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MMRINet: Efficient Mamba-Based Segmentation with Dual-Path Refinement for Low-Resource MRI Analysis

arXiv ·

Researchers from MBZUAI have developed MMRINet, a Mamba-based neural network for efficient brain tumor segmentation in MRI scans. The model uses Dual-Path Feature Refinement and Progressive Feature Aggregation to achieve high accuracy with only 2.5M parameters, making it suitable for low-resource clinical environments. MMRINet achieves a Dice score of 0.752 and HD95 of 12.23 on the BraTS-Lighthouse SSA 2025 benchmark.

When AI stops playing “spot the difference” and starts understanding changes in MRIs

MBZUAI ·

MBZUAI researchers presented DEFUSE-MS at MICCAI 2025, a novel AI system for analyzing changes in MRI scans of multiple sclerosis (MS) patients. DEFUSE-MS uses a deformation field-guided spatiotemporal graph-based framework to identify new lesions by reasoning about how the brain has changed. The model constructs graphs of small regions within baseline and follow-up MRIs, linking them across time with edges enriched with learned embeddings of the deformation field. Why it matters: DEFUSE-MS reframes the task from simple "spot the difference" to understanding structural changes, potentially improving the speed and accuracy of MS diagnosis and treatment monitoring.

KAUST-Bruker CoE in Magnetic Resonance Inaugurated

KAUST ·

KAUST and Bruker Corporation have launched the KAUST–Bruker Center of Excellence (CoE) in Magnetic Resonance, formalizing a long-standing collaboration. The CoE will provide KAUST users access to cutting-edge magnetic resonance technologies and serve as a training site. Bruker showcased advanced technologies including the world's first 900 MHz wide-bore NMR spectrometer and a 500 MHz super wide-bore MRI spectrometer. Why it matters: This CoE enhances KAUST's position as a leading research institution in the region and fosters innovation in magnetic resonance research and applications.

BRIQA: Balanced Reweighting in Image Quality Assessment of Pediatric Brain MRI

arXiv ·

This paper introduces BRIQA, a new method for automated assessment of artifact severity in pediatric brain MRI, which is important for diagnostic accuracy. BRIQA uses gradient-based loss reweighting and a rotating batching scheme to handle class imbalance in artifact severity levels. Experiments show BRIQA improves average macro F1 score from 0.659 to 0.706, especially for Noise, Zipper, Positioning and Contrast artifacts.