Gadgetron Global Network and Artificial Intelligence Powered Cardiac Imaging

去年，人工智能应用和数据管理有了新的发展。以下应用程序已开发并部署到临床扫描仪： .心脏灌注标测，基于AI的分割和报告 .心脏电影成像，基于AI的轮廓，用于后门控和自由呼吸成像 .基于AI的电影应变映射和报告 .基于浅层神经网络的缺血性疾病分类及其在MR扫描仪上的报告 .基于深度神经网络的图像增强 .基于深度学习的MR重建 更多应用正在开发中： .肺囊肿检测和MRI自动报告 .使用AI从电影成像进行心脏功能量化和报告 .快速心脏MR成像和自由呼吸数据采集报告 - 非造影CMR研究的每小时5名患者方案，使用AI完整报告功能参数 - 每小时3名患者进行造影研究，完整报告功能参数、灌注流图和LGE结果 - 人工智能驱动的CMR生成值的临床证据收集人工智能的帮助下，是否有可能使CMR患者吞吐量翻倍，包括更快的成像和更快的报告？ .来自自由呼吸脂肪-水MRI的AI动力身体和器官脂肪量化 除了开发更多的人工智能应用程序，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？如何确保开发的应用程序具有临床价值，除了出版物？如何确保临床合作者的参与来指导AI开发？ .基础设施：支持管理具有不同来源/类型/格式的大量数据，支持管理标记数据，支持迭代培训/测试/部署，确保道德合规并实现国际合作 .证据：收集证据的方法和工具，证明人工智能应用程序实际上作为临床工作流程的一部分产生价值，例如更好的成像效率（例如每天成像更多的患者）和更好或新颖的诊断。我们想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.