A Comparative Study: Greedy versus GAN-Based Compressed Sensing for Fast MRI Reconstruction
Keywords:
Compressed sensing, MRI reconstruction, Orthogonal Matching Pursuit, Generative Adversarial Networks, Deep learning, Undersampled MRI, Image reconstructionAbstract
Abstract The clinical applicability of Magnetic Resonance Imaging (MRI) is often constrained by prolonged scanning durations, particularly in emergency scenarios. To address this, this study conducts a comparative analysis between a traditional greedy algorithm, Orthogonal Matching Pursuit (OMP), and a deep learning alternative known as Generative Adversarial Networks for compressed sensing (GANCS). Empirical evidence suggests that GANCS outperforms the classical method by delivering higher fidelity images from highly undersampled data, while also ensuring rapid processing speeds following the training phase.
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