Accelerating CS-MRI Reconstruction With Fine-Tuning Wasserstein Generative Adversarial Network

Jiang, Mingfeng; Yuan, Zihan; Yang, Xu; Zhang, Jucheng; Gong, Yue; Xia, Ling; Li, Tie‐Qiang

doi:10.1109/access.2019.2948220

Cited by 25 publications

(21 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, several novel GAN-based approaches have been proposed for MRI reconstruction. For example, the DA-FWGAN [ 24 ] architecture used a fine-tuning method for training the neural network and the Wasserstein distance as the discrepancy measure between the reference and reconstructed images. SARA-GAN [ 26 ] integrated the self-attention mechanism with relative average discriminator to reconstruct images with more realistic details and better integrity.…”

Section: Discussionmentioning

confidence: 99%

“…This framework was composed of two consecutive networks, one was used to reconstruct the under-sampled k -space data and the other was used to refine the result. Jiang et al [ 24 ] proposed a de-aliasing fine-tuning Wasserstein generative adversarial network (DA-FWGAN) to perform CS-MRI reconstruction. This approach combines fine-tuning and Wasserstein distance for training.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

2021

View full text Add to dashboard Cite

In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an “end-to-end” reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in ×10−5) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other conventional and state-of-the-art reconstruction methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

2021

View full text Add to dashboard Cite

show abstract

“…The authors of [129] utilized a U-Net to reconstruct parallel imaging. WGAN [130] was exploited in [131] and three sequentially connected U-Nets were used as the generator.…”

Section: Parallel Imagingmentioning

confidence: 99%

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

Xie

2022

Electronics

View full text Add to dashboard Cite

Compressed sensing (CS) and its medical applications are active areas of research. In this paper, we review recent works using deep learning method to solve CS problem for images or medical imaging reconstruction including computed tomography (CT), magnetic resonance imaging (MRI) and positron-emission tomography (PET). We propose a novel framework to unify traditional iterative algorithms and deep learning approaches. In short, we define two projection operators toward image prior and data consistency, respectively, and any reconstruction algorithm can be decomposed to the two parts. Though deep learning methods can be divided into several categories, they all satisfies the framework. We built the relationship between different reconstruction methods of deep learning, and connect them to traditional methods through the proposed framework. It also indicates that the key to solve CS problem and its medical applications is how to depict the image prior. Based on the framework, we analyze the current deep learning methods and point out some important directions of research in the future.

show abstract

“…At present, GAN and its variants have achieved excellent performance in image-to-image translation (Zhu et al, 2017), image super-resolution (Ledig et al, 2017), and others. In recent years, since its good data representation capabilities, GAN have also been used for MRI fast imaging (Arjovsky et al, 2017;Yang et al, 2017;Jiang et al, 2019;Kwon et al, 2019) and super-resolution (Chen et al, 2018;Lyu et al, 2019;Mahapatra et al, 2019). Yang et al (2017) applied conditional GAN to MRI reconstruction and proposed the De-Aliasing Generative Adversarial Networks (DAGAN) model.…”

Section: Introductionmentioning

confidence: 99%

“…Wasserstein GAN (Arjovsky et al, 2017) is a variant of the original GAN, by replacing the Jensen-Shannon divergence in the original GAN with Wasserstein distance, it stabilizes the learning process and solves the problem of mode collapse. Jiang et al (2019) proposed a de-aliasing fine-tuning Wasserstein generative adversarial network (DA-FWGAN) for MR imaging reconstruction. The DA-FWGAN could provide reconstruction with improved peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).…”

Section: Introductionmentioning

confidence: 99%

SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Yuan

Jiang

Wang

et al. 2020

Front. Neuroinform.

Self Cite

View full text Add to dashboard Cite

Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.

show abstract

Accelerating CS-MRI Reconstruction With Fine-Tuning Wasserstein Generative Adversarial Network

Cited by 25 publications

References 41 publications

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Contact Info

Product

Resources

About