MRI Reconstruction
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Benchmarks
fastMRI Knee 8x
fastMRI Knee Val 8x
fastMRI Brain 4x
fastMRI Brain 8x
fastMRI Knee 4x
IXI
Most implemented
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction
Homotopic Gradients of Generative Density Priors for MR Image Reconstruction
Accelerated MRI with Un-trained Neural Networks
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models
Papers
FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction
We present FlowMoDL, an unrolled neural network for highly accelerated 4D flow MRI reconstruction that directly optimizes for both anatomical magnitude and phase-derived velocity accuracy. Building on the MoDL framework,…
MRI ReconstructionConsistency Models for Fast MRI Reconstruction Using Regularization by Denoising
Diffusion models (DMs) have emerged as powerful generative priors for MRI reconstruction with promising results. Yet DM-based methods require extensive iterative refinement, limiting their practical deployment. Consisten…
MRI ReconstructionPrimitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction
Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific reconstructions using Gaussian and Gabor primitives have sho…
Representation LearningMRI ReconstructionHarnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors
MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase retrieval have shown that magnitude-only measurements may provide complementar…
MRI ReconstructionUMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for Accelerated MRI
MRI reconstruction from undersampled k-space measurements is an ill-posed inverse problem. Physics-driven deep learning (PD-DL) methods have shown strong performance for this task by combining the MRI forward model with …
MRI ReconstructionSHFormer: Dynamic Spectral Filtering Convolutional Neural Network and High-pass Kernel Generation Transformer for Adaptive MRI Reconstruction
Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between distant pixel neighborhoods to compute feature representations. Accelerated MRI reconstruction be…
Self-Supervised LearningMRI Reconstruction