Gadgetron Global Network and Artificial Intelligence Powered Cardiac Imaging

过去的一年，人工智能应用和数据管理取得了新的发展。已经开发了以下应用程序并将其部署到临床扫描仪： 。基于人工智能的分割和报告心脏灌注图 。基于人工智能轮廓的心脏电影成像，用于回溯门控和自由呼吸成像 。基于人工智能的电影菌株定位与报告 。浅层神经网络在缺血性疾病分类中的应用及磁共振扫描报告 。基于深度神经网络的图像增强 。基于深度学习的MR重建 更多的应用程序正在开发中： 。来自MRI的肺囊肿检测和自动报告 。使用人工智能从电影成像中定量和报告心功能 。快速心脏磁共振成像和报告与自由呼吸数据采集 -每小时5名患者，用于非对比CMR研究，使用人工智能完整报告功能参数 -每小时3名患者，用于对比研究，完整报告功能参数、血流灌注图和LGE结果 -人工智能支持的CMR产生的价值的临床证据收集在人工智能的帮助下，是否有可能将CMR患者的吞吐量翻一番，包括更快的成像和更快的报告？ 。基于自由呼吸脂肪水磁共振成像的AI驱动的身体和器官脂肪定量 除了开发更多的人工智能应用程序外，未来的工作应该更多地关注人工智能战略、基础设施和临床价值的证据： 。AI战略：如何开发一个系统来持续生产AI应用程序？除了出版外，如何确保开发的应用程序具有临床价值？如何确保临床合作者的参与，以指导AI的发展？ 。基础设施：支持管理具有不同来源/类型/格式的大量数据，支持管理有标签的数据，支持反复培训/测试/部署，以确保合规道德并促进国际协作 。证据：收集证据的方法和工具，以证明人工智能应用程序作为临床工作流程的一部分确实产生了价值，例如更高的成像效率(例如，每天为更多的患者成像)和更好的或新的诊断。我们想要证明人工智能不仅仅是花哨的研究。

Past year saw new development in AI applications and data curation. Following applications had been developed and deployed to clinical scanners: . Cardiac perfusion mapping with AI based segmentation and reporting . Cardiac cine imaging with AI based contouring, for retro-gated and free-breathing imaging . AI based cine strain mapping and reporting . Ischemic disease classification using shallow neural nets and reporting on MR scanner . Deep neural net based image enhancement . Deep learning based MR reconstruction More applications are under development: . Lung Cyst detection and auto reporting from MRI . Cardiac function quantification and reporting from cine imaging using AI . Fast cardiac MR imaging and reporting with free-breathing data acquisition - 5 patients per hour protocols for non-contrast CMR study, with full reporting for functional parameters using AI - 3 patients per hour protocols for contrast study, with full reporting of functional parameters, perfusion flow mapping and LGE findings - Clinical evidences collection for values generated by AI powered CMR Is it possible to double the CMR patient throughput with the help of AI, including both faster imaging and faster reporting? . AI powered body and organ fat quantification from free-breathing fat-water MRI Besides developing more AI applications, future work should focus more on AI strategy, infrastructure and evidences for clinical value: . AI strategy: how to develop a system to keep producing AI applications? How to ensure the developed applications have clinical value, besides making publications? How to ensure the involvement of clinical collaborators to guide AI development? . Infrastructure: to support curation of significant amount of data with different sources/types/formats, to support curation of labelled data, to support iterative training/testing/deployment, to ensure ethical compliance and enable international collaboration . Evidences: methods and tools to collect proof that AI applications actually generate values as a part of clinical workflow, e.g. better imaging efficiency (e.g. imaging more patients per day) and better or novel diagnosis. We want to show AI is more than just fancy research.