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

摘要 高图像噪声降低了正电子发射计算机断层扫描的诊断效能和定量准确性，因为噪声很容易 结果高估了SUV，导致诊断时出现假阳性病变。图像噪声也很高 降低临床决策的信心，导致通过其他方式进行额外的不必要的跟进 成像方式和侵入性程序。基于深度学习的降噪技术在PET中显示出良好的前景 成像。然而，现有的方法只专注于转换低计数图像(例如，通过低计数图像获取的图像)。 剂量注射或更短的扫描时间)在典型的临床扫描中转换为标准计数图像。然而，对于这两个低- 计数和绝大多数常规获取的临床PET图像在正常剂量和扫描时间下，有 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 获得高计数的PET图像作为训练标签的挑战。现实世界中的另一个挑战 应用是将训练数据与测试数据在噪声水平、噪声结构、 重建参数、扫描仪模型等。这种匹配在多中心、多中心和多中心的情况下尤其具有挑战性。 扫描仪设置。在这个学术-产业伙伴关系R01项目中，我们形成了理想的合作伙伴关系 和三个领先的学术中心(耶鲁大学、麻省理工学院、加州大学戴维斯分校) 开发、评估、部署和转换稳健的深度学习方法，以生成虚拟高计数PET 通过考虑每个患者每个器官的噪音水平，以高度个性化的方式进行图像处理，如 以及相关的非成像患者信息。这些学术网站可以访问大量的高级- 统计通过长时间动态扫描(至少90分钟)或通过超敏感的 长轴向视野(FOV)探索者扫描仪。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(西门子、GE、联合成像医疗保健公司)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。因为耶鲁大学、MGH大学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地转换为Visage PACS 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 以及监管备案文件。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……里面 目的2.评估模型并将其部署到Visage Research PACS服务器上，并对虚拟高计数进行评估 临床环境中的宠物。在目标3中，我们将集成开发的虚拟高计数PET深度学习 模型到临床生产PACS服务器中，并生成法规文档和支持数据 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).