Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks
This work addresses faster and more robust MRI reconstruction for clinical applications, representing an incremental improvement over existing methods.
The paper tackles the problem of accelerating MRI acquisition through compressive sensing reconstruction, proposing a GAN-based framework that preserves high-frequency content and fine details, achieving state-of-the-art reconstruction quality with millisecond-level times suitable for real-time clinical use.
Compressive sensing magnetic resonance imaging (CS-MRI) accelerates the acquisition of MR images by breaking the Nyquist sampling limit. In this work, a novel generative adversarial network (GAN) based framework for CS-MRI reconstruction is proposed. Leveraging a combination of patch-based discriminator and structural similarity index based loss, our model focuses on preserving high frequency content as well as fine textural details in the reconstructed image. Dense and residual connections have been incorporated in a U-net based generator architecture to allow easier transfer of information as well as variable network length. We show that our algorithm outperforms state-of-the-art methods in terms of quality of reconstruction and robustness to noise. Also, the reconstruction time, which is of the order of milliseconds, makes it highly suitable for real-time clinical use.