  Deep learning in k-space has demonstrated great potential for image
reconstruction from undersampled k-space data in fast magnetic resonance
imaging (MRI). However, existing deep learning-based image reconstruction
methods typically apply weight-sharing convolutional neural networks (CNNs) to
k-space data without taking into consideration the k-space data's spatial
frequency properties, leading to ineffective learning of the image
reconstruction models. Moreover, complementary information of spatially
adjacent slices is often ignored in existing deep learning methods. To overcome
such limitations, we develop a deep learning algorithm, referred to as adaptive
convolutional neural networks for k-space data interpolation (ACNN-k-Space),
which adopts a residual Encoder-Decoder network architecture to interpolate the
undersampled k-space data by integrating spatially contiguous slices as
multi-channel input, along with k-space data from multiple coils if available.
The network is enhanced by self-attention layers to adaptively focus on k-space
data at different spatial frequencies and channels. We have evaluated our
method on two public datasets and compared it with state-of-the-art existing
methods. Ablation studies and experimental results demonstrate that our method
effectively reconstructs images from undersampled k-space data and achieves
significantly better image reconstruction performance than current
state-of-the-art techniques.
