Machine Learning in PET: From Photon Detection to Quantitative Image Reconstruction
Machine Learning in PET: From Photon Detection to Quantitative Image Reconstruction
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DOI：
10.1109/jproc.2019.2936809
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发表时间：
2020-01-01
影响因子：
20.6
通讯作者：
Qi, Jinyi
中科院分区：
文献类型：
--
作者：
Gong, Kuang;Berg, Eric;Qi, Jinyi
机器学习在核医学中有着独特的应用，从光子检测到定量图像重建。尽管在用于飞行时间正电子发射断层扫描（PET）的探测器开发方面已经有了令人印象深刻的进步，但是大多数探测器仍然利用简单的信号处理方法来从探测器信号中提取时间和位置信息。现在，随着快速波形数字化仪的出现，机器学习技术已被应用于估计高能光子的位置和到达时间。在定量图像重建中，机器学习已被用于估计各种校正因子，包括散射事件和衰减图像，以及减少重建图像中的统计噪声。在这里，机器学习要么为现有的耗时计算提供更快的替代方案，例如在散射估计的情况下，要么创建数据驱动的方法来映射隐式定义的函数，例如在估计PET/MR扫描的衰减图的情况下。在本文中，我们将回顾上述机器学习在核医学中的应用。
Machine learning has found unique applications in nuclear medicine from photon detection to quantitative image reconstruction. Although there have been impressive strides in detector development for time-of-flight positron emission tomography (PET), most detectors still make use of simple signal processing methods to extract the time and position information from the detector signals. Now, with the availability of fast waveform digitizers, machine learning techniques have been applied to estimate the position and arrival time of high-energy photons. In quantitative image reconstruction, machine learning has been used to estimate various corrections factors, including scattered events and attenuation images, as well as to reduce statistical noise in reconstructed images. Here, machine learning either provides a faster alternative to an existing time-consuming computation, such as in the case of scatter estimation, or creates a data-driven approach to map an implicitly defined function, such as in the case of estimating the attenuation map for PET/MR scans. In this article, we will review the above-mentioned applications of machine learning in nuclear medicine.