论文标题
加速MRI临床适应的途径
A Path Towards Clinical Adaptation of Accelerated MRI
论文作者
论文摘要
加速的MRI从稀疏采样的信号数据中重建了临床解剖学的图像，以减少患者扫描时间。尽管最近的作品利用了深入的学习来完成这项任务，但这种方法通常仅在没有信号损坏或资源限制的模拟环境中进行了探索。在这项工作中，我们探讨了神经网络MRI图像重建器的增强，以增强其临床相关性。也就是说，我们提出了一个用于检测图像货物源的Convnet模型，该模型可实现分类器$ f_2 $得分为79.1％。我们还证明，具有可变加速度因子的MR信号数据上的训练重建器可以在临床患者扫描期间提高其平均性能高达2％。当模型学会重建多个解剖和方向的MR图像时，我们提供损失功能来克服灾难性的遗忘。最后，我们提出了一种使用模拟幻影数据来预先培训重建器的方法，在有限的临床获取数据集和计算功能的情况下。我们的结果为加速MRI的临床适应提供了潜在的途径。
Accelerated MRI reconstructs images of clinical anatomies from sparsely sampled signal data to reduce patient scan times. While recent works have leveraged deep learning to accomplish this task, such approaches have often only been explored in simulated environments where there is no signal corruption or resource limitations. In this work, we explore augmentations to neural network MRI image reconstructors to enhance their clinical relevancy. Namely, we propose a ConvNet model for detecting sources of image artifacts that achieves a classifier $F_2$ score of 79.1%. We also demonstrate that training reconstructors on MR signal data with variable acceleration factors can improve their average performance during a clinical patient scan by up to 2%. We offer a loss function to overcome catastrophic forgetting when models learn to reconstruct MR images of multiple anatomies and orientations. Finally, we propose a method for using simulated phantom data to pre-train reconstructors in situations with limited clinically acquired datasets and compute capabilities. Our results provide a potential path forward for clinical adaptation of accelerated MRI.
