Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging.
Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging.
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DOI：
10.3390/diagnostics12081945
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发表时间：
2022-08-12
期刊：
影响因子：
3.6
通讯作者：
Du, Yong
中科院分区：
文献类型：
--
作者：
Shao, Wenyi;Leung, Kevin H.;Xu, Jingyan;Coughlin, Jennifer M.;Pomper, Martin G.;Du, Yong
虽然机器学习（ML）方法可以显着提高用于诊断和监测帕金森病（PD）的SPECT成像的图像质量，但它们需要大量数据进行训练。通常很难收集大量患者数据来支持ML研究，并且病变的基础事实也是未知的。本文利用生成对抗网络（GAN）生成数字脑模型，用于训练基于ML的PD SPECT算法。共审查了155名患者（113名男性和42名女性）的594个PET 3D脑模型，并选择了1597个包含全部或部分纹状体的2D切片。根据这些图像还生成了相应的衰减图。然后使用这些数据开发GAN以生成2D脑体模，其中每个体模由放射性图像和相应的衰减图组成。统计方法，包括直方图，Fréchet距离，和结构相似性被用来评估基于10，000生成体模的发生器。当生成的体模和训练数据集都被传递到训练器时，获得了相似的正态分布，这表明训练器无法区分生成的体模和训练数据集。生成的数字体模可用于2D SPECT仿真，并作为地面实况开发基于ML的重建算法。从这项工作中积累的经验也奠定了基础，建立一个3D GAN相同的应用。
While machine learning (ML) methods may significantly improve image quality for SPECT imaging for the diagnosis and monitoring of Parkinson’s disease (PD), they require a large amount of data for training. It is often difficult to collect a large population of patient data to support the ML research, and the ground truth of lesion is also unknown. This paper leverages a generative adversarial network (GAN) to generate digital brain phantoms for training ML-based PD SPECT algorithms. A total of 594 PET 3D brain models from 155 patients (113 male and 42 female) were reviewed and 1597 2D slices containing the full or a portion of the striatum were selected. Corresponding attenuation maps were also generated based on these images. The data were then used to develop a GAN for generating 2D brain phantoms, where each phantom consisted of a radioactivity image and the corresponding attenuation map. Statistical methods including histogram, Fréchet distance, and structural similarity were used to evaluate the generator based on 10,000 generated phantoms. When the generated phantoms and training dataset were both passed to the discriminator, similar normal distributions were obtained, which indicated the discriminator was unable to distinguish the generated phantoms from the training datasets. The generated digital phantoms can be used for 2D SPECT simulation and serve as the ground truth to develop ML-based reconstruction algorithms. The cumulated experience from this work also laid the foundation for building a 3D GAN for the same application.
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