A Complex-Valued Dual-Domain Dilated Convolution Neural Network for Brain MRI Reconstruction

Abstract

Magnetic resonance imaging (MRI) is a powerful imaging method that provides rich anatomical information in clinical applications, leading to more accurate diagnosis and pathological analysis. However, MRI acquisition is limited by the hardware performance and scan time, making it challenging to obtain complete high-quality images. In recent years, MRI reconstruction algorithms using deep learning (DL) have demonstrated good capabilities in improving image quality and accelerating image acquisition. Thus, a new complex-valued dual-domain dilated convolution neural network (C3DNet) providing fast and accurate MRI reconstruction is proposed in this paper. Unlike existing DL-based methods, the developed C3DNet uses complex-valued convolution to extract complex-valued features from the k-space and image-domain data separately and perform dual-domain feature fusion. Additionally, we use dilated convolution to expand the features’ receptive field and thus capture contextual information. To fully use the prior knowledge, we utilize two data consistency (DC) methods and apply them several times to both the k-space and image-domain feature maps. Experimental results demonstrate that our proposed method outperforms several state-of-the-art algorithms regarding imaging results and computation time.