Gadgetron Global Network and Intelligence Computing: Clinical Imaging Application Development and Software Infrastructure

过去的2019-2020财年，Gadgetron AI研发取得了成功。更多的AI应用程序被开发并部署到临床实践中。收集了5万名患者的数据并储存在NHLBI。Gadgetron AI出现在国际新闻中，我们在临床成像AI方面的研究发表在顶级期刊上。我在这篇报道后列举了几个。 .开发人工智能反馈和患者历史界面软件，使Gadgetron成为一个独特的平台来管理成像数据，患者记录和临床反馈。有了这三个关键要素，我们计划进入疾病诊断和自动分析领域（例如预测心脏结局和分类患者是否应该接受干预手术）。 .开发完整的AI驱动CMR分析解决方案，并将其部署到医院用于日常使用，包括电影，LGE，灌注，T1/T2/T2* 映射，脂肪水成像等。这里的动机是完全自动化地提取患者特定的成像信息。这些信息将与患者病史和队列训练的疾病模型一起使用。 .开发用于重大心脏病的MR扫描仪的精确成像。目标是开发患者特定模型，以预测a）患者是否应接受介入手术; B）患者将来是否会发生心脏事件。实现这些的技术路线是：1）自由呼吸CMR成像; 2）AI衍生的成像信息和生物标志物; 3）Gadgetron接收的患者病史和记录; 4）使用队列模型进行预测，其中包含步骤1-3的信息。 选定出版物清单： 卷积神经网络在心脏磁共振成像中的界标检测。Hui Xue，Jessica Artico，Marianna丰塔纳，James C Moon，Rhodri H Davies，Peter Kellman. arXiv：2008.06142 eess.IV（正在审查）。 使用深度学习对心肌灌注MRI进行自动在线分析。Hui Xue，Rhodri Davies，Louis AE Brown，Kristopher D Knott，Tushar Kotecha，Marianna丰塔纳，Sven Plein，James C Moon，Peter Kellman.放射学：人工智能（出版中）。arXiv：1911.00625 q-bio. QM。 COVID-19：幸存者的心肌损伤。丹尼尔S. Knight、Tushar Kotecha、Yousuf Razvi、Liza Chacko、James T.布朗，Paramjit S.放大图片创作者：James Goldring，Michael Jacobs，Lucy E.放大图片作者：James C. Moon、Hui Xue、Peter Kellman、Niket Patel和Marianna丰塔纳。发行量，https://doi.org/10.1161/CIRCULATIONAHA.120.049252 使用深度学习在动脉输入功能图像中自动检测左心室，以进行在线灌注映射：一项针对15，000名患者的研究。Hui Xue，Ethan Tseng，Kristopher D Knott，Tushar Kotecha，Louise Brown，Sven Plein，Marianna丰塔纳，James C Moon，Peter Kellman.磁共振医学，第84卷，第5期，2020年11月，第2788-2800页。 定量心肌灌注的预后意义：基于人工智能的灌注成像方法。Kristopher D Knott，Andreas Seraphim，Joao B Augusto，Hui Xue，Liza Chacko，Nay Aung，Steffen E Petersen，Jackie A库珀，夏洛特曼尼斯蒂，Anish N Bhuva，Tushar Kotecha，Christoph V Bourantas，Rhodri H Davies，Louise AE Brown，Sven Plein，Marianna丰塔纳，Peter Kellman，James C Moon.流通2020;141：12821291. 媒体报道： https://www.newscientist.com/article/2224403-an-ai-doctor-is-analysing-heart-scans-in-dozens-of-hospitals/#ixzz6VfVSiMrH https://www.beckershospitalreview.com/artificial-intelligence/ai-measures-blood-flow-in-real-time-predicting-heart-attack-and-stroke-study-finds.html https://time.com/5784090/ai-heart-attack-stroke/

The past FY of 2019-2020 was successful for Gadgetron AI R&D. More AI applications were developed and deployed to clinical practice. Data from 50K patients were collected and stored at NHLBI. Gadgetron AI was on international news and our research in clinical imaging AI was published at top journals. I listed a few after this report. . Develop AI feedback and patient history interface software, so Gadgetron will be an unique platform to curate imaging data, patient record and clinical feedback. With these three key ingredients, we plan to move into disease diagnosis and automated analysis fields (e.g. to predict cardiac outcome and classify whether a patient should receive intervention procedure). . Develop complete AI powered CMR analysis solution and deploy them to hospitals for daily usage, including cine, LGE, perfusion, T1/T2/T2* mapping, fat water imaging etc. The motivation here is to extract patient specific imaging information with full automation. These info will be used with patient history and cohort trained disease model. . Develop precision imaging on MR scanner for major cardiac disease. The target is to develop a patient specific model to predict a) whether a patient should receive intervention surgery or not; b) whether a patient will have cardiac events down the road. The technical route to achieve these are: 1) free-breathing CMR imaging; 2) AI derived imaging information and biomarkers; 3) Patient history and record received in Gadgetron; 4) Make prediction using cohort model with info from step 1-3. List of selected publications: Landmark detection in Cardiac Magnetic Resonance Imaging Using A Convolutional Neural Network. Hui Xue, Jessica Artico, Marianna Fontana, James C Moon, Rhodri H Davies, Peter Kellman. arXiv:2008.06142 eess.IV (under review). Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning. Hui Xue, Rhodri Davies, Louis AE Brown, Kristopher D Knott, Tushar Kotecha, Marianna Fontana, Sven Plein, James C Moon, Peter Kellman. Radiology: Artificial Intelligence (In Press). arXiv:1911.00625 q-bio.QM. COVID-19: Myocardial injury in survivors. Daniel S. Knight , Tushar Kotecha , Yousuf Razvi , Liza Chacko , James T. Brown , Paramjit S. Jeetley , James Goldring , Michael Jacobs , Lucy E. Lamb , Rupert Negus , Anthony Wolff , James C. Moon , Hui Xue , Peter Kellman , Niket Patel , and Marianna Fontana. Circulation, https://doi.org/10.1161/CIRCULATIONAHA.120.049252 Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients. Hui Xue, Ethan Tseng, Kristopher D Knott, Tushar Kotecha, Louise Brown, Sven Plein, Marianna Fontana, James C Moon, Peter Kellman. Magnetic Resonance in Medicine, Volume84, Issue 5, November 2020, Pages 2788-2800. The prognostic significance of quantitative myocardial perfusion: an artificial intelligencebased approach using perfusion mapping. Kristopher D Knott, Andreas Seraphim, Joao B Augusto, Hui Xue, Liza Chacko, Nay Aung, Steffen E Petersen, Jackie A Cooper, Charlotte Manisty, Anish N Bhuva, Tushar Kotecha, Christos V Bourantas, Rhodri H Davies, Louise AE Brown, Sven Plein, Marianna Fontana, Peter Kellman, James C Moon. Circulation. 2020;141:12821291. Media press: https://www.newscientist.com/article/2224403-an-ai-doctor-is-analysing-heart-scans-in-dozens-of-hospitals/#ixzz6VfVSiMrH https://www.beckershospitalreview.com/artificial-intelligence/ai-measures-blood-flow-in-real-time-predicting-heart-attack-and-stroke-study-finds.html https://time.com/5784090/ai-heart-attack-stroke/