Meijs Midas, Christensen Soren, Lansberg Maarten G, Albers Gregory W, Calamante Fernando
Eindhoven University of Technology, Eindhoven, The Netherlands.
Stanford Stroke Center, Stanford University School of Medicine, Stanford, California, USA.
Magn Reson Med. 2016 Oct;76(4):1282-90. doi: 10.1002/mrm.26024. Epub 2015 Oct 31.
There is currently controversy regarding the benefits of deconvolution-based parameters in stroke imaging, with studies suggesting a similar infarct prediction using summary parameters. We investigate here the performance of deconvolution-based parameters and summary parameters for dynamic-susceptibility contrast (DSC) MRI analysis, with particular emphasis on precision.
Numerical simulations were used to assess the contribution of noise and arterial input function (AIF) variability to measurement precision. A realistic AIF range was defined based on in vivo data from an acute stroke clinical study. The simulated tissue curves were analyzed using two popular singular value decomposition (SVD) based algorithms, as well as using summary parameters.
SVD-based deconvolution methods were found to considerably reduce the AIF-dependency, but a residual AIF bias remained on the calculated parameters. Summary parameters, in turn, show a lower sensitivity to noise. The residual AIF-dependency for deconvolution methods and the large AIF-sensitivity of summary parameters was greatly reduced when normalizing them relative to normal tissue.
Consistent with recent studies suggesting high performance of summary parameters in infarct prediction, our results suggest that DSC-MRI analysis using properly normalized summary parameters may have advantages in terms of lower noise and AIF-sensitivity as compared to commonly used deconvolution methods. Magn Reson Med 76:1282-1290, 2016. © 2015 Wiley Periodicals, Inc.
目前，基于去卷积的参数在中风成像中的益处存在争议，研究表明使用汇总参数可进行类似的梗死预测。我们在此研究基于去卷积的参数和汇总参数在动态磁敏感对比（DSC）MRI分析中的性能，特别强调精度。
使用数值模拟来评估噪声和动脉输入函数（AIF）变异性对测量精度的影响。根据急性中风临床研究的体内数据定义了现实的AIF范围。使用两种流行的基于奇异值分解（SVD）的算法以及汇总参数对模拟的组织曲线进行分析。
发现基于SVD的去卷积方法可显著降低对AIF的依赖性，但计算出的参数上仍存在残余的AIF偏差。相反，汇总参数对噪声的敏感性较低。相对于正常组织对去卷积方法的残余AIF依赖性和汇总参数的大AIF敏感性进行归一化后，可大大降低。
与最近表明汇总参数在梗死预测中具有高性能的研究一致，我们的结果表明，与常用的去卷积方法相比，使用适当归一化的汇总参数进行DSC-MRI分析在降低噪声和AIF敏感性方面可能具有优势。《磁共振医学》76:1282 - 1290, 2016。© 2015威利期刊公司。
