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 201–250 of 441 papers

TitleStatusHype
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction—0
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction—0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies—0
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers—0
Data augmentation for deep learning based accelerated MRI reconstruction—0
DD-CISENet: Dual-Domain Cross-Iteration Squeeze and Excitation Network for Accelerated MRI Reconstruction—0
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
Deep Cardiac MRI Reconstruction with ADMM—0
Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction—0
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study—0
Deep Learning-based Intraoperative MRI Reconstruction—0
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review—0
Deep learning for undersampled MRI reconstruction—0
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction—0
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks—0
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network—0
Deep Plug-and-Play Prior for Parallel MRI Reconstruction—0
Deep unfolding as iterative regularization for imaging inverse problems—0
Deep Unfolding Network with Spatial Alignment for multi-modal MRI reconstruction—0
Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization—0
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation—0
Deep variational network for rapid 4D flow MRI reconstruction—0
Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms—0
Training-Free Mitigation of Adversarial Attacks on Deep Learning-Based MRI Reconstruction—0
Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction—0
Diffusion Posterior Sampling is Computationally Intractable—0
Domain-conditioned and Temporal-guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction—0
DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction—0
Dual-Domain Multi-Contrast MRI Reconstruction with Synthesis-based Fusion Network—0
Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction—0
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction—0
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI—0
Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network—0
Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction—0
Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model—0
JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model—0
Joint Supervised and Self-supervised Learning for MRI Reconstruction—0
LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset—0
LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Prior—0
Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction—0
Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformer—0
Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach—0
Linear Predictability in MRI Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging—0
Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction—0
Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction—0
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction—0
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction—0
Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction—0
Reconstruction of Cardiac Cine MRI Using Motion-Guided Deformable Alignment and Multi-Resolution Fusion—0
Memory-efficient Learning for High-Dimensional 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