Primitive 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 shown promising results without the need for large training datasets, but have not addressed the additional dimension of dynamic contrast. We propose a multi-dimensional, primitive based framework for dynamic contrast-enhanced MRI reconstruction that disentangles the underlying anatomy, the dynamic contrast enhancement, and residual motion into separate temporal basis functions, t
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- PossiblePossibly related (embedding) · 49%PnP-CoSMo: A Multi-Contrast MRI Reconstruction Framework based on Content/Style Modeling [R] →
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Primitive Representation Learning for Unsupervised Dynamic C” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Veronika Spieker →
“Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction”
- LinkedLinked via arxiv author · 85%Wenqi Huang →
“Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction”
- LinkedLinked via arxiv author · 85%Cemre Ariyurek →
“Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction”
- LinkedLinked via arxiv author · 85%Liam Timms →
“Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction”
- LinkedLinked via arxiv author · 85%Daniel Rueckert →
“Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction”
- LinkedLinked via arxiv author · 85%Onur Afacan →
“Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction”