Deep Plug-and-Play Prior for Parallel MRI Reconstruction

Yazdanpanah, Ali Pour; Afacan, Onur; Warfield, Simon K.

doi:10.1109/iccvw.2019.00489

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…The network is trained to replace the image prior of iterative reconstruction algorithms, and it is plugged on the iterative reconstruction method. Yazdanpanah et al [ 97 ] introduced a deep plug-and-play prior framework for parallel MRI reconstruction. An encoder-decoder UNet convolutional network was employed with skip connections as Deep Neural Network (DNN) architecture.…”

Section: Papers Improving Deep Mri Reconstruction Methodsmentioning

confidence: 99%

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review

Singh,

Monga,

de Moura

et al. 2023

Bioengineering

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Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.

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Section: Papers Improving Deep Mri Reconstruction Methodsmentioning

confidence: 99%

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review

Singh,

Monga,

de Moura

et al. 2023

Bioengineering

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“…A wide variety of empirical results (see, e.g., [15] [31] [32] [33]) have demonstrated that, when f is a powerful denoising algorithm like BM3D, the PnP algorithm (13) produces far better recoveries than the regularization-based approach (9). For parallel MRI, the advantages of PnP ADMM were demonstrated in [34].Although the value of η does not change the fixed point of the standard ADMM algorithm (9), it affects the fixed point of the PnP ADMM algorithm (13) through the ratio σ 2 /η in (12). The success of PnP methods raises important theoretical questions.…”

Section: A Prox-based Pnpmentioning

confidence: 99%

“…where h was defined in (12) and L is the algorithmic parameter that appears in (34). 6 Since (47) takes the same form as (40), we can directly compare the CE conditions of RED and prox-based PnP.…”

Section: B Consensus Equilibrium For Redmentioning

confidence: 99%

Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery

Ahmad

Bouman

Buzzard

et al. 2020

IEEE Signal Process. Mag.

221

135

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Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging modalities (e.g., CT or ultrasound), however, the data acquisition process for MRI is inherently slow, which motivates undersampling and thus drives the need for accurate, efficient reconstruction methods from undersampled datasets. In this article, we describe the use of "plug-and-play" (PnP) algorithms for MRI image recovery. We first describe the linearly approximated inverse problem encountered in MRI. Then we review several PnP methods, where the unifying commonality is to iteratively call a denoising subroutine as one step of a larger optimizationinspired algorithm. Next, we describe how the result of the PnP method can be interpreted as a solution to an equilibrium equation, allowing convergence analysis from the equilibrium perspective. Finally, we present illustrative examples of PnP methods applied to MRI image recovery.

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“…CNNs are used to replace proximal operators. A U-Net was used in [134] to substitute the proximal operator in the ADMM algorithm. In [135], CNNs and data consistency layers are connected alternately and two different process of multi-coil were considered.…”

Section: Parallel Imagingmentioning

confidence: 99%

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

Xie

2022

Electronics

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Compressed sensing (CS) and its medical applications are active areas of research. In this paper, we review recent works using deep learning method to solve CS problem for images or medical imaging reconstruction including computed tomography (CT), magnetic resonance imaging (MRI) and positron-emission tomography (PET). We propose a novel framework to unify traditional iterative algorithms and deep learning approaches. In short, we define two projection operators toward image prior and data consistency, respectively, and any reconstruction algorithm can be decomposed to the two parts. Though deep learning methods can be divided into several categories, they all satisfies the framework. We built the relationship between different reconstruction methods of deep learning, and connect them to traditional methods through the proposed framework. It also indicates that the key to solve CS problem and its medical applications is how to depict the image prior. Based on the framework, we analyze the current deep learning methods and point out some important directions of research in the future.

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Deep Plug-and-Play Prior for Parallel MRI Reconstruction

Cited by 14 publications

References 38 publications

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review

Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

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