Fast diffusion kurtosis imaging (DKI) with Inherent COrrelation-based Normalization (ICON) enhances automatic segmentation of heterogeneous diffusion MRI lesion in acute stroke.
Fast diffusion kurtosis imaging (DKI) with Inherent COrrelation-based Normalization (ICON) enhances automatic segmentation of heterogeneous diffusion MRI lesion in acute stroke.
复制标题
具有基于固有相关性的归一化 (ICON) 的快速扩散峰度成像 (DKI) 增强了急性中风中异质扩散 MRI 病变的自动分割。
DOI：
10.1002/nbm.3617
复制
发表时间：
2016-12
影响因子：
2.9
通讯作者：
Sun, Phillip Zhe
中科院分区：
文献类型：
--
作者：
Zhou, Iris Yuwen;Guo, Yingkun;Igarashi, Takahiro;Wang, Yu;Mandeville, Emiri;Chan, Suk-Tak;Wen, Lingyi;Vangel, Mark;Lo, Eng H.;Ji, Xunming;Sun, Phillip Zhe
关键词：
弥散峰度成像（DKI）已被证明可以增强DWI来确定不可逆的缺血性损伤。然而，大脑结构/组成的复杂性使得峰度图具有异质性，限制了峰度高强度对急性缺血的特异性。我们提出了一种基于内在相关的归一化（ICON）分析来抑制内在峰度异质性，以改善异质性缺血性组织损伤的表征。对正常大鼠（N=10）和脑卒中大鼠（N=20）进行MCAO后快速DKI和舒张测量。我们评估了快速DKI序列的平均峰度（MK）、平均扩散率（MD）、分数各向异性（FA）与R1和R2弛豫率之间的相关性，发现MK与R1之间具有高度显著的相关性（P<0.001）。我们发现，ICON分析抑制了正常脑组织的固有峰度异质性，使动物中风模型中的组织自动分割成为可能。我们发现峰度和弥散性病变体积差异显著，分别为147±59和180±66 mm3 （P=0.003，配对t检验）。峰度与弥漫性病变体积之比为84±19% （P<0.001，单样本t检验）。我们发现松弛归一化MK （RNMK）而非MD值在峰度和弥漫性病变之间存在显著差异（P<0.001，方差分析）。我们的研究表明，快速DKI与ICON分析为非均匀DWI脑卒中病变的划分提供了一种有希望的方法。峰度增加DWI以确定不可逆缺血性损伤。然而，大脑结构/组成的复杂性使得峰度图具有异质性，限制了峰度高强度对急性缺血的特异性。由于平均峰度与R1之间存在很强的相关性，我们提出了一种基于固有相关性的归一化（ICON）方法，通过大幅减少扫描时间来减轻正常大脑的峰度异质性。我们进一步证明，这种方法能够自动分割病变，增强异质性DWI病变的分层，有助于将快速DKI转化为急性卒中设置。
Diffusion kurtosis imaging (DKI) has been shown to augment DWI for defining irreversible ischemic injury. However, the complexity of cerebral structure/composition makes kurtosis map heterogeneous, limiting the specificity of kurtosis hyperintensity to acute ischemia. We proposed an Inherent COrrelation-based Normalization (ICON) analysis to suppress the intrinsic kurtosis heterogeneity for improved characterization of heterogeneous ischemic tissue injury. Fast DKI and relaxation measurements were performed on normal (N=10) and stroke rats following MCAO (N=20). We evaluated the correlations between mean kurtosis (MK), mean diffusivity (MD), fractional anisotropy (FA) derived from fast DKI sequence and relaxation rates of R1 and R2, and found highly significant correlation between MK and R1 (P<0.001). We showed that ICON analysis suppressed the intrinsic kurtosis heterogeneity in the normal cerebral tissue, enabling automated tissue segmentation in an animal stroke model. We found significantly different kurtosis and diffusivity lesion volumes, being 147±59 and 180±66 mm3, respectively (P=0.003, Paired-t test). The ratio of kurtosis to diffusivity lesion volume was 84±19% (P<0.001, One-sample t-test). We found relaxation normalized MK (RNMK) but not MD values significantly different between kurtosis and diffusivity lesions (P<0.001, ANOVA). Our study showed that fast DKI with ICON analysis provides a promising means for demarcating heterogeneous DWI stroke lesion. Kurtosis augments DWI for defining irreversible ischemic injury. However, the complexity of cerebral structure/composition makes kurtosis map heterogeneous, limiting the specificity of kurtosis hyperintensity to acute ischemia. With strong correlation found between mean kurtosis and R1, we proposed an Inherent COrrelation-based Normalization (ICON) approach to mitigate the kurtosis heterogeneity in normal brain with substantially reduced scan time. We further demonstrated that this approach enabled automatic lesion segmentation and enhanced stratification of heterogeneous DWI lesion, aiding the translation of fast DKI to the acute stroke setting.
登录
查看更多内容
影响因子：
2.9
作者：
Jensen, Jens H.;Helpern, Joseph A.
通讯作者：
Helpern, Joseph A.
影响因子：
4.4
作者：
Helpern, Joseph A.;Adisetiyo, Vitria;Falangola, Maria F.;Hu, Caixia;Di Martino, Adriana;Williams, Kathleen;Castellanos, Francisco X.;Jensen, Jens H.
通讯作者：
Jensen, Jens H.
影响因子：
8.3
作者：
Baron, Corey Allan;Kate, Mahesh;Beaulieu, Christian
通讯作者：
Beaulieu, Christian
影响因子：
4.4
作者：
Falangola, Maria F.;Jensen, Jens H.;Babb, James S.;Hu, Caixia;Castellanos, Francisco X.;Di Martino, Adriana;Ferris, Steven H.;Helpern, Joseph A.
通讯作者：
Helpern, Joseph A.
影响因子：
8.3
作者：
Cheung JS;Wang E;Lo EH;Sun PZ
通讯作者：
Sun PZ