用神经隐式表示实现3D多参数MRI零样本重建，大幅缩短扫描时间
Coordinate-Based Neural Representation Enabling Zero-Shot Learning for 3D Multiparametric Quantitative MRI
- 通过坐标编码将多参数信息嵌入欠采样k空间
- 无需外部训练数据，直接重建T1/T2/T2*/QSM四图
- 适用于多种医学成像，开启零样本学习新范式
定量磁共振成像（qMRI）可提供组织特异性物理参数，在神经科学研究和临床中具有重要潜力。然而，3D多参数qMRI采集耗时过长，限制了其临床应用。本文提出SUMMIT，一种创新的成像方法，包含数据采集与无监督重建。SUMMIT首先将多个关键定量特性编码至高度欠采样的k空间；进一步结合专用物理模型的隐式神经表示，实现无需外部训练数据的多参数图重建。该方法可生成共注册的T1、T2、T2*及定量磁化率图。大量仿真与体模实验验证了SUMMIT的高精度。此外，所提出的无监督重建方法还引入了一种适用于多种医学成像模态的新型零样本学习范式。
原文摘要 · Abstract (English)
Quantitative magnetic resonance imaging (qMRI) offers tissue-specific physical parameters with significant potential for neuroscience research and clinical practice. However, lengthy scan times for 3D multiparametric qMRI acquisition limit its clinical utility. Here, we propose SUMMIT, an innovative imaging methodology that includes data acquisition and an unsupervised reconstruction for simultaneous multiparametric qMRI. SUMMIT first encodes multiple important quantitative properties into highly undersampled k-space. It further leverages implicit neural representation incorporated with a dedicated physics model to reconstruct the desired multiparametric maps without needing external training datasets. SUMMIT delivers co-registered T1, T2, T2*, and quantitative susceptibility mapping. Extensive simulations and phantom imaging demonstrate SUMMIT's high accuracy. Additionally, the proposed unsupervised approach for qMRI reconstruction also introduces a novel zero-shot learning paradigm for multiparametric imaging applicable to various medical imaging modalities.
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