肺SPECT - CT配准的扩展归一化互信息

L. Papp, M. Zuhayra, E. Henze, Ulf Luetzen
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引用次数: 5

摘要

本文提出了一种归一化互信息的扩展,将独立获得的低剂量CT图像与已经叠加的肺吸入SPECT和灌注SPECT图像进行配准。为了验证我们的方法,采用混合SPECT/CT相机同时采集了吸入SPECT、灌注SPECT和低剂量CT叠加图像。将已知变换应用于低剂量CT以模拟不对准,然后执行扩展归一化互信息(eNMI)方法进行最优变换搜索。为了比较和评价,我们还采用双归一化互信息(dual normalized mutual information-based, dNMI)方法对低剂量CT的吸入和灌注图像逐一进行配准。对比结果表明,我们的eNMI方法具有最小的配准误差和迭代次数,因此它可以成功地应用于单独进行低剂量CT - SPECT配准。我的介绍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extended Normalized Mutual Information for Lung SPECT - CT Registration
In this paper an extension of the normalized mutual information is proposed to register standalone obtained low dose CT with already superimposed lung inhalation SPECT and perfusion SPECT images. In order to validate our method, superimposed inhalation SPECT, perfusion SPECT and low dose CT image triples were collected obtained by a hybrid SPECT/CT camera at the same time. A known transformation was applied to the low dose CT to simulate a misalignment, followed by an optimal transformation search performing our extended normalized mutual information-based (eNMI) method. For comparison and evaluation, the low dose CT was also registered to both inhalation and perfusion images one-by-one applying a dual-normalized mutual information-based (dNMI) method. Comparative results have shown that our eNMI method worked with minimal registration error and number of iterations, hence it can be successfully applied to stand alone performed low dose CT - SPECT registrations. I. INTRODUCTION
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