Data-driven optimal binning for respiratory motion management in PET
Data-driven optimal binning for respiratory motion management in PET
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DOI：
10.1002/mp.12651
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发表时间：
2018-01-01
期刊：
影响因子：
3.8
通讯作者：
Lynch, David A.
中科院分区：
文献类型：
--
作者：
Kesner, Adam L.;Meier, Joseph G.;Lynch, David A.
目的：呼吸门控已用于PET成像，以减少患者运动引起的图像模糊量。最优分箱是一种通过将运动特征化数据分箱到单个易于理解/使用的最优分箱中来使用运动特征化数据的方法。到目前为止，最佳分箱协议已经利用外部驱动的运动表征策略，这些策略已经用人口衍生的假设和参数进行了调整。在这项工作中，我们提出了一种新的策略，直接从病人的门控扫描，并使用该信号来创建一个病人/实例特定的最佳bin images.Methods：二百一十九相位门控FDG PET扫描，采集使用数据驱动门控，如前所述，被用作本研究的输入。对于每次扫描，使用主成分分析生成并归一化相位-幅度运动表征。使用这种表征，通过反映传统最佳窗口分箱策略的方法，推导出特定于患者的最佳分箱窗口。由此产生的最佳箱图像进行了验证相关的定量和定性测量的人口PET scans.Results：在53%（n = 115）的图像人口，最佳箱确定为包括100%的图像统计。在剩余的图像中，最佳合并窗口平均占统计量的60%，范围在20%到90%之间。调整算法，通过一个单一的接受窗口参数，允许调整算法的性能，在人口对运动或减少噪音的保护，使用户能够将他们的最佳定义。在被认为适合隔离的图像群体中，非选通图像、最佳箱和选通图像的平均病变SUV最大值分别为7.9、8.5和9.0。最佳箱图像和门控图像之间的FWHM测量的Pearson相关性优于非门控图像，分别为0.89和0.85。一般来说，最佳箱图像有更好的分辨率比nongated图像和更好的噪声特性比门控images.Discussion：我们扩展了最佳箱的概念，以数据驱动的形式，更新传统的一个尺寸适合所有的方法，支持自适应成像的共形。这种自动化策略在大量人群中很容易实现，并将运动信息封装在易于使用的3D图像中。它的简单性和实用性可能使这种方法或类似的方法非常适合在临床环境中使用。(C)2017年美国医学物理学家协会
Purpose: Respiratory gating has been used in PET imaging to reduce the amount of image blurring caused by patient motion. Optimal binning is an approach for using the motion-characterized data by binning it into a single, easy to understand/use, optimal bin. To date, optimal binning protocols have utilized externally driven motion characterization strategies that have been tuned with population-derived assumptions and parameters. In this work, we are proposing a new strategy with which to characterize motion directly from a patient's gated scan, and use that signal to create a patient/instance-specific optimal bin image.Methods: Two hundred and nineteen phase-gated FDG PET scans, acquired using data-driven gating as described previously, were used as the input for this study. For each scan, a phase-amplitude motion characterization was generated and normalized using principle component analysis. A patient-specific optimal bin window was derived using this characterization, via methods that mirror traditional optimal window binning strategies. The resulting optimal bin images were validated by correlating quantitative and qualitative measurements in the population of PET scans.Results: In 53% (n = 115) of the image population, the optimal bin was determined to include 100% of the image statistics. In the remaining images, the optimal binning windows averaged 60% of the statistics and ranged between 20% and 90%. Tuning the algorithm, through a single acceptance window parameter, allowed for adjustments of the algorithm's performance in the population toward conservation of motion or reduced noise-enabling users to incorporate their definition of optimal. In the population of images that were deemed appropriate for segregation, average lesion SUV max were 7.9, 8.5, and 9.0 for nongated images, optimal bin, and gated images, respectively. The Pearson correlation of FWHM measurements between optimal bin images and gated images were better than with nongated images, 0.89 and 0.85, respectively. Generally, optimal bin images had better resolution than the nongated images and better noise characteristics than the gated images.Discussion: We extended the concept of optimal binning to a data-driven form, updating a traditionally one-size-fits-all approach to a conformal one that supports adaptive imaging. This automated strategy was implemented easily within a large population and encapsulated motion information in an easy to use 3D image. Its simplicity and practicality may make this, or similar approaches ideal for use in clinical settings. (C) 2017 American Association of Physicists in Medicine