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   Articles with "mri reconstruction" as a keyword



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Compressed sensing MRI reconstruction from 3D multichannel data using GPUs

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recommendations! Published in 2017 at "Magnetic Resonance in Medicine"

DOI: 10.1002/mrm.26636

Abstract: To accelerate iterative reconstructions of compressed sensing (CS) MRI from 3D multichannel data using graphics processing units (GPUs). read more here.

Keywords: data using; compressed sensing; mri reconstruction; multichannel data ... See more keywords
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Analysis of deep complex‐valued convolutional neural networks for MRI reconstruction and phase‐focused applications

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recommendations! Published in 2021 at "Magnetic Resonance in Medicine"

DOI: 10.1002/mrm.28733

Abstract: Deep learning has had success with MRI reconstruction, but previously published works use real‐valued networks. The few works which have tried complex‐valued networks have not fully assessed their impact on phase. Therefore, the purpose of… read more here.

Keywords: phase; convolutional neural; mri reconstruction; valued convolutional ... See more keywords

Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models

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recommendations! Published in 2022 at "Magnetic Resonance in Medicine"

DOI: 10.1002/mrm.29624

Abstract: We introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction. read more here.

Keywords: bayesian mri; reconstruction joint; reconstruction; joint uncertainty ... See more keywords

CS-MRI reconstruction via group-based eigenvalue decomposition and estimation

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recommendations! Published in 2018 at "Neurocomputing"

DOI: 10.1016/j.neucom.2017.12.038

Abstract: Abstract This paper proposes a novel method for compressed sensing MRI (CS-MRI) reconstruction that combines both the sparse representation and statistical estimation. In this work, the low-rank property is observed and utilized to sparsely represent… read more here.

Keywords: reconstruction; estimation; mri reconstruction; group based ... See more keywords

An Effective Co-Support Guided Analysis Model for Multi-Contrast MRI Reconstruction

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recommendations! Published in 2023 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2023.3244669

Abstract: Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical diagnosis. However, it is time-consuming to obtain MR data of multi-contrasts and the long scanning time may bring unexpected physiological motion artifacts. To obtain MR… read more here.

Keywords: contrast; multi contrast; support; model ... See more keywords

FEFA: Frequency Enhanced Multi-Modal MRI Reconstruction With Deep Feature Alignment

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recommendations! Published in 2024 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2024.3432139

Abstract: Integrating complementary information from multiple magnetic resonance imaging (MRI) modalities is often necessary to make accurate and reliable diagnostic decisions. However, the different acquisition speeds of these modalities mean that obtaining information can be time… read more here.

Keywords: reconstruction; fefa frequency; frequency enhanced; enhanced multi ... See more keywords

High-Fidelity MRI Reconstruction Using Adaptive Spatial Attention Selection and Deep Data Consistency Prior

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recommendations! Published in 2023 at "IEEE Transactions on Computational Imaging"

DOI: 10.1109/tci.2023.3258839

Abstract: Compressed sensing (CS) has shown great potential for fast magnetic resonance imaging (fastMRI). Traditional CS methods model the inverse problem by leveraging the sparsity prior to guarantee the success of signal recovery, which is not… read more here.

Keywords: high fidelity; fidelity mri; data consistency; consistency prior ... See more keywords

Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction.

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recommendations! Published in 2024 at "IEEE transactions on computational imaging"

DOI: 10.1109/tci.2024.3477329

Abstract: Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of interest is crucial for model-based methods. Plug-and-play… read more here.

Keywords: plug play; reconstruction; sensing mri; compressed sensing ... See more keywords

Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction

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recommendations! Published in 2024 at "IEEE Transactions on Computational Imaging"

DOI: 10.1109/tci.2024.3518210

Abstract: Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the limitation of long scanning time. Though previous model-based and learning-based MRI reconstruction methods have shown promising performance, most of them have… read more here.

Keywords: edge; reconstruction; edge optimization; mri reconstruction ... See more keywords

TRANS-Net: Transformer-Enhanced Residual-Error AlterNative Suppression Network for MRI Reconstruction

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recommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2022.3205684

Abstract: Since deep priors could exploit more intrinsic features than handcrafted prior knowledge, unrolled reconstruction methods significantly improve image quality for fast magnetic resonance imaging (MRI) reconstruction with the combination of iterative optimization and deep neural… read more here.

Keywords: network; trans net; error; reconstruction ... See more keywords

Improved Computational Efficiency of Locally Low Rank MRI Reconstruction Using Iterative Random Patch Adjustments

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recommendations! Published in 2017 at "IEEE Transactions on Medical Imaging"

DOI: 10.1109/tmi.2017.2659742

Abstract: This paper presents and analyzes an alternative formulation of the locally low-rank (LLR) regularization framework for magnetic resonance image (MRI) reconstruction. Generally, LLR-based MRI reconstruction techniques operate by dividing the underlying image into a collection… read more here.

Keywords: regularization; low rank; mri reconstruction; llr regularization ... See more keywords