A new method for improving functional-to-structural MRI alignment using local Pearson correlation.
A new method for improving functional-to-structural MRI alignment using local Pearson correlation.
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一种使用局部Pearson相关性改善功能到结构MRI对准的新方法。
DOI：
10.1016/j.neuroimage.2008.09.037
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发表时间：
2009-02-01
期刊：
影响因子：
5.7
通讯作者：
Cox RW
中科院分区：
文献类型：
--
作者：
Saad ZS;Glen DR;Chen G;Beauchamp MS;Desai R;Cox RW
功能磁共振成像(FMRI)T2*加权体积与同一受试者高分辨率T1加权结构体积的准确配准对于血液氧合水平依赖(BOLD)功能磁共振成像非常重要，并且对于基于皮质表面的分析和术前规划等应用至关重要。这种配准通常通过最小化代价泛函来实现，该代价泛函测量在适当仿射变换组上的两个图像体积之间的失配。广泛使用的成本函数，如互信息(MI)和相关比(CR)，当通过匹配大脑外部轮廓进行视觉判断时，似乎产生了良好的比对。然而，仔细观察发现，大脑内部结构经常严重错位。脑室和脑沟皱褶是脑脊液集中的部位，注册不良最为明显。这一观察结果促使我们开发了一种改进的特定于医疗设备的成本函数，它使用加权的局部皮尔逊系数(LPC)来对齐T2*和T1加权图像。在没有对齐黄金标准的情况下，我们使用了三个人类观察者盲目注册的方法来为每个成本函数提供注册质量的独立评估。我们发现，LPC的表现明显好于一般成本函数(包括MI和CR)(p<；0.001)。一般成本泛函往往不是最小的，接近最佳排列，因此表明优化并不是它们失败的原因。最后，我们强调了精确视觉检测对准质量的重要性，并提出了一种自动生成合成图像的方法，以帮助捕获未对准误差。
Accurate registration of Functional Magnetic Resonance Imaging (FMRI) T2*-weighted volumes to same-subject high-resolution T1-weighted structural volumes is important for Blood Oxygenation Level Dependent (BOLD) FMRI and crucial for applications such as cortical surface-based analyses and pre-surgical planning. Such registration is generally implemented by minimizing a cost functional, which measures the mismatch between two image volumes over the group of proper affine transformations. Widely used cost functionals, such as mutual information (MI) and correlation ratio (CR), appear to yield decent alignments when visually judged by matching outer brain contours. However, close inspection reveals that internal brain structures are often significantly misaligned. Poor registration is most evident in the ventricles and sulcal folds, where CSF is concentrated. This observation motivated our development of an improved modality-specific cost functional which uses a weighted local Pearson coefficient (LPC) to align T2*- and T1-weighted images. In the absence of an alignment gold standard, we used three human observers blinded to registration method to provide an independent assessment of the quality of the registration for each cost functional. We found that LPC performed significantly better (p < 0.001) than generic cost functionals including MI and CR. Generic cost functionals were very often not minimal near the best alignment, thereby suggesting that optimization is not the cause of their failure. Lastly, we emphasize the importance of precise visual inspection of alignment quality and present an automated method for generating composite images that help capture errors of misalignment.
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