一种基于数据的自校准和稳健的化学位移编码，通过使用聚类（OSCAR）-神经肌肉疾病的理论，优化和临床验证
A data-Oriented based Self-Calibration And Robust chemical-shift encoding by using clusterization (OSCAR) - Theory, Optimization and Clinical Validation in Neuromuscular disorders
摘要：
用于脂肪-水定量的多回波化学位移编码方法由于其估计和校正一些混杂效应的能力而在临床使用中越来越多。现有技术的CSE水-脂肪分离方法依赖于多峰脂肪谱，其中峰值频率和相对振幅在整个MRI数据集上保持恒定。然而，由于峰值幅度的空间依赖性，后一种近似在脂质化学组成差异显著的患者（例如神经肌肉疾病）的脂肪百分比定量中引入了系统误差。本工作的目的是克服这一限制，利用无监督的基于聚类的方法提供了一个可靠的标准，进行数据驱动的分割的输入MRI数据集到多个区域。我们的想法是应用聚类划分的多回波MRI数据集成有限数量的集群，其内部体素表现出类似的距离度量。对于每个聚类，脂肪光谱特性的估计用自校准技术进行评估，最后通过非线性拟合计算脂肪-水百分比。该方法在ad-hoc和公共数据集上进行了测试。与其他国家的最先进的CSE算法的拟合精度，鲁棒性和再现性方面的整体性能和结果进行了比较。与其他校准和非校准方法相比，这种方法提供了更准确和可重复的化学物质识别，因此可以进行脂肪-水分离。
英文摘要：
Multi-echo Chemical Shift Encoded methods for Fat-Water quantification are growing in clinical use due to their ability to estimate and correct some confounding effects. State of the art CSE water-fat separation approaches rely on a multi-peak fat spectrum with peak frequencies and relative amplitudes kept constant over the entire MRI dataset. However, the latter approximation introduces a systematic error in fat percentage quantification in patients where the differences in lipid chemical composition are significant, such as for neuromuscular disorders, because of the spatial dependence of the peak amplitudes. The present work aims to overcome this limitation by taking advantage of an unsupervised clusterization-based approach offering a reliable criterion to carry out a data-driven segmentation of the input MRI dataset into multiple regions. The idea is to apply the clusterization for partitioning the multi-echo MRI dataset into a finite number of clusters whose internal voxels exhibit similar distance metrics. For each cluster, the estimation of the fat spectral properties are evaluated with a self-calibration technique and finally the fat-water percentages are computed via a non-linear fitting. The method is tested in ad-hoc and public datasets. The overall performance and results in terms of fitting accuracy, robustness and reproducibility are compared with other state-of-the-art CSE algorithms. This approach provides a more accurate and reproducible identification of chemical species, hence fat-water separation, when compared with other calibrated and non-calibrated approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
