Dynamic PET denoising with HYPR processing.

Dynamic PET denoising with HYPR processing.
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DOI:
10.2967/jnumed.109.073999
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发表时间:
2010-07
期刊:
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
影响因子:
--
通讯作者:
Mistretta CA
Mistretta CA
中科院分区:
其他
文献类型:
--
作者:
Christian BT;Vandehey NT;Floberg JM;Mistretta CA

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高度约束反投影 (HYPR) 是一种很有前途的图像处理策略,在时间分辨 MRI 中得到广泛应用,也非常适合需要时间序列数据的 PET 应用。 HYPR 技术涉及从整个时间序列创建合成图像。然后,各个时间范围为复合的权重矩阵提供基础。使用合成图像的高信噪比可以显着提高各个时间帧的信噪比 (SNR)。在本研究中,我们引入了改进的 HYPR 算法(将反投影限制到局部感兴趣区域的 HYPR 方法 [HYPR-LR]),用于处理动态 PET 研究。我们通过定性、半定量和定量比较展示了 HYPR-LR 在体模、小动物和人体研究中的性能。结果表明,在 PET 时间序列中,尤其是基于体素的分析,可以在不牺牲空间分辨率的情况下实现信噪比的显着改善。 HYPR-LR 处理在动态扫描中低信噪比的所有核医学成像应用中具有巨大潜力,包括生成基于体素的参数图像以及快速放射性示踪剂摄取和分布的可视化。
HighlY constrained backPRojection (HYPR) is a promising image-processing strategy with widespread application in time-resolved MRI that is also well suited for PET applications requiring time series data. The HYPR technique involves the creation of a composite image from the entire time series. The individual time frames then provide the basis for weighting matrices of the composite. The signal-to-noise ratio (SNR) of the individual time frames can be dramatically improved using the high SNR of the composite image. In this study, we introduced the modified HYPR algorithm (the HYPR method constraining the backprojections to local regions of interest [HYPR-LR]) for the processing of dynamic PET studies. We demonstrated the performance of HYPR-LR in phantom, small-animal, and human studies using qualitative, semiquantitative, and quantitative comparisons. The results demonstrate that significant improvements in SNR can be realized in the PET time series, particularly for voxel-based analysis, without sacrificing spatial resolution. HYPR-LR processing holds great potential in nuclear medicine imaging for all applications with low SNR in dynamic scans, including for the generation of voxel-based parametric images and visualization of rapid radiotracer uptake and distribution.
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期刊: RADIOLOGY
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