Quantifying in vivo MR spectra with circles
Quantifying in vivo MR spectra with circles
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DOI：
10.1016/j.jmr.2005.11.004
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发表时间：
2006-03-01
影响因子：
2.2
通讯作者：
Bottomley, PA
中科院分区：
文献类型：
--
作者：
Gabr, RE;Ouwerkerk, R;Bottomley, PA
体内磁共振波谱（MRS）数据的准确和鲁棒量化对于其在研究和医学中的应用至关重要。现有分析方法的性能对于低信噪比、重叠峰和强烈伪影是地方性的体内研究是有问题的。在这里，介绍了一种新的频域技术的MRS数据分析，其中的圆形轨迹时，频谱峰值投影到复平面上，与活动的圆模型拟合。主动轮廓策略的使用自然地允许将先验知识作为约束能量项并入。相位谱的问题被消除，基线文物处理使用主动轮廓蛇。新技术CFIT的稳定性和准确性与标准时域拟合工具进行了比较，使用具有不同噪声量的模拟P-31数据和98个真实的人类胸部和心脏P-31 MRS数据集。真实的数据也进行了分析，我们的标准频域吸收模式技术。在真实的数据上，CFIT证明了所有方法中最少的拟合失败和与后一种方法相似的精度，这两种技术都优于时域方法。模拟结果的对比表明，相对于Cramer-Rao界限的性能可能不是典型体内数据（如这些）拟合性能的合适指标。我们得出结论，CFIT是一种稳定，准确的替代现有的最佳方法拟合体内数据。(c)2005年爱思唯尔公司All rights reserved.
Accurate and robust quantification of in vivo magnetic resonance spectroscopy (MRS) data is essential to its application in research and medicine. The performance of existing analysis methods is problematic for in vivo studies where low signal-to-noise ratio, overlapping peaks and intense artefacts are endemic. Here, a new frequency-domain technique for MRS data analysis is introduced wherein the circular trajectories which result when spectral peaks are projected onto the complex plane, are fitted with active circle models. The use of active contour strategies naturally allows incorporation of prior knowledge as constraint energy terms. The problem of phasing spectra is eliminated, and baseline artefacts are dealt with using active contours-snakes. The stability and accuracy of the new technique, CFIT, is compared with a standard time-domain fitting tool, using simulated P-31 data with varying amounts of noise and 98 real human chest and heart P-31 MRS data sets. The real data were also analyzed by our standard frequency-domain absorption-mode technique. On the real data, CFIT demonstrated the least fitting failures of all methods and an accuracy similar to the latter method, with both these techniques outperforming the time-domain approach. Contrasting results from simulations argue that performance relative to Cramer-Rao Bounds may not be a suitable indicator of fitting performance with typical in vivo data such as these. We conclude that CFIT is a stable, accurate alternative to the best existing methods of fitting in vivo data. (c) 2005 Elsevier Inc. All rights reserved.