谷歌浏览器插件
订阅小程序
在清言上使用

An Efficient Approach to Perform MR-Assisted PET Data Optimization in Simultaneous PET/MR Neuroimaging Studies

˜The œJournal of nuclear medicine(2018)

引用 16|浏览38
暂无评分
摘要
A main advantage of PET is that it provides quantitative measures of the radiotracer concentration, but its accuracy is confounded by factors including attenuation, subject motion, and limited spatial resolution. Using the information from one simultaneously acquired morphologic MR sequence with embedded navigators for MR motion correction (MC), we propose an efficient method, MR-assisted PET data optimization (MaPET), for attenuation correction (AC), PET MC, and anatomy-aided reconstruction. Methods: For AC, voxelwise linear attenuation coefficient maps were generated using an SPM8-based method on the MR volume. The embedded navigators were used to derive head motion estimates for event-based PET MC. The anatomy provided by the MR volume was incorporated into the PET image reconstruction using a kernel-based method. Region-based analyses were performed to assess the quality of images generated through various stages of PET data optimization. Results: The optimized PET images reconstructed with MaPET were superior in image quality to images reconstructed using only AC, with high signal-to-noise ratio and low coefficient of variation (5.08 and 0.229 in a composite cortical region compared with 3.12 and 0.570, P < 10−4 for both comparisons). The optimized images were also shown using the Cohen’s d metric to achieve a greater effect size in distinguishing cortical regions with hypometabolism from regions of preserved metabolism. Conclusion: We have shown that the spatiotemporally correlated data acquired using a single MR sequence can be used for PET attenuation, motion, and partial-volume effects corrections and that the MaPET method may enable more accurate assessment of pathologic changes in dementia and other brain disorders.
更多
查看译文
关键词
PET/MRI,motion correction,anatomy-aided reconstruction,simultaneous imaging
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要