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Iterative Structural and Functional Synergistic Resolution Recovery (iSFS-RR) Applied to PET-MR Images in Epilepsy

机译:迭代结构和功能协同分辨率恢复(isFs-RR)应用于癫痫中的pET-mR图像

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摘要

Structural Functional Synergistic Resolution Recovery (SFS-RR) is a technique that uses supplementary structural information from MR or CT to improve the spatial resolution of PET or SPECT images. This wavelet-based method may have a potential impact on the clinical decision-making of brain focal disorders such as refractory epilepsy, since it can produce images with better quantitative accuracy and enhanced detectability. In this work, a method for the iterative application of SFS-RR (iSFS-RR) was firstly developed and optimized in terms of convergence and input voxel size, and the corrected images were used for the diagnosis of 18 patients with refractory epilepsy. To this end, PET/MR images were clinically evaluated through visual inspection, atlas-based asymmetry indices (AIs) and SPM (Statistical Parametric Mapping) analysis, using uncorrected images and images corrected with SFS-RR and iSFS-RR. Our results showed that the sensitivity can be increased from 78% for uncorrected images, to 84% for SFS-RR and 94% for the proposed iSFS-RR. Thus, the proposed methodology has demonstrated the potential to improve the management of refractory epilepsy patients in the clinical routine.
机译:结构功能协同分辨率恢复(SFS-RR)是一种使用来自MR或CT的补充结构信息来提高PET或SPECT图像的空间分辨率的技术。这种基于小波的方法可能会对诸如难治性癫痫之类的脑局灶性疾病的临床决策产生潜在影响,因为它可以产生具有更好的定量准确性和增强的可检测性的图像。在这项工作中,首先开发了一种迭代应用SFS-RR的方法(iSFS-RR),并在收敛性和输入体素大小方面进行了优化,并将校正后的图像用于18例难治性癫痫患者的诊断。为此,使用未校正的图像和经SFS-RR和iSFS-RR校正的图像,通过目视检查,基于图集的不对称指数(AI)和SPM(统计参数映射)分析对PET / MR图像进行临床评估。我们的结果表明,灵敏度可以从未经校正的图像的78%增加到SFS-RR的84%和建议的iSFS-RR的94%。因此,所提出的方法论证明了在临床常规中改善难治性癫痫患者管理的潜力。

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