Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information. (arXiv:2112.05758v1 [eess.IV])

In clinical medicine, magnetic resonance imaging (MRI) is one of the most
important tools for diagnosis, triage, prognosis, and treatment planning.
However, MRI suffers from an inherent slow data acquisition process because
data is collected sequentially in k-space. In recent years, most MRI
reconstruction methods proposed in the literature focus on holistic image
reconstruction rather than enhancing the edge information. This work steps
aside this general trend by elaborating on the enhancement of edge information.
Specifically, we introduce a novel parallel imaging coupled dual discriminator
generative adversarial network (PIDD-GAN) for fast multi-channel MRI
reconstruction by incorporating multi-view information. The dual discriminator
design aims to improve the edge information in MRI reconstruction. One
discriminator is used for holistic image reconstruction, whereas the other one
is responsible for enhancing edge information. An improved U-Net with local and
global residual learning is proposed for the generator. Frequency channel
attention blocks (FCA Blocks) are embedded in the generator for incorporating
attention mechanisms. Content loss is introduced to train the generator for
better reconstruction quality. We performed comprehensive experiments on
Calgary-Campinas public brain MR dataset and compared our method with
state-of-the-art MRI reconstruction methods. Ablation studies of residual
learning were conducted on the MICCAI13 dataset to validate the proposed
modules. Results show that our PIDD-GAN provides high-quality reconstructed MR
images, with well-preserved edge information. The time of single-image
reconstruction is below 5ms, which meets the demand of faster processing.

Source: https://arxiv.org/abs/2112.05758

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