Institute for Biomedical Engineering, University and ETH Zurich, Switzerland.
J Magn Reson Imaging. 2012 Oct;36(4):979-86. doi: 10.1002/jmri.23733. Epub 2012 Jun 11.
To determine the precision for in vivo applications of model and non-model-based bootstrap algorithms for estimating the measurement uncertainty of diffusion parameters derived from diffusion tensor imaging data.
Four different bootstrap methods were applied to diffusion datasets acquired during 10 repeated imaging sessions. Measurement uncertainty was derived in eight manually selected regions of interest and in the entire brain white matter and gray matter. The precision of the bootstrap methods was analyzed using coefficients of variation and intra-class correlation coefficients. Comprehensive simulations were performed to validate the results.
All bootstrap algorithms showed similar precision which slightly varied in dependence of the selected region of interest. The averaged coefficient of variation in the selected regions of interest was 13.81%, 12.35%, and 17.93% with respect to the apparent diffusion coefficient, the fractional anisotropy value, and the cone of uncertainty, respectively. The repeated measurements showed a very high similarity with intraclass-correlation coefficients larger than 0.96. The simulations confirmed most of the in vivo findings.
All investigated bootstrap methods perform with a similar, high precision in deriving the measurement uncertainty of diffusion parameters. Thus, the time-efficient model-based bootstrap approaches should be the method of choice in clinical practice.
确定模型和非模型基础自举算法在体内应用于估计从扩散张量成像数据得出的扩散参数的测量不确定性的精度。
将四种不同的自举方法应用于在 10 次重复成像过程中采集的扩散数据集。在手动选择的 8 个感兴趣区域和整个脑白质和灰质中得出测量不确定性。使用变异系数和组内相关系数分析自举方法的精度。进行全面的模拟以验证结果。
所有自举算法的精度都相似，仅根据所选感兴趣区域的不同略有变化。所选感兴趣区域的平均变异系数分别为表观扩散系数、各向异性分数值和不确定锥的 13.81%、12.35%和 17.93%。重复测量的组内相关系数大于 0.96，非常相似。模拟证实了大多数体内发现。
所有研究的自举方法在得出扩散参数的测量不确定性方面都具有相似的高精度。因此，在临床实践中，高效的基于模型的自举方法应该是首选方法。
