Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors

Liu, Qiegen; Yang, Qing; Cheng, Huitao; Wang, Rongpin; Zhang, Minghui; Liang, Dong

doi:10.1002/mrm.27921

Cited by 56 publications

(35 citation statements)

References 39 publications

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“…For example, starting with the ESPIRiT maps could be helpful in rejecting noise and ringing artefacts. Furthermore, the proposed network holds the potential to be integrated with other advanced image reconstruction models, including the most recent deep-learning based methods [31][32][33][34][35][36] where sensitivity functions are required. Finally, it would also be worthwhile to build electromagnetics constraints into the learning model and explore the complementary power of physicsbased and data-driven priors.…”

Section: Discussionmentioning

confidence: 99%

DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning

Sutton

Lam

et al. 2021

Magnetic Resonance in Med

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To improve the estimation of coil sensitivity functions from limited auto-calibration signals (ACS) in SENSE-based reconstruction for brain imaging. Methods:We propose to use deep learning to estimate coil sensitivity functions by leveraging information from previous scans obtained using the same RF receiver system. Specifically, deep convolutional neural networks were designed to learn an end-to-end mapping from the initial sensitivity to the high-resolution counterpart. Sensitivity alignment was further proposed to reduce the geometric variation caused by different subject positions and imaging FOVs. Crossvalidation with a small set of datasets was performed to validate the learned neural network. Iterative SENSE reconstruction was adopted to evaluate the utility of the sensitivity functions from the proposed and conventional methods. Results:The proposed method produced improved sensitivity estimates and SENSE reconstructions compared to the conventional methods in terms of aliasing and noise suppression with very limited ACS data. Cross-validation with a small set of data demonstrated the feasibility of learning coil sensitivity functions for brain imaging. The network learned on the spoiled GRE data can be applied to predict sensitivity functions for spin-echo and MPRAGE datasets. Conclusion:A deep learning-based method has been proposed for improving the estimation of coil sensitivity functions. Experimental results have demonstrated the feasibility and potential of the proposed method for improving SENSE-based reconstructions especially when the ACS data are limited.

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Section: Discussionmentioning

confidence: 99%

DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning

Sutton

Lam

et al. 2021

Magnetic Resonance in Med

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“…The authors of [136] proposed to unrolled a first-order gradient method and the regularization term in object function is related to a neural network. The authors of [137] applied the method in [43] to parallel MR imaging. The authors of [138] proposed to utilized variable splitting algorithm.…”

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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“…Image reconstruction and SR techniques can improve image quality without changing the MR image acquisition hardware, therefore they have been widely used for accelerated MR imaging. Traditional technologies such as CS [22], low rank [23]- [26], and dictionary learning [27], [28] have made progress in this task. More recently, deep learning has been widely used.…”

Section: Related Work a Deep Learning For Accelerated Mr Imagingmentioning

confidence: 99%

Multi-Modal Transformer for Accelerated MR Imaging

Feng¹,

Yan²,

Chen³

et al. 2021

Preprint

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Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multimodal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans is the first attempt at using improved transformers for multimodal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. It affords both distinct structural information and subtle pixel-level information, which supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multimodal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-ofthe-art methods on fastMRI and real-world clinical datasets.

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Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors

Cited by 56 publications

References 39 publications

DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning

DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning

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

Multi-Modal Transformer for Accelerated MR Imaging

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