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https://doi.org/10.1109/icip51287.2024.10647348

A Cross Domain Generative Network for Accelerated MRI

Oct 27, 2024
Vazim Ibrahim +1 more

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Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of Fourier space (k-space). Existing deep learning-based CSMRI methods are commonly concerned with optimizing datadriven network models with input undersampled data points and an efficient learning framework. Generative modelling is a learning framework employed in different applications for learning an abstract distribution of observed data and thereby generating new data points similar to the true features. In this regard, the current work proposes a Generative Adversarial Network (GAN) based Cross Domain Extrapolation Generative Adversarial Network (CdE-GAN) that incorporates an extrapolation mechanism through decoder-type architectural design to represent the fine details with a large set of pixels. The results obtained from the experiments show that the extrapolation network enables robust and accurate estimation of missing frequencies, alleviating the structural artifacts at higher acceleration/downsampling factors compared to state-of-the-art methods.

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Most current generative adversarial network (GAN) cannot simultaneously consider the quality and diversity of generated samples due to limited data and variable working condition. To solve the problem, a Transformer-based conditional GAN transfer learning network is proposed. Firstly, a transformer-based conditional GAN (TCGAN) generative network is constructed with sample label information, enhancing the quality of generated data while retaining the diversity of generated signals. Secondly, a transfer learning network based on TCGAN is established, and a "generation-transfer" collaborative training strategy based on the expectation maximization is introduced to realize parallel updating of the parameters of the generative network and the transfer network. Finally, the effectiveness of the proposed method is verified using bearing datasets from CWRU and the self-made KUST-SY. The results show that the proposed method can generate higher quality data than comparative methods such as TTS-GAN and CorGAN, which provides a new solution for improving the cross-domain fault diagnosis performance.