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[bibtex]@InProceedings{Vasudeva_2022_WACV, author = {Vasudeva, Bhavya and Deora, Puneesh and Bhattacharya, Saumik and Pradhan, Pyari Mohan}, title = {Compressed Sensing MRI Reconstruction With Co-VeGAN: Complex-Valued Generative Adversarial Network}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {672-681} }
Compressed Sensing MRI Reconstruction With Co-VeGAN: Complex-Valued Generative Adversarial Network
Abstract
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 on different datasets demonstrates that it significantly outperforms the existing CS-MRI reconstruction techniques.
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