Introduces a local measurement-manifold compatibility measure that bounds stable reconstruction error under generative priors and motivates fixed and adaptive acquisition rules.
Pruessmann, Markus Weiger, Markus B
4 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
years
2026 4roles
background 1polarities
background 1representative citing papers
PICO estimates image-domain noise covariance in linear and nonlinear MRI reconstructions up to 7x faster than PMR by using complex random-phase probes.
A plug-and-play bilateral breast gradient insert prototype achieves 2.8 mT/m/A efficiency and local strengths up to 1850 mT/m, allowing b=10000 s/mm² diffusion MRI at TE=78 ms versus 161 ms with scanner gradients.
Mixed training with contrast-informed augmentation and domain-adversarial training improves E2E-VarNet performance on neonatal T2-weighted brain MR reconstruction at R=4 and R=8 compared to adult-only training.
citing papers explorer
-
Measurement Geometry and Design for Trustworthy Generative Inverse Problems
Introduces a local measurement-manifold compatibility measure that bounds stable reconstruction error under generative priors and motivates fixed and adaptive acquisition rules.