Compressed Sensing MRI Reconstruction with Co-VeGAN: Complex-Valued Generative Adversarial Network

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Conference Article

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Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022


Compressed sensing (CS) is extensively used to reduce magnetic resonance imaging (MRI) acquisition time. State-of-the-art deep learning-based methods have proven effective in obtaining fast, high-quality reconstruction of CS-MR images. However, they treat the inherently complex-valued MRI data as real-valued entities by extracting the magnitude content or concatenating the complex-valued data as two real-valued channels for processing. In both cases, the phase content is discarded. To address the fundamental problem of real-valued deep networks, i.e. their inability to process complex-valued data, we propose a complex-valued generative adversarial network (Co-VeGAN) framework, which is the first-of-its-kind generative model exploring the use of complex-valued weights and operations. Further, since real-valued activation functions do not generalize well to the complex-valued space, we propose a novel complex-valued activation function that is sensitive to the input phase and has a learnable profile. Extensive evaluation of the proposed approach 1 on different datasets demonstrates that it significantly outperforms the existing CS-MRI reconstruction techniques.

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