发表机构
University Medical Center Hamburg-Eppendorf; Hamburg University of Technology; Fraunhofer Research Institution for Individualized Medical Technology and Engineering IMTE（汉堡-埃彭多夫大学医学中心; 汉堡工业大学; 弗劳恩霍夫个性化医疗技术与工程研究所 IMTE）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对化学工程中快速评估化学成分空间变化的需求，采用基于模型的重建框架，嵌入先验光谱知识并考虑场不均匀性，通过体模实验验证方法有效性及精度，应用稀疏k空间采样可进一步减少采集时间。
AI 中文摘要
磁共振光谱成像方法在化学工程应用中很有吸引力，如监测化学反应时需要快速评估化学成分的空间变化。传统方法增加光谱编码维度会大幅增加采集时间，因此快速空间分辨光谱仍是研究热点。本文采用基于模型的重建框架，将先验光谱知识嵌入正向模型加速成分映射，可在不获取高分辨率光谱的情况下重建各化学成分的摩尔比图。扩展前人研究，该模型考虑了主磁场不均匀性，在大口径系统中更显著。二维多梯度回波序列的体模实验表明，该方法能确定单峰及多峰化学成分的摩尔比。20秒扫描时，方法偏差和精度分别约为0.01 mol/mol和0.09 mol/mol，适用于动态过程。应用稀疏k空间采样可进一步减少采集时间，扫描可能缩短至5秒且定量性能仅有轻微下降。
英文摘要
Magnetic resonance spectroscopic imaging methods are particularly attractive for chemical engineering applications, including the monitoring of chemical reactions, where a rapid assessment of spatial variations in chemical composition is required. Conventional approaches, such as chemical shift imaging, introduce an additional spectral-encoding dimension, which substantially increases acquisition time. Consequently, fast spatially resolved spectroscopy remains an active research topic. This work uses a model-based reconstruction framework that embeds a priori spectral knowledge of the involved chemical components into the forward model to accelerate composition mapping. It allows for the reconstruction of molar ratio maps for individual chemical components without acquiring high-resolution spectra. Extending from previous studies, the proposed model accounts for inhomogeneities of the main field, which become more pronounced in systems with larger bores relevant for process engineering. Phantom experiments employing a 2D multi-gradient echo sequence demonstrate the ability to determine molar ratios for chemical components with single peaks as well as multiple peaks in their spectra. The bias and precision of the method remain around 0.01 mol/mol and 0.09 mol/mol, respectively, for a 20 s scan, indicating suitability for dynamic processes. Finally, acquisition time can be reduced further by applying sparse k-space sampling, potentially shortening the scan to 5 s with only minor degradation in quantitative performance.
