  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.
