Deep Learning-based MRI Reconstruction with Artificial Fourier Transform (AFT)-Net

• URL: http://arxiv.org/abs/2312.10892v2
• Date: Fri, 18 Oct 2024 19:41:06 GMT
• Title: Deep Learning-based MRI Reconstruction with Artificial Fourier Transform (AFT)-Net
• Authors: Yanting Yang, Yiren Zhang, Zongyu Li, Jeffery Siyuan Tian, Matthieu Dagommer, Jia Guo,
• Abstract summary: We introduce a unified complex-valued deep learning framework-Artificial Fourier Transform Network (AFTNet) AFTNet can be readily used to solve image inverse problems in domain transformation. We show that AFTNet achieves superior accelerated MRI reconstruction compared to existing approaches.
• Score: 14.146848823672677
• License:
• Abstract: Deep complex-valued neural networks provide a powerful way to leverage complex number operations and representations and have succeeded in several phase-based applications. However, most previously published networks have not fully explored the impact of complex-valued networks in the frequency domain. Here, we introduce a unified complex-valued deep learning framework-Artificial Fourier Transform Network (AFTNet)-which combines domain-manifold learning and complex-valued neural networks. AFTNet can be readily used to solve image inverse problems in domain transformation, especially for accelerated magnetic resonance imaging (MRI) reconstruction and other applications. While conventional methods only accept magnitude images, the proposed method takes raw k-space data in the frequency domain as input, allowing a mapping between the k-space and image domains to be determined through cross-domain learning. We show that AFTNet achieves superior accelerated MRI reconstruction compared to existing approaches. Furthermore, our approach can be applied to various tasks, such as denoised magnetic resonance spectroscopy (MRS) reconstruction and datasets with various contrasts. The AFTNet presented here is a valuable preprocessing component for different preclinical studies and provides an innovative alternative for solving inverse problems in imaging and spectroscopy. The code is available at: https://github.com/yanting-yang/AFT-Net.

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