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Three-dimensional multi-echo MRI provides rich anatomical and quantitative information, but repeated volumetric encoding prolongs acquisition and motivates k-space undersampling. Reconstructing unders...
MIGA is a scan‑specific framework for accelerated 3D multi‑echo MRI that uses shared anisotropic Gaussian geometry, a coordinate‑conditioned multi‑output amplitude network, and explicit echo‑specific phase variables. The method jointly optimizes all components from undersampled multi‑coil k‑space data without requiring fully sampled training data. Experiments demonstrate that MIGA outperforms existing methods across various imaging tasks and acceleration factors, especially under stronger undersampling, and offers a favorable quality‑cost balance among full‑volume multi‑echo reconstruction techniques.
The paper introduces a subject‑specific spatial‑angular Gaussian field for self‑supervised joint k‑q diffusion MRI reconstruction. It uses shared 3D Gaussian primitives that provide local spatial support while each primitive carries a continuous q‑conditioned tensor‑residual response, allowing the signal at each location to be synthesized from overlapping primitive responses. Experiments on three HCP diffusion shells with various acceleration settings show consistent improvements in missing‑direction DWI reconstruction, tensor‑derived metrics, and principal diffusion orientation estimation.
arXiv:2606. 17989v1 Announce Type: cross Abstract: Multi-contrast magnetic resonance imaging (MRI) provides complementary information for clinical diagnosis.
Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution input to a high-resolution target. This overlooks a key property of MRI acquisition physics: spatial resolution and signal-to-noise ratio (SNR) are inherently coupled, making any given low-resolution scan merely one of many possible realizations under varying acquisition trade-offs.
The paper presents a primitive‑based framework for reconstructing dynamic contrast‑enhanced MRI at high undersampling rates. It separates anatomy, contrast dynamics, and motion into distinct temporal basis functions, allowing a geometric interpretation of the data. The method matches conventional reconstruction quality and accurately extracts aorta and kidney enhancement curves, with a modular design that can accommodate additional dynamic factors and higher acceleration.