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Correction of partial volume effect in 99mTc-TRODAT-1 brain SPECT images using an edge-preserving weighted regularization

机译:使用保留边缘的加权正则化校正99mTc-TRODAT-1脑SPECT图像中的部分体积效应

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The partial volume effect (PVE) due to the low resolution of SPECT in brain SPECT volumes can be modeled as a convolution of a three-dimensional point-spread function (PSF) with the underlying true radioactivity. In this paper, a deconvolution guided by the edge locations in the geometric transfer matrix (GTM) method as a weighted regularization, denoted as RGTM, was proposed to take into account both the discrepancy from the convolution and the regional-homogeneity prior information in the correction of the PVE (PVC). Two steps were conducted: GTM and then a weighted regularization. Twenty digital phantom simulations were made to compare the performance of RGTM with those of Van-Cittert deconvolution (VC), GTM, and the region-based voxel-wise correction (RBV). Clinical data from eighty-four healthy adults with 99mTc-TRODAT-1 SPECT and MRI scans were also tested. Because the proposed RGTM was good in both constant and non-constant ROIs, its robustness is better than other methods if the distribution of the underlying radioactivity is not known exactly.
机译:由于脑SPECT体积中SPECT分辨率低而导致的部分体积效应(PVE)可以建模为三维点扩散函数(PSF)与基础真实放射性的卷积。本文提出了一种以几何传递矩阵(GTM)方法中的边缘位置为导向的反卷积作为加权正则化方法(表示为RGTM),该方法考虑了卷积中的差异和区域同质性先验信息。修正PVE(PVC)。进行了两个步骤:GTM,然后进行加权正则化。进行了二十次数字幻像仿真,以比较RGTM的性能与Van-Cittert反卷积(VC),GTM和基于区域的体素方向校正(RBV)的性能。还对来自84名健康成年人的99mTc-TRODAT-1 SPECT和MRI扫描的临床数据进行了测试。由于建议的RGTM在恒定和非恒定ROI方面均表现出色,因此,如果无法确切知道潜在放射性的分布,其稳健性将优于其他方法。

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