Virtual Coil Augmentation Technology for MRI via Deep Learning

by   Cailian Yang, et al.

Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, scan time, and throughput, it is often clinically challenging to obtain high-quality MR images. In this article, we propose a method of using artificial intelligence to expand the channel to achieve the effect of increasing the virtual coil. The main feature of our work is utilizing dummy variable technology to expand the channel in both the image and k-space domains. The high-dimensional information formed by channel expansion is used as the prior information of parallel imaging to improve the reconstruction effect of parallel imaging. Two features are introduced, namely variable enhancement and sum of squares (SOS) objective function. Variable argumentation provides the network with more high-dimensional prior information, which is helpful for the network to extract the deep feature in-formation of the image. The SOS objective function is employed to solve the problem that k-space data is difficult to train while speeding up the convergence speed. Ablation studies and experimental results demonstrate that our method achieves significantly higher image reconstruction performance than current state-of-the-art techniques.


page 1

page 3

page 4

page 5

page 7

page 8

page 9


Adaptive convolutional neural networks for k-space data interpolation in fast magnetic resonance imaging

Deep learning in k-space has demonstrated great potential for image reco...

Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information

In clinical medicine, magnetic resonance imaging (MRI) is one of the mos...

Homotopic Gradients of Generative Density Priors for MR Image Reconstruction

Deep learning, particularly the generative model, has demonstrated treme...

WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction

Parallel Imaging (PI) is one of the most im-portant and successful devel...

Engineering AI Tools for Systematic and Scalable Quality Assessment in Magnetic Resonance Imaging

A desire to achieve large medical imaging datasets keeps increasing as m...

Prior image-based medical image reconstruction using a style-based generative adversarial network

Computed medical imaging systems require a computational reconstruction ...

Variable Augmented Network for Invertible MR Coil Compression

A large number of coils are able to provide enhanced signal-to-noise rat...