A Spatio-Temporal Deconvolution Method to Improve Perfusion CT Quantification
A Spatio-Temporal Deconvolution Method to Improve Perfusion CT Quantification
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DOI：
10.1109/tmi.2010.2043536
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发表时间：
2010-05-01
影响因子：
10.6
通讯作者：
Pien, Homer
中科院分区：
文献类型：
--
作者：
He, Lili;Orten, Burkay;Pien, Homer
灌注成像在许多诊断和治疗监测环境中是解剖成像的有用辅助手段。灌注成像的一种方法是假设局部动脉输入函数与感兴趣区域的组织增强轮廓之间经由“残差函数”的卷积关系，并且随后求解该残差函数。这种不适定问题通常使用基于奇异值分解的方法来解决，并且针对每个体素独立地求解血液动力学参数。在本文中，我们提出了一种配方，它结合了空间和时间的相关性，并通过模拟表明，这种新的配方产生更高的精度和更大的鲁棒性相对于图像噪声。我们还使用直肠癌肿瘤图像表明，这种新的配方可以更好地分离正常和癌性体素。
Perfusion imaging is a useful adjunct to anatomic imaging in numerous diagnostic and therapy-monitoring settings. One approach to perfusion imaging is to assume a convolution relationship between a local arterial input function and the tissue enhancement profile of the region of interest via a "residue function" and subsequently solve for this residue function. This ill-posed problem is generally solved using singular-value decomposition based approaches, and the hemodynamic parameters are solved for each voxel independently. In this paper, we present a formulation which incorporates both spatial and temporal correlations, and show through simulations that this new formulation yields higher accuracy and greater robustness with respect to image noise. We also show using rectal cancer tumor images that this new formulation results in better segregation of normal and cancerous voxels.