相似性提高了1.1%,峰值信噪比提高了4.4%;与深度卷积神经网络模型和基于图谱方法比较,相对误差分别降低了0.065%和0.55%。本文利用深度学习模型优点,能根据MR图像合成更接近真实的CT图像,更好地满足放射治疗计划系统剂量计算的需求。

The radiation treatment plan system needs to calculate the dose distribution accurately based on CT images,but sometimes clinical MR images can only be obtained.Image synthesis effectively creates new modality images from another modality,which enhances image information.This paper presents a new method of synthesizing high precision and definition of CT images from MR images.To synthesize clearly pseudo CT images,an improved cycle-consistent generative adversarial network(CycleGAN)with densely connected convolutional network(DenseNet)is proposed.Avoiding the disappearance of input information and the vanishing of gradient information,the improved network can synthesize more credible CT images.Compared with the original method,the proposed method is reduced by 5.9%on mean absolute error,increased by 1.1%on structural similarity and increased by 4.4%on peak signal to ratio,which is trained and tested on the dataset of 18 patients.And compared with the deep convolutional neural network and the atlas-based method,the improved CycleGAN is reduced by 0.065%and 0.55%on relative error,respectively.The proposed method can synthesize more vivid CT images owing to the advantages of deep learning model,which better meets the requirements of dose calculation in radiation treatment planning system.