arXiv subjects

Ruru Xu

Publications and source records attributed to Ruru Xu.

5 recordsLinked to original sources

arXiv2026

Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRI

Active sampling for accelerated MRI must distribute a tight sampling budget across spatial frequencies that carry very different kinds of information. Low frequencies hold most of the anatomical context; high frequencies carry the fine details that drive pathology assessment. Existing active samplers either treat both regions identically or restrict the action space to entire Cartesian rows, which forces a poor compromise at high acceleration. We propose HieraSample, a task-driven framework built around this hierarchy. A cosine-annealed curriculum lowers the acceleration factor from 20x to 4x across 80 acquisition steps while keeping a fully-sampled low-frequency disk at every step; a Mamba-based policy then picks individual high-frequency coordinates from features extracted by dual disease and severity classifiers. The reward is the per-sample reduction in class-weighted cross-entropy after each action, so a positive reward corresponds directly to a more confident correct prediction. On the fastMRI+ knee benchmark, HieraSample matches the fully-sampled oracle on ACL diagnosis from 4x to 10x acceleration, and improves on a recent Cartesian baseline by as much as 20.4 AUC points on ACL severity.

cs.CV↗

arXiv2025

HierAdaptMR: Cross-Center Cardiac MRI Reconstruction with Hierarchical Feature Adapters

Deep learning-based cardiac MRI reconstruction faces significant domain shift challenges when deployed across multiple clinical centers with heterogeneous scanner configurations and imaging protocols. We propose HierAdaptMR, a hierarchical feature adaptation framework that addresses multi-level domain variations through parameter-efficient adapters. Our method employs Protocol-Level Adapters for sequence-specific characteristics and Center-Level Adapters for scanner-dependent variations, built upon a variational unrolling backbone. A Universal Adapter enables generalization to entirely unseen centers through stochastic training that learns center-invariant adaptations. The framework utilizes multi-scale SSIM loss with frequency domain enhancement and contrast-adaptive weighting for robust optimization. Comprehensive evaluation on the CMRxRecon2025 dataset spanning 5+ centers, 10+ scanners, and 9 modalities demonstrates superior cross-center generalization while maintaining reconstruction quality. code: https://github.com/Ruru-Xu/HierAdaptMR

cs.CV↗

arXiv2025

Adaptive k-space Radial Sampling for Cardiac MRI with Reinforcement Learning

Accelerated Magnetic Resonance Imaging (MRI) requires careful optimization of k-space sampling patterns to balance acquisition speed and image quality. While recent advances in deep learning have shown promise in optimizing Cartesian sampling, the potential of reinforcement learning (RL) for non-Cartesian trajectory optimization remains largely unexplored. In this work, we present a novel RL framework for optimizing radial sampling trajectories in cardiac MRI. Our approach features a dual-branch architecture that jointly processes k-space and image-domain information, incorporating a cross-attention fusion mechanism to facilitate effective information exchange between domains. The framework employs an anatomically-aware reward design and a golden-ratio sampling strategy to ensure uniform k-space coverage while preserving cardiac structural details. Experimental results demonstrate that our method effectively learns optimal radial sampling strategies across multiple acceleration factors, achieving improved reconstruction quality compared to conventional approaches. Code available: https://github.com/Ruru-Xu/RL-kspace-Radial-Sampling

q-bio.TO↗

arXiv2025

Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge

Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the {clinical reference standard} for diagnosing cardiovascular disease. However, its adoption is hindered by long scan times, complex contrasts, and inconsistent quality. While deep learning methods perform well on specific CMR imaging {sequences}, they often fail to generalize across modalities and sampling schemes. The lack of benchmarks for high-quality, fast CMR image reconstruction further limits technology comparison and adoption. The CMRxRecon2024 challenge, attracting over 200 teams from 18 countries, addressed these issues with two tasks: generalization to unseen {modalities} and robustness to diverse undersampling patterns. We introduced the largest public multi-{modality} CMR raw dataset, an open benchmarking platform, and shared code. Analysis of the best-performing solutions revealed that prompt-based adaptation and enhanced physics-driven consistency enabled strong cross-scenario performance. These findings establish principles for generalizable reconstruction models and advance clinically translatable AI in cardiovascular imaging.

eess.IV↗

arXiv2024

HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss

Accelerating image acquisition for cardiac magnetic resonance imaging (CMRI) is a critical task. CMRxRecon2024 challenge aims to set the state of the art for multi-contrast CMR reconstruction. This paper presents HyperCMR, a novel framework designed to accelerate the reconstruction of multi-contrast cardiac magnetic resonance (CMR) images. HyperCMR enhances the existing PromptMR model by incorporating advanced loss functions, notably the innovative Eagle Loss, which is specifically designed to recover missing high-frequency information in undersampled k-space. Extensive experiments conducted on the CMRxRecon2024 challenge dataset demonstrate that HyperCMR consistently outperforms the baseline across multiple evaluation metrics, achieving superior SSIM and PSNR scores.

eess.IV↗