Brain phantom generation by generative adversarial net (GAN) for AI-based emission tomography

项目摘要 正电子发射断层扫描(PET)和单光子发射计算机断层扫描(SPECT)是有用的 功能医学成像技术，可用于评估脑功能，如局部 脑血流和神经传递。PET重建的空间分辨率通常为3-6 mm， 而SPECT仅为1-2 cm。受人工智能(AI)/机器学习最新进展的推动 (ML)及其在MRI和CT上的成功应用，开发一个基于ML的系统是非常可取的 PET/SPECT脑图像重建(我们特别感兴趣的之一是帕金森病)实现更高的 分辨率和比使用传统方法更低的噪声。但是，要开发这样一个学习系统， 指导训练的地面真实数据(准确的图像，用作标签)无法从真实中获得 世界。已发表的用于PET成像的ML系统使用来自传统方法的重建图像作为 指导培训的标签。因此，目标只是为了提高重建速度，而不是 而不是提高图像质量。由于ML系统重建的图像质量不能超过 在引导图像方面，最大似然系统的性能不能超过常规方法。因此，在这个项目中， 我们提出了一个为期两年的项目，该项目将使用条件生成对抗网络(GAN)来 生成数字2-D人脑幻影，它将与真实的人脑高度相似。这个 生成的模型将作为开发基于ML的PET/SPECT的(精确)地面真实数据 重建系统(我们未来的研究)。生成的幻影将包含活动图像和 衰减贴图。因此，这项工作的结果可以用于模拟脑PET或SPECT检查 对于各种神经障碍，神经网络可以用已知的地面真理进行训练。此外, 设计ML系统往往依赖于大量的数据，但要从大量的数据中访问数据并不容易 用于特定医学研究的美国患者(为计算机视觉和图像开发的成熟ML系统 分类通常涉及用于训练的百万级别的图像)。现有的ML系统是为MRI、CT、 而PET成像通常只使用几十个患者数据进行训练，而要验证的数据甚至更少。 因此，这些系统很可能与训练中使用的数据过度匹配。利用发电系统 从这个项目中提出，我们可以产生一个大的幻影种群，以避免过度匹配的问题 在设计人工智能图像重建系统时。一旦GaN系统成功开发，它就可以 轻松移植到基于AI的CT和基于AI的MRI的幻影生成。该方法还具有潜在的 可扩展以生成用于模拟心脏成像的躯干、腹部和四肢的幻影群体， 肿瘤成像等。

Project Summary Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are useful functional medical imaging techniques that can be performed to evaluate brain functions such as regional cerebral perfusion and neurotransmission. The spatial resolution of reconstruction for PET is usually 3-6 mm, and for SPECT is only 1-2 cm. Motivated by the latest advances in artificial intelligence (AI)/machine learning (ML) and its successful application to MRI and CT, it is highly desirable to develop an ML-based system for PET/SPECT cerebral image reconstruction (one of our specific interests is Parkinson disease) to achieve higher resolution and lower noise than using conventional approaches. However, to develop such a learning system, ground-truth data (accurate images, used as the labels) that guide the training are unavailable from the the real world. Published ML systems for PET imaging have used reconstructed images from conventional methods as the label to guide the training. As a result, the goal was only targeted to improve reconstruction speed, rather than improving the image quality. Since the quality of reconstructed image by ML system cannot exceed the guiding images, the performance of ML system cannot surpass conventional methods. Therefore, in this project, we propose a two-year project that will use conditional generative adversarial networks (GAN) to generate digital 2-D human brain phantoms, which will be highly similar to real human brains. The generated phantoms will serve as the (precise) ground-truth data to develop ML-based PET/SPECT reconstruction systems (our future research). The generated phantoms will contain an activity image and an attenuation map. Hence, results from this work can be used for simulating brain PET or SPECT examinations for various neurological disorders, and neural network can be trained with known ground truth. In addition, designing ML systems often relies on large amounts of data, but it is not easy to access data from a large number of patients in the US for specific medical research (mature ML systems developed for computer vision and image classification often involve images on the million level for training). Existing ML systems developed for MRI, CT, and PET imaging often merely uses a few tens of patient data for training and even less data to validate. Therefore, those systems are high-likely overfitted to the data used in training. With the generation system proposed from this project, we can produce a large phantom population to avoid the overfitting problem when design the AI image-reconstruction system. Once the GAN system is successfully developed, it can be easily transplanted to phantom generation for the AI-based CT and AI-based MRI. The method is also potentially extendable to generate phantom populations of torso, abdomen, and extremities for simulating cardiac imaging, tumor imaging, etc.