Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation.

ABSTRACT: 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 have developed 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 a

SUBMITTER: Du T 

PROVIDER: S-EPMC8734583 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature