A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning
원문 초록
Magnetic resonance imaging (MRI) is renowned for its exceptional soft tissue contrast and high spatial resolution, making it a pivotal tool in medical imaging. The integration of deep learning algorithms offers significant potential for optimizing MRI reconstruction processes. Despite the growing body of research in this area, a comprehensive survey of optimization-based deep learning models tailored for MRI reconstruction has yet to be conducted. This review addresses this gap by presenting a thorough examination of the latest optimization-based algorithms in deep learning specifically designed for MRI reconstruction. The goal of this paper is to provide researchers with a detailed understanding of these advancements, facilitating further innovation and application within the MRI community.
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MDPG: Multi-domain Diffusion Prior Guidance for MRI Reconstruction
Self-supervised feature learning for cardiac Cine MR image reconstruction
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting
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