SOTAVerified

MRI Reconstruction

In its most basic form, MRI reconstruction consists in retrieving a complex-valued image from its under-sampled Fourier coefficients. Besides, it can be addressed as a encoder-decoder task, in which the normative model in the latent space will only capture the relevant information without noise or corruptions. Then, we decode the latent space in order to have a reconstructed MRI.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 441 papers

TitleStatusHype
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction—0
Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction—0
MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction—0
ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction—0
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction—0
Rethinking Dual-Domain Undersampled MRI reconstruction: domain-specific design from the perspective of the receptive field—0
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP—0
DD-CISENet: Dual-Domain Cross-Iteration Squeeze and Excitation Network for Accelerated MRI Reconstruction—0
A Self-supervised Diffusion Bridge for MRI Reconstruction—0
Data augmentation for deep learning based accelerated MRI reconstruction—0
Deep Cardiac MRI Reconstruction with ADMM—0
A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation—0
A Densely Interconnected Network for Deep Learning Accelerated MRI—0
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers—0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies—0
A Scale Invariant Approach for Sparse Signal Recovery—0
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks—0
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction—0
Deep Learning-based Intraoperative MRI Reconstruction—0
A Comprehensive Survey on Magnetic Resonance Image Reconstruction—0
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review—0
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction—0
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction—0
A review and experimental evaluation of deep learning methods for MRI reconstruction—0
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All datasetsfastMRI Knee 8xfastMRI Knee Val 8xfastMRI Brain 4xfastMRI Brain 8xfastMRI Knee 4xIXI

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HUMUS-Net (train+val data)SSIM0.89—Unverified
2HUMUS-Net (train only)SSIM0.89—Unverified
3End-to-end variational networkSSIM0.89—Unverified
4XPDNetSSIM0.89—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptMRSSIM0.9—Unverified
2HUMUS-Net-LSSIM0.9—Unverified
3HUMUS-NetSSIM0.89—Unverified
4E2E-VarNet (train+val)SSIM0.89—Unverified
#ModelMetricClaimedVerifiedStatus
1End-to-end variational networkSSIM0.96—Unverified
2XPDNetSSIM0.96—Unverified
#ModelMetricClaimedVerifiedStatus
1End-to-end variational networkSSIM0.94—Unverified
2XPDNetSSIM0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1End-to-end variational networkSSIM0.93—Unverified
2XPDNetSSIM0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1Residual U-NETDSSIM0—Unverified