Yao Michael S, Hansen Michael S
Microsoft Research, University of Pennsylvania, Department of Bioengineering, University of Pennsylvania, School of Medicine.
Microsoft Research.
Proc Mach Learn Res. 2022 Nov;193:489-511.
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 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.
加速磁共振成像（MRI）从稀疏采样的信号数据中重建临床解剖结构的图像，以减少患者扫描时间。虽然最近的研究利用深度学习来完成这项任务，但这些方法通常只在没有信号损坏或资源限制的模拟环境中进行探索。在这项工作中，我们探索对神经网络MRI图像重建器进行增强，以提高其临床相关性。具体来说，我们提出了一种用于检测图像伪影来源的卷积神经网络（ConvNet）模型，其分类器得分达到79.1%。我们还证明，在具有可变加速因子的MR信号数据上训练重建器，可以在临床患者扫描期间将其平均性能提高多达2%。当模型学习重建多个解剖结构和方向的MR图像时，我们提供了一种损失函数来克服灾难性遗忘。最后，我们提出了一种在临床采集数据集和计算能力有限的情况下，使用模拟体模数据对重建器进行预训练的方法。我们的结果为加速MRI的临床应用提供了一条潜在的前进道路。
