论文标题
使用V-NET和K-NET进行快速MRI的双域重建网络
Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI
论文作者
论文摘要
目的：引入使用V-NET和K-NET的双域重建网络，以准确地从无效的K-Space数据中进行准确的MR图像重构。方法：大多数最先进的重建方法在图像域和/或K空间域中应用U-NET或级联的U-NET。然而，这些方法存在以下问题：（1）直接在k空间域中应用U-NET对于在K空间域中提取特征并不是最佳的； （2）经典图像域面向U-NET是重量重量，因此由于产生良好的重建精度而被级联多次级联。 （3）经典的图像域面向U-NET不能完全使用编码网络的信息来提取解码器网络中的功能； （4）现有方法在图像域及其双k空间域中同时提取和融合特征无效。为了解决这些问题，我们在本文（1）中提出了一个图像域编码器编码器子网，称为V-net，它更轻巧，可以使级联且有效地充分利用编码器中的特征来解码，（2）k空间域子网络称为K-sub sub-NetWork，称为k-net，它更适合于提取k-space intruction in k space recion in k space rounain in ar a ain ar ain ain ain ai ain ain ai ain and（3），并且（3）demain and（3）（3）（3）（3）（3） V-NET和K-NET是平行的，有效地组合和级联。结果：对挑战性FastMRI数据集的广泛实验结果表明，所提出的KV-NET可以重建高质量的图像，并以更少的参数胜过当前最新方法。结论：为了从不完整的K空间数据中有效地重建图像，我们提出了一个并行的双域KV-NET，以结合K-NET和V-NET。 KV-NET比最先进的方法更轻巧，但可以实现更好的重建性能。
Purpose: To introduce a dual-domain reconstruction network with V-Net and K-Net for accurate MR image reconstruction from undersampled k-space data. Methods: Most state-of-the-art reconstruction methods apply U-Net or cascaded U-Nets in image domain and/or k-space domain. Nevertheless, these methods have following problems: (1) Directly applying U-Net in k-space domain is not optimal for extracting features in k-space domain; (2) Classical image-domain oriented U-Net is heavy-weight and hence is inefficient to be cascaded many times for yielding good reconstruction accuracy; (3) Classical image-domain oriented U-Net does not fully make use information of encoder network for extracting features in decoder network; and (4) Existing methods are ineffective in simultaneously extracting and fusing features in image domain and its dual k-space domain. To tackle these problems, we propose in this paper (1) an image-domain encoder-decoder sub-network called V-Net which is more light-weight for cascading and effective in fully utilizing features in the encoder for decoding, (2) a k-space domain sub-network called K-Net which is more suitable for extracting hierarchical features in k-space domain, and (3) a dual-domain reconstruction network where V-Nets and K-Nets are parallelly and effectively combined and cascaded. Results: Extensive experimental results on the challenging fastMRI dataset demonstrate that the proposed KV-Net can reconstruct high-quality images and outperform current state-of-the-art approaches with fewer parameters. Conclusions: To reconstruct images effectively and efficiently from incomplete k-space data, we have presented a parallel dual-domain KV-Net to combine K-Nets and V-Nets. The KV-Net is more lightweight than state-of-the-art methods but achieves better reconstruction performance.
