Over the past decade, Magnetic Resonance Fingerprinting (MRF) has emerged as an efficient paradigm for the rapid and simultaneous quantification of multiple MRI parameters, including fat fraction (FF), water T1 ($T1_{H2O}$), water T2 ($T2_{H2O}$), and fat T1 ($T1_{fat}$). These parameters serve as promising imaging biomarkers in various anatomical targets such as the heart, liver, and skeletal muscles. However, measuring these parameters in the upper body poses challenges due to physiological motion, particularly respiratory motion. In this work, we propose a novel approach, motion-corrected (MoCo) MRF T1-FF, which estimates the motion field using an optimized preliminary motion scan and uses it to correct the MRF acquisition data before dictionary search for reconstructing motion-corrected FF and $T1_{H2O}$ parametric maps of the upper-body region. We validated this framework using an $\textit{in vivo}$ dataset comprising ten healthy volunteers and a 10-year-old boy with Duchenne muscular dystrophy. At the ROI level, in regions minimally affected by motion, no significant bias was observed between the uncorrected and MoCo reconstructions for FF (mean difference of -0.7%) and $T1_{H2O}$ (-4.9 ms) values. Moreover, MoCo MRF T1-FF significantly reduced the standard deviations of distributions assessed in these regions, indicating improved precision. Notably, in regions heavily affected by motion, such as respiratory muscles, liver, and kidneys, the MRF parametric maps exhibited a marked reduction in motion blurring and streaking artifacts after motion correction. Furthermore, the diaphragm was consistently discernible on parametric maps after motion correction. This approach lays the groundwork for the joint 3D quantification of FF and $T1_{H2O}$ in regions that are rarely studied, such as the respiratory muscles, particularly the intercostal muscles and diaphragm.

翻译：在过去的十年中，磁共振指纹成像（MRF）已成为一种高效范式，用于快速、同步量化多种MRI参数，包括脂肪分数（FF）、水T1（$T1_{H2O}$）、水T2（$T2_{H2O}$）和脂肪T1（$T1_{fat}$）。这些参数在心脏、肝脏和骨骼肌等多种解剖靶区中作为有前景的成像生物标志物。然而，在上躯干测量这些参数因生理运动（尤其是呼吸运动）而面临挑战。在本工作中，我们提出了一种新方法——运动校正（MoCo）MRF T1-FF，该方法利用优化的初步运动扫描估计运动场，并在字典搜索前使用该运动场校正MRF采集数据，以重建上躯干区域的运动校正FF和$T1_{H2O}$参数图。我们使用一个包含十名健康志愿者和一名患有杜氏肌营养不良症的10岁男孩的体内数据集验证了该框架。在感兴趣区域层面，在受运动影响最小的区域，未校正重建与MoCo重建在FF（平均差异为-0.7%）和$T1_{H2O}$（-4.9 ms）值之间未观察到显著偏差。此外，MoCo MRF T1-FF显著降低了这些区域评估分布的标准差，表明精度得到改善。值得注意的是，在受运动严重影响区域，如呼吸肌、肝脏和肾脏，运动校正后MRF参数图的运动模糊和条纹伪影显著减少。此外，运动校正后参数图上膈肌始终清晰可辨。该方法为在呼吸肌（尤其是肋间肌和膈肌）等鲜有研究区域联合三维量化FF和$T1_{H2O}$奠定了基础。