Multi-Center Academic-Industrial Partnership for Personalized Al-Enabled High Count PET

摘要 高图像噪声降低了PET的诊断功效和定量准确性，因为噪声可以容易地 导致SUV的高估，并导致诊断中的假阳性病变检测。高图像噪声也 降低了临床决策的信心，导致通过其他途径进行额外的不必要的随访 成像方式和侵入性操作。基于深度学习的降噪技术已显示出PET的前景 显像然而，现有的方法仅关注于将低计数图像（例如，通过低计数图像获取的图像）转换为低计数图像。 剂量注射或更短的扫描时间）到典型临床扫描中的标准计数图像。对于两个低- 计数和绝大多数常规采集的具有正常剂量和扫描时间的临床PET图像， 没有将这种临床图像转换为高计数图像以进一步降低图像噪声的方法，主要是由于 获得高计数PET图像作为训练标签的挑战。现实世界中的另一个挑战 应用是在噪声水平，噪声结构， 这样的匹配在多中心多成像中是特别具有挑战性的。 扫描仪设置。在这个学术与工业合作伙伴关系R 01项目中，我们与以下人员建立了理想的合作伙伴关系 Visage Imaging是一家领先的PACS公司，与三个领先的学术中心（耶鲁大学、MGH、加州大学戴维斯分校）合作， 开发、评估、部署和翻译强大的深度学习方法，以生成虚拟高计数PET 通过考虑每个患者的每个器官的噪声水平，以高度个性化的方式获得图像， 以及相关的非成像患者信息。学术网站可以访问大量的高- 通过长时间动态扫描（至少90分钟）或超灵敏的 长轴向视野（FOV）Explorer扫描仪。开发的产品将是深度学习网络， 可以转换来自所有主要供应商（Siemens、GE、United Imaging Healthcare）的任何临床PET图像数据 （UIH））转换为虚拟高计数超低噪声图像。由于耶鲁大学，MGH和加州大学戴维斯分校都由Visage提供服务， 成像，开发的深度学习技术可以无缝转换到Visage PACS中 用于验证和评估、beta测试和用户反馈以及最终翻译的研究/临床服务器 和监管文件。在目标1中，我们将开发用于虚拟高计数PET生成的深度学习模型。在 目标2，我们将评估和部署模型到Visage research PACS服务器，并评估虚拟高计数 临床环境中的PET。在目标3中，我们将整合开发的虚拟高计数PET深度学习 模型到临床生产PACS服务器中，并生成监管文档和支持数据， FDA 510（k）。

Abstract High image noise degrades the diagnostic efficacy and quantitative accuracy of PET, as noise could easily results in overestimation of SUV and cause false positive lesion detections in diagnosis. High image noise also decreases the confidence of clinical decision making, leading to additional unnecessary follow-ups through other imaging modalities and invasive procedure. Deep learning-based noise reduction has shown promises for PET imaging. However, existing approaches only focus on converting low-count image (e.g. acquired through low- dose injection or shorter scan time) to standard-count image in typical clinical scans. However, for both low- count and the vast majority of routinely acquired clinical PET images with normal dose and scan time, there is no approach to convert such clinical images to high-count images to further reduce the image noise, mainly due to the challenge of obtaining high-count PET images as training labels. Another challenge in the real-world application is to match the training data with the testing data, in terms of noise level, noise structure, reconstruction parameters, scanner model, etc. Such matching is particularly challenging in a multi-center multi- scanner setting. In this Academic-Industrial Partnership R01 project, we formed an ideal partnership between Visage Imaging, a leading PACS company, and three leading academic centers (Yale, MGH, UC Davis) to develop, evaluate, deploy, and translate robust deep learning methods to generate virtual-high-count PET images in a highly personalized manner by taking into account the noise level of each organ in each patient, as well as associated non-imaging patient information. The academic sites have access to a large number of high- count data that are acquired either through long dynamic scans (at least 90 minutes) or by the ultra-sensitive long axial field-of-view (FOV) Explorer scanner. The developed product would be deep learning networks that can convert any clinical PET images data from all major vendors (Siemens, GE, United Imaging Healthcare (UIH)) into virtual-high-count ultra-low noise images. Since Yale, MGH, and UC Davis are all serviced by Visage Imaging, the developed deep learning technique can be seamlessly translated into Visage PACS research/clinical servers for validation and evaluation, beta testing and user feedback, and ultimate translation and regulatory filings. In Aim 1, we will develop deep learning models for virtual-high-count PET generation. In Aim 2, we will evaluate and deploy the models into Visage research PACS server and evaluate virtual-high-count PET in clinical environments. In Aim 3, we will integrate the developed virtual-high-count PET deep learning models into the clinical production PACS server and generate regulatory documents and supporting data for FDA 510(k).