Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared
to their real counter-parts in speech enhancement, image and signal processing …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

Inspired by biological neurons, the activation functions play an essential part in the learning
process of any artificial neural network (ANN) commonly used in many real-world problems …

Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of
cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular …

Purpose Demonstrate a novel fast method for reconstruction of multi‐dimensional MR
fingerprinting (MRF) data using deep learning methods. Methods A neural network (NN) is …

Physics-driven deep learning methods have emerged as a powerful tool for computational
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …

Artificial neural networks (ANNs) based machine learning models and especially deep
learning models have been widely applied in computer vision, signal processing, wireless …

This paper proposes a multi-channel image reconstruction method, named
DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional …

Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying
signals to recover high-resolution images from highly undersampled measurements. When …

Purpose Deep learning has had success with MRI reconstruction, but previously published
works use real‐valued networks. The few works which have tried complex‐valued networks …