A multi-contrast self-supervised MRI reconstruction framework with end-to-end learned k-space partitioning produces higher-fidelity images than single-contrast self-supervised baselines on two public datasets.
Duncan, and Michal Sofka
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
RMCT matches the rate of target behaviors like bias-following across input perturbations to reduce sycophancy in LLMs while preserving verbalization of bias cues.
MoE-dqINR factorizes INR-based MRI reconstruction into shared spatial experts plus state-conditioned routing to unify dynamic and quantitative reconstruction at roughly 30 seconds per scan.
citing papers explorer
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Optimized Multi-Contrast Self-Supervised MRI Reconstruction using Learned k-space Partitioning
A multi-contrast self-supervised MRI reconstruction framework with end-to-end learned k-space partitioning produces higher-fidelity images than single-contrast self-supervised baselines on two public datasets.
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Consistency Training while Mitigating Obfuscation via Rate Matching
RMCT matches the rate of target behaviors like bias-following across input perturbations to reduce sycophancy in LLMs while preserving verbalization of bias cues.
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MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction
MoE-dqINR factorizes INR-based MRI reconstruction into shared spatial experts plus state-conditioned routing to unify dynamic and quantitative reconstruction at roughly 30 seconds per scan.