论文标题
自动关注+：磁共振成像中的降噪运动校正
Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging
论文作者
论文摘要
运动伪像的图像腐败是磁共振成像（MRI）中的根深蒂固的问题。在这项工作中，我们提出了一个基于神经网络的正则化项，以增强自动对焦，这是一种基于经典的优化方法来删除运动伪像。该方法将两全其美。基于优化的常规迭代，对不切实际的修复体进行了盲目降级和基于深度学习的先验惩罚，并加快了融合的速度。我们使用合成和真实噪声数据验证了三种运动轨迹的方法。该方法证明对噪声和解剖结构变化有弹性，表现优于最先进的降级方法。
Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Autofocusing, a classic optimization-based method to remove motion artifacts. The method takes the best of both worlds: the optimization-based routine iteratively executes the blind demotion and deep learning-based prior penalizes for unrealistic restorations and speeds up the convergence. We validate the method on three models of motion trajectories, using synthetic and real noisy data. The method proves resilient to noise and anatomic structure variation, outperforming the state-of-the-art demotion methods.
