The aim of this study was to develop a deep neural network for respiratory motion compensation in free‐breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteers and patients who underwent clinically indicated cardiac MRI examinations. A U‐net structure was used for the encoder and decoder parts of the network and the code space was regularized by an adversarial objective. The autoencoder learns the identity map for the free‐breathing motion‐corrupted images and preserves the structural content of the images, while the discriminator, which interacts with the output of the encoder, forces the encoder to remove motion artifacts. The network was first evaluated based on data that were artificially corrupted with simulated rigid motion with regard to motion‐correction accuracy and the presence of any artificially created structures. Subsequently, to demonstrate the feasibility of the proposed approach in vivo, our network was trained on respiratory motion‐corrupted images in an unpaired manner and was tested on volunteer and patient data. In the simulation study, mean structural similarity index scores for the synthesized motion‐corrupted images and motion‐corrected images were 0.76 and 0.93 (out of 1), respectively. The proposed method increased the Tenengrad focus measure of the motion‐corrupted images by 12% in the simulation study and by 7% in the in vivo study. The average overall subjective image quality scores for the motion‐corrupted images, motion‐corrected images and breath‐held images were 2.5, 3.5 and 4.1 (out of 5.0), respectively. Nonparametric‐paired comparisons showed that there was significant difference between the image quality scores of the motion‐corrupted and breath‐held images (P < .05); however, after correction there was no significant difference between the image quality scores of the motion‐corrected and breath‐held images. This feasibility study demonstrates the potential of an adversarial autoencoder network for correcting respiratory motion‐related image artifacts without requiring paired data.

中文翻译：

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使用对抗性自动编码器和无监督学习在心脏电影 MRI 重建中进行回顾性呼吸运动校正

本研究的目的是开发一种深度神经网络，用于在自由呼吸电影 MRI 中进行呼吸运动补偿并评估其性能。使用来自健康志愿者和接受临床指示的心脏 MRI 检查的患者的未配对训练数据来训练对抗性自动编码器网络。AU-net 结构用于网络的编码器和解码器部分，代码空间通过对抗目标进行正则化。自动编码器学习自由呼吸运动损坏图像的身份映射并保留图像的结构内容，而与编码器输出交互的鉴别器强制编码器去除运动伪影。该网络首先根据在运动校正精度和任何人工创建的结构的存在方面被模拟刚性运动人为破坏的数据进行评估。随后，为了证明所提出方法在体内的可行性，我们的网络以不成对的方式在呼吸运动损坏的图像上进行了训练，并在志愿者和患者数据上进行了测试。在模拟研究中，合成的运动损坏图像和运动校正图像的平均结构相似性指数得分分别为 0.76 和 0.93（满分 1）。所提出的方法在模拟研究中将运动损坏图像的 Tenengrad 焦点测量增加了 12%，在体内研究中增加了 7%。运动损坏图像的平均整体主观图像质量得分，运动校正图像和屏气图像分别为 2.5、3.5 和 4.1（满分 5.0）。非参数配对比较表明，运动损坏图像和屏气图像的图像质量得分之间存在显着差异（P < .05); 然而，校正后运动校正图像和屏气图像的图像质量得分之间没有显着差异。这项可行性研究证明了对抗性自动编码器网络在不需要配对数据的情况下校正与呼吸运动相关的图像伪影的潜力。