论文标题
无监督的MR运动伪像深度学习使用离群式拒绝引导汇总
Unsupervised MR Motion Artifact Deep Learning using Outlier-Rejecting Bootstrap Aggregation
论文作者
论文摘要
最近，已经对MR运动伪影校正的深度学习方法进行了广泛的研究。尽管与经典方法相比，这些方法显示出高性能和计算复杂性的降低，但其中大多数需要使用配对的无伪影和人工腐败的图像进行监督培训，这可能会禁止其在许多重要的临床应用中使用。例如，由于GD-EOB-DTPA增强MR中的急性瞬态呼吸困难引起的瞬时严重运动（TSM）很难控制成对数据生成的模型。为了解决这个问题，我们在这里提出了一种新颖的无监督的深度学习方案，该方案是通过拒绝的引导子采样和聚合。这是受到观察到的观察，即运动通常会在相位编码方向引起稀疏的k空间异常值，因此沿相编码方向的K空间子采样可以消除一些异常值，而聚合步骤可以进一步改善重建网络的结果。我们的方法不需要任何配对的数据，因为训练步骤仅需要无伪影的图像。此外，为了解决从潜在偏差到无伪影图像的平滑，使用最佳运输驱动的自行车以无监督的方式对网络进行训练。我们验证我们的方法可用于从模拟运动中进行伪影校正以及成功的TSM的真实运动，从而超过了现有的最新深度学习方法。
Recently, deep learning approaches for MR motion artifact correction have been extensively studied. Although these approaches have shown high performance and reduced computational complexity compared to classical methods, most of them require supervised training using paired artifact-free and artifact-corrupted images, which may prohibit its use in many important clinical applications. For example, transient severe motion (TSM) due to acute transient dyspnea in Gd-EOB-DTPA-enhanced MR is difficult to control and model for paired data generation. To address this issue, here we propose a novel unsupervised deep learning scheme through outlier-rejecting bootstrap subsampling and aggregation. This is inspired by the observation that motions usually cause sparse k-space outliers in the phase encoding direction, so k-space subsampling along the phase encoding direction can remove some outliers and the aggregation step can further improve the results from the reconstruction network. Our method does not require any paired data because the training step only requires artifact-free images. Furthermore, to address the smoothing from potential bias to the artifact-free images, the network is trained in an unsupervised manner using optimal transport driven cycleGAN. We verify that our method can be applied for artifact correction from simulated motion as well as real motion from TSM successfully, outperforming existing state-of-the-art deep learning methods.
