Patch-based local learning method for cerebral blood flow quantification with arterial spin-labeling MRI
Patch-based local learning method for cerebral blood flow quantification with arterial spin-labeling MRI
复制标题
基于斑块的局部学习方法用于动脉自旋标记 MRI 脑血流定量
DOI：
10.1007/s11517-017-1735-6
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发表时间：
2018-06
影响因子：
3.2
通讯作者：
Wang Ze
中科院分区：
文献类型：
--
作者：
Zhu Hancan;He Guanghua;Wang Ze
动脉自旋标记（ASL）灌注MRI是一种非侵入性的定量脑血流量（CBF）的方法。标准ASL CBF校准主要依赖于自旋标记图像的成对减法，并分别控制每个体素处的图像，忽略了ASL数据中丰富的空间相关性。为了解决这个问题，我们以前提出了一个多变量支持向量机（SVM）学习为基础的算法ASL CBF量化（SVMASLQ）。但原始SVMASLQ被设计为同时对所有图像体素进行CBF量化，这对于考虑局部信号和噪声变化并不理想。为了解决这个问题，我们在本文中扩展SVMASLQ到一个补丁的方法，通过使用补丁的分类核。在每个体素处，从控制图像和标记图像中提取以该体素为中心的图像块，然后将其输入到SVMASLQ中，以使用非线性SVM分类器找到替代灌注图的对应块。这些补丁最终被组合成最终的灌注图。方法评价使用ASL数据从30个年轻的健康受试者。结果表明，与非块式SVMASLQ相比，块式SVMASLQ使灌注图SNR增加了6.6%。
Arterial spin-labeling (ASL) perfusion MRI is a non-invasive method for quantifying cerebral blood flow (CBF). Standard ASL CBF calibration mainly relies on pair-wise subtraction of the spin-labeled images and controls images at each voxel separately, ignoring the abundant spatial correlations in ASL data. To address this issue, we previously proposed a multivariate support vector machine (SVM) learning-based algorithm for ASL CBF quantification (SVMASLQ). But the original SVMASLQ was designed to do CBF quantification for all image voxels simultaneously, which is not ideal for considering local signal and noise variations. To fix this problem, we here in this paper extended SVMASLQ into a patch-wise method by using a patch-wise classification kernel. At each voxel, an image patch centered at that voxel was extracted from both the control images and labeled images, which was then input into SVMASLQ to find the corresponding patch of the surrogate perfusion map using a non-linear SVM classifier. Those patches were eventually combined into the final perfusion map. Method evaluations were performed using ASL data from 30 young healthy subjects. The results showed that the patch-wise SVMASLQ increased perfusion map SNR by 6.6% compared to the non-patch-wise SVMASLQ.
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影响因子：
4.8
作者：
Wang, Ze
通讯作者：
Wang, Ze
影响因子：
2.6
作者：
Mirman D;Zhang Y;Wang Z;Coslett HB;Schwartz MF
通讯作者：
Schwartz MF
影响因子：
4.8
作者：
Zhang, Yongsheng;Kimberg, Daniel Y.;Coslett, H. Branch;Schwartz, Myrna F.;Wang, Ze
通讯作者：
Wang, Ze
影响因子：
3.2
作者：
Kim, KH;Bang, SW;Kim, SR
通讯作者：
Kim, SR
DOI：
10.1007/s10334-010-0209-8
发表时间：
2010-06-01
影响因子：
2.3
作者：
Bibic, Adnan;Knutsson, Linda;Wirestam, Ronnie
通讯作者：
Wirestam, Ronnie