论文标题
ERNAS：进化神经架构搜索磁共振图像重建
ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions
论文作者
论文摘要
磁共振成像（MRI）是可以产生高质量图像的非侵入成像方式之一。但是，扫描程序相对较慢，这会导致患者的不适感和图像中的运动伪像。加速MRI硬件受到物理和生理局限性的限制。加速MRI的一种流行的替代方法是调解K空间数据。虽然散采样速度加快了扫描程序的速度，但它会在图像中产生工件，并且需要高级重建算法来产生无伪影的图像。最近，深度学习已成为解决此问题的有希望的MRI重建方法。但是，在MRI重建中，直接采用现有的深度学习神经网络体系结构通常在效率和重建质量方面并不是最佳的。在这项工作中，使用新型的进化神经架构搜索算法使用优化的神经网络进行了无效数据的MRI重建。大脑和膝盖MRI数据集表明，所提出的算法的表现优于手动设计的基于神经网络的MR重建模型。
Magnetic resonance imaging (MRI) is one of the noninvasive imaging modalities that can produce high-quality images. However, the scan procedure is relatively slow, which causes patient discomfort and motion artifacts in images. Accelerating MRI hardware is constrained by physical and physiological limitations. A popular alternative approach to accelerated MRI is to undersample the k-space data. While undersampling speeds up the scan procedure, it generates artifacts in the images, and advanced reconstruction algorithms are needed to produce artifact-free images. Recently deep learning has emerged as a promising MRI reconstruction method to address this problem. However, straightforward adoption of the existing deep learning neural network architectures in MRI reconstructions is not usually optimal in terms of efficiency and reconstruction quality. In this work, MRI reconstruction from undersampled data was carried out using an optimized neural network using a novel evolutionary neural architecture search algorithm. Brain and knee MRI datasets show that the proposed algorithm outperforms manually designed neural network-based MR reconstruction models.
