Papers with Code 아카이브

MRI Reconstruction

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하위 태스크: 2
평가표: 6
보존된 평가 기록: 15

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.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/moucheng2017/MRI-GMM-VAE

공식 코드

MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixels

https://github.com/mrirecon/aid

공식 코드

Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI

https://github.com/mrirecon/image-priors

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Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase Augmentation

https://github.com/mrirecon/spreco

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MRI Reconstruction Using Deep Bayesian Estimation

https://github.com/multimodallearning/miccai24_immoco

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IM-MoCo: Self-supervised MRI Motion Correction using Motion-Guided Implicit Neural Representations

https://github.com/myigitavci/dropout_ismrm

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Improving accuracy and uncertainty quantification of deep learning based quantitative MRI using Monte Carlo dropout

https://github.com/nalinimsingh/interlacer

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Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis

https://github.com/nifm-gin/marvel

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MARVEL: MR Fingerprinting with Additional micRoVascular Estimates using bidirectional LSTMs

https://github.com/nikolasmorshuis/sgr

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Segmentation-guided MRI reconstruction for meaningfully diverse reconstructions

https://github.com/nitzanavidan/pd_recon

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MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation

https://github.com/nki-ai/direct

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Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction

https://github.com/nvlabs/smrd

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SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models

https://github.com/osu-mr/discus

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An unsupervised method for MRI recovery: Deep image prior with structured sparsity

https://github.com/osu-mr/reside

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MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data

https://github.com/pandafriedlich/relax_qmri_recon

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Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction

https://github.com/paulkogni/mr-recon-uq

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Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction

https://github.com/phernst/pd-unet

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Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction

https://github.com/ppn-paper/ppn

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Fast Controllable Diffusion Models for Undersampled MRI Reconstruction

https://github.com/project-monai/generativemodels

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Generative AI for Medical Imaging: extending the MONAI Framework

https://github.com/qic999/fsmnet

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Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning

https://github.com/quanato607/xlstm-hved

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XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder

https://github.com/quentin-duchemin/mrf-crbloss

공식 코드

Cramér-Rao bound-informed training of neural networks for quantitative MRI

https://github.com/saiboxx/bernoulli-mri

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Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction

https://github.com/samahkh/npb-rec

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NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data

https://github.com/sarafridov/volumetric-propeller

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Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI

https://github.com/sbb-gh/prosub

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Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI

https://github.com/seancepstein/training_labels

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Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation

https://github.com/sizhuoliu/reside

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MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data

https://github.com/sjames40/adversarial-purification-for-mri

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Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification

https://github.com/sjames40/smug_journal

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Robust MRI Reconstruction by Smoothed Unrolling (SMUG)

https://github.com/sodicksonlab/tgvn

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A Trust-Guided Approach to MR Image Reconstruction with Side Information

https://github.com/solor-pikachu/nila

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Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI

https://github.com/solor-pikachu/roger

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Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative Priors

https://github.com/soumickmj/FTSuperResDynMRI

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Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

https://github.com/sriprabhar/MAC-ReconNet

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MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction

https://github.com/sriprabhar/hypercoil-recon

공식 코드

HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.