Kim Dae-Jin, Park Hae-Jeong, Kang Kyung-Whun, Shin Yong-Wook, Kim Jae-Jin, Moon Won-Jin, Chung Eun-Chul, Kim In Young, Kwon Jun Soo, Kim Sun I
Department of Biomedical Engineering, Hanyang University, and Department of Radiology, Kangbuk Samsung Hospital, Seoul 133-605, South Korea.
Magn Reson Imaging. 2006 Dec;24(10):1369-76. doi: 10.1016/j.mri.2006.07.014. Epub 2006 Oct 25.
The purpose of this study was to determine a suitable registration algorithm for diffusion tensor imaging (DTI) using conventional preprocessing tools [statistical parametric mapping (SPM) and automated image registration (AIR)] and to investigate how anisotropic indices for clinical assessments are affected by these distortion corrections.
Brain DTI data from 15 normal healthy volunteers were used to evaluate four spatial registration schemes within subjects to correct image distortions: noncorrection, SPM-based affine registration, AIR-based affine registration and AIR-based nonlinear polynomial warping. The performance of each distortion correction was assessed using: (a) quantitative parameters: tensor-fitting error (Ef), mean dispersion index (MDI), mean fractional anisotropy (MFA) and mean variance (MV) within 11 regions of interest (ROI) defined from homogeneous fiber bundles; and (b) fiber tractography through the uncinate fasciculus and the corpus callosum. Fractional anisotropy (FA) and mean diffusivity (MD) were calculated to demonstrate the effects of distortion correction. Repeated-measures analysis of variance was used to investigate differences among the four registration paradigms.
AIR-based nonlinear registration showed the best performance for reducing image distortions with respect to smaller Ef (P<.02), MDI (P<.01) and MV (P<.01) with larger MFA (P<.01). FA was decreased to correct distortions (P<.0001) whether the applied registration was linear or nonlinear and was lowest after nonlinear correction (P<.001). No significant differences were found in MD.
In conventional DTI processing, anisotropic indices of FA can be misestimated by noncorrection or inappropriate distortion correction, which leads to an erroneous increase in FA. AIR-based nonlinear distortion correction would be required for a more accurate measurement of this diffusion parameter.
本研究的目的是使用传统的预处理工具[统计参数映射（SPM）和自动图像配准（AIR）]确定一种适用于扩散张量成像（DTI）的配准算法，并研究这些失真校正如何影响用于临床评估的各向异性指数。
使用来自15名正常健康志愿者的脑DTI数据来评估受试者内的四种空间配准方案，以校正图像失真：不校正、基于SPM的仿射配准、基于AIR的仿射配准和基于AIR的非线性多项式变形。使用以下方法评估每种失真校正的性能：（a）定量参数：在由均匀纤维束定义的11个感兴趣区域（ROI）内的张量拟合误差（Ef）、平均弥散指数（MDI）、平均分数各向异性（MFA）和平均方差（MV）；（b）通过钩束和胼胝体的纤维束成像。计算分数各向异性（FA）和平均扩散率（MD）以证明失真校正的效果。采用重复测量方差分析来研究四种配准范式之间的差异。
基于AIR的非线性配准在减少图像失真方面表现最佳，其Ef较小（P<0.02）、MDI较小（P<0.01）和MV较小（P<0.），而MFA较大（P<0.01）。无论应用的配准是线性还是非线性，FA都会降低以校正失真（P<0.0001），并且在非线性校正后最低（P<0.001）。MD未发现显著差异。
在传统的DTI处理中，不校正或不适当的失真校正可能会错误估计FA的各向异性指数，从而导致FA错误增加。为了更准确地测量这种扩散参数，需要基于AIR的非线性失真校正。
