发表机构
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院劳特伯生物医学成像研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MIGA提出共享高斯几何与隐式幅度建模的扫描特定框架，仅用欠采样k空间数据联合优化，在加速三维多回波MRI重建中优于现有方法，兼顾质量与计算成本。
AI 中文摘要
三维多回波MRI提供了丰富的解剖和定量信息，但重复的体积编码延长了采集时间，并促使采用k空间欠采样。重建欠采样的多回波数据需要利用共享解剖结构，同时保留回波依赖的信号变化；全体积建模也带来了大量的计算和内存需求。我们提出MIGA，一种扫描特定的框架，包含共享的各向异性高斯几何、坐标条件的多输出幅度网络和显式的回波特定相位变量。高斯几何提供跨回波的共同空间支持，隐式网络建模空间结构的幅度变化，相位变量保留回波特定的复信号信息。所有组件仅使用采集的多线圈k空间联合优化，无需全采样的训练数据。实验表明，MIGA在各种成像任务和加速因子下始终优于比较方法，在更强的欠采样下改进更大。MIGA在评估的全体积多回波方法中也实现了良好的质量-成本平衡。这些结果支持将共享高斯几何与隐式回波依赖幅度建模相结合用于加速三维多回波MRI重建的有效性。
英文摘要
Three-dimensional (3D) multi-contrast magnetic resonance imaging (MCMRI) provides rich anatomical and quantitative information but requires long acquisition times, motivating k-space undersampling. However, reconstruction of large volumetric datasets imposes substantial computational and memory demands. To address this challenge, we propose SGAM, a memory-efficient, scan-specific framework for joint full-volume 3D MCMRI reconstruction. SGAM is based on a shared-geometry Gaussian representation in which amplitudes are modeled by a multi-output implicit neural representation (INR) and contrast-specific phases by explicit variables. This design exploits common anatomical structure across contrasts while preserving contrast-specific signal variations. The representation is jointly optimized using only the acquired multi-coil k-space without external training data. Experiments showed that SGAM consistently outperformed the comparison methods across imaging tasks and acceleration factors, with greater improvements under stronger undersampling. SGAM also achieved a favorable balance between reconstruction quality and computational cost, demonstrating its effectiveness for 3D multi-contrast MRI reconstruction.
