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

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Showing 1–25 of 441 papers

TitleStatusHype
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing IntegrationCode2
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023Code2
vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-ProblemsCode2
Generative AI for Medical Imaging: extending the MONAI FrameworkCode2
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data GenerationCode1
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shiftingCode1
Generative Autoregressive Transformers for Model-Agnostic Federated MRI ReconstructionCode1
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI ReconstructionCode1
ContextMRI: Enhancing Compressed Sensing MRI through Metadata ConditioningCode1
XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-DecoderCode1
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual LearningCode1
TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature ScalingCode1
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
IM-MoCo: Self-supervised MRI Motion Correction using Motion-Guided Implicit Neural RepresentationsCode1
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRICode1
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion ModelsCode1
ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance ImagingCode1
Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI ReconstructionCode1
Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRICode1
TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet methodCode1
MRPD: Undersampled MRI reconstruction by prompting a large latent diffusion modelCode1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRICode1
Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without NoiseCode1
k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image ReconstructionCode1
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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