Multi-task magnetic resonance imaging reconstruction using meta-learning

Bian, Wanyu; Jang, Albert; Liu, Fang

doi:10.1016/j.mri.2024.110278

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Cited by 3 publications

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Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction

Zhang,

Alvandipour,

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et al. 2024

J Sci Comput

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Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction

Zhang,

Alvandipour,

Xia

et al. 2024

J Sci Comput

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Style Transfer: From Stitching to Neural Networks

Xu,

Wang,

Zhang

et al. 2024

2024 5th International Conference on Big Data &Amp;amp; Artificial Intelligence &Amp;amp; Software Engineering (ICBASE)

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A Review of Optimization-Based Deep Learning Models for MRI Reconstruction

Bian,

Tamilselvam

2024

AppliedMath

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Magnetic resonance imaging (MRI) is crucial for its superior soft tissue contrast and high spatial resolution. Integrating deep learning algorithms into MRI reconstruction has significantly enhanced image quality and efficiency. This paper provides a comprehensive review of optimization-based deep learning models for MRI reconstruction, focusing on recent advancements in gradient descent algorithms, proximal gradient descent algorithms, ADMM, PDHG, and diffusion models combined with gradient descent. We highlight the development and effectiveness of learnable optimization algorithms (LOAs) in improving model interpretability and performance. Our findings demonstrate substantial improvements in MRI reconstruction in handling undersampled data, which directly contribute to reducing scan times and enhancing diagnostic accuracy. The review offers valuable insights and resources for researchers and practitioners aiming to advance medical imaging using state-of-the-art deep learning techniques.

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Multi-task magnetic resonance imaging reconstruction using meta-learning

Cited by 3 publications

References 25 publications

Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction

Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction

Style Transfer: From Stitching to Neural Networks

A Review of Optimization-Based Deep Learning Models for MRI Reconstruction

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