用保边加权正则化校正99mTc-TRODAT-1脑SPECT图像的部分体积效应

T. Yin, N. Chiu
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引用次数: 0

摘要

脑SPECT体积中由于SPECT低分辨率而产生的部分体积效应(PVE)可以建模为三维点扩散函数(PSF)与潜在的真实放射性的卷积。本文提出了一种以几何传递矩阵(GTM)方法中的边缘位置为指导的加权正则化反卷积方法(RGTM),以综合考虑卷积误差和区域均匀性先验信息对PVE (PVC)校正的影响。经过两个步骤:GTM和加权正则化。为了比较RGTM与Van-Cittert反卷积(VC)、GTM和基于区域的体素校正(RBV)的性能,进行了20次数字模拟。对84名健康成人99mTc-TRODAT-1 SPECT和MRI扫描的临床数据也进行了测试。由于所提出的RGTM在恒定和非恒定roi中都具有良好的鲁棒性,因此在底层放射性分布不确切的情况下,其鲁棒性优于其他方法。
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Correction of partial volume effect in 99mTc-TRODAT-1 brain SPECT images using an edge-preserving weighted regularization
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.
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