MosaicMRI provides a diverse raw MSK MRI dataset that enables deep learning models to exploit cross-anatomical correlations, outperforming anatomy-specific training in low-sample regimes for accelerated reconstruction.
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fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
23 Pith papers cite this work. Polarity classification is still indexing.
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Hallucinations in inverse problem reconstructions are fundamental to ill-posedness, with necessary and sufficient conditions plus computable bounds depending only on the forward model.
UNITS framework proves self-supervised splitting risk in MRI reconstruction is a weighted supervised risk, yielding identical Bayes-optimal predictors and relating training residuals to prediction bias.
A temporal diffusion model enables adaptive selection of focused ultrasound transmits, outperforming random subsampling and diverging waves on EchoNet-Dynamic and in-house echocardiogram datasets while supporting real-time operation.
SO-Mamba introduces state-ownership routing in Mamba regularizers for unrolled MRI reconstruction to separate resident carrier content from non-resident evidence across stages.
NexOP jointly optimizes NEX-aware k-space sampling probabilities and multi-measurement reconstruction to raise effective SNR in low-field MRI under a fixed total sampling budget.
Conditional score-based diffusion models synthesize phase maps from magnitude-only MR images to generate k-space datasets that train superior deep learning models for accelerated MRI reconstruction compared to smooth-phase or GAN-based alternatives.
Natural-domain foundation models provide competitive and more robust priors than task-specific models for accelerated cardiac MRI reconstruction in cross-domain settings.
A cVAE plus flow-matching model generates realistic complex-valued brain MRI that preserves phase coherence above 0.997 and yields synthetic data that trains abnormality classifiers to 0.880 AUROC, beating the 0.842 real-data baseline on fastMRI.
An information-theoretic optimization framework for task-adapted CS-MRI enables adaptive sampling at arbitrary ratios and probabilistic inference for uncertainty while supporting joint reconstruction-task or privacy-focused scenarios.
PASS combines a vision-language model with physics-based deep unrolling to create personalized, anomaly-aware fast MRI that improves image quality and downstream diagnostic tasks.
Noise injection into plug-and-play algorithms using pretrained score-based diffusion denoisers optimizes a Gaussian-smoothed objective and yields better reconstructions for severely ill-posed imaging tasks.
Diffusion inverse solvers are assessed for posterior fidelity using a new score-based Kernel Stein Discrepancy metric that requires no ground-truth posterior, revealing that reconstruction accuracy alone is insufficient.
iR2D2 extends the R2D2 DNN series paradigm with an interlaced dual-series architecture and error-controlled updates to jointly reconstruct MR images and self-calibrate sensitivity maps from undersampled radial k-space data.
Bilevel-optimized implicit neural representation with Gaussian process hyperparameter tuning enables scan-specific accelerated MRI reconstruction without training data.
SUNO learns per-scan adaptive k-space undersampling patterns via ICD optimization and NN lookup from low-frequency data, showing better reconstruction quality than standard patterns at 4x and 8x acceleration on fastMRI knee and brain data.
MK-ResRecon predicts missing slices with a multi-kernel texture-aware loss while IdentityRefineNet3D refines the combined 3D volume to enable accurate reconstruction from highly sparse 2D inputs.
Nirvana adds a task-aware memory trigger and updater to specialized generalist models, achieving strong general benchmark results, lowest perplexity in biomedicine/finance/law, and improved MRI reconstruction fidelity.
HiFi-Mamba uses stacked W-Laplacian spectral decoupling and unidirectional HiFi-Mamba blocks to improve high-frequency detail preservation and efficiency over prior Mamba, CNN, and Transformer models for MRI reconstruction.
A survey that introduces taxonomies for categorizing pre-trained diffusion model methods applied to inverse problems and analyzes their connections and challenges.
Flemme is a modular platform separating encoders (conv/transformer/SSM) from encoder-decoder architectures for medical images, with a hierarchical pyramid loss yielding reported average gains of 5.6% Dice and 5.57% PSNR.
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MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI
MosaicMRI provides a diverse raw MSK MRI dataset that enables deep learning models to exploit cross-anatomical correlations, outperforming anatomy-specific training in low-sample regimes for accelerated reconstruction.