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

National Heart, Lung, And Blood Institute

Investigators

Linked publications, trials & patents

Abstract

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/