Papers with Code 아카이브
MRI Reconstruction
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MDPG: Multi-domain Diffusion Prior Guidance for MRI Reconstruction
Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction
From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction
DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction
Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction
Implicit Neural Representation-Based MRI Reconstruction Method with Sensitivity Map Constraints
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study
Self-supervised feature learning for cardiac Cine MR image reconstruction
Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction
SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction
Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for MRI Reconstruction without High-Quality Training Reference
Smooth optimization algorithms for global and locally low-rank regularizers
MoRe-3DGSMR: Motion-resolved reconstruction framework for free-breathing pulmonary MRI based on 3D Gaussian representation
A New k-Space Model for Non-Cartesian Fourier Imaging
Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction
Efficient Noise Calculation in Deep Learning-based MRI Reconstructions
Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction
Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction
SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation
How Should We Evaluate Uncertainty in Accelerated MRI Reconstruction?
A Comprehensive Survey on Magnetic Resonance Image Reconstruction
Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations
Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting
Guiding Quantitative MRI Reconstruction with Phase-wise Uncertainty
Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction
Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation
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