The past FY of 2018-2019 saw significant advances in Gadgetron for AI application development. The number of deployed sites and number of patients scanned with Gadgetron were doubled, achieving 40 sites globally and 80K patients annual. Lots of publications had been generated using Gadgetron and I listed a few with this report. More are either under review and submitted. More importantly, Gadgetron is developed into an effective infrastructure to support future AI R&D and clinical trials. Among all deployed sites, significant (100 patients per day) amount of data were sent back to NIH. We curated these huge datasets and used them to develop new AI imaging technique. This demonstrated a new pattern which does not require to pay every site, but provide best imaging and processing service, in exchange of high mutual trust and huge buy-in and largest CMR datasets ever accumulated in the world (to my best knowledge). Next FY is important and in my opinion a key year, since the AI imaging is taking off with noticeable momentum around the world, including CMR. Given what we have achieved, I propose following new targets: . 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. To achieve the goals, and to not miss this big wave of AI powered imaging, may I propose to increase resource for Gadgetron in the incoming FY? In specific, a) Hire a top software system developer. I am very good at imaging application development and numeric algorithms, but I feel the team will benefit a lot more if we can hire a good software developer focusing on feedback system, patient record interface and cloud computing. The expected expertise will be on software system, inter system communication, UI design and development etc, which mainly are for non-numeric software components. b) Establish a CAN for Gadgetron to formalize it as a long-term project, which I will lead. The basis for this is 1) Gadgetron is supporting NHLBI research scanner and NIH Radiology clinical scanners on daily basis; 2) Gadgetron is heavily used world-wide to scan real patients every day; 3) Resulting customer supporting is ever increasing to a level that I can barely keep up. To maintain top customer satisfaction (which is essential for us to collect data from deployed sites and do AI R&D) and still focus on developing new features and conducting new research, I feel it is a good time to formalize Gadgetron, including its technical development and customer support. Please may give me some feedback about this proposal and thank you very much!

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Brown, Louise A E; Onciul, Sebastian C; Broadbent, David A et al. (2018) Fully automated, inline quantification of myocardial blood flow with cardiovascular magnetic resonance: repeatability of measurements in healthy subjects. J Cardiovasc Magn Reson 20:48
Merlocco, Anthony; Olivieri, Laura; Kellman, Peter et al. (2018) Improved Workflow for Quantification of Right Ventricular Volumes Using Free-Breathing Motion Corrected Cine Imaging. Pediatr Cardiol :
Campbell-Washburn, Adrienne E; Rogers, Toby; Stine, Annette M et al. (2018) Right heart catheterization using metallic guidewires and low SAR cardiovascular magnetic resonance fluoroscopy at 1.5 Tesla: first in human experience. J Cardiovasc Magn Reson 20:41
Campbell-Washburn, Adrienne E; Tavallaei, Mohammad A; Pop, Mihaela et al. (2017) Real-time MRI guidance of cardiac interventions. J Magn Reson Imaging 46:935-950
Inati, Souheil J; Naegele, Joseph D; Zwart, Nicholas R et al. (2017) ISMRM Raw data format: A proposed standard for MRI raw datasets. Magn Reson Med 77:411-421
Campbell-Washburn, Adrienne E; Xue, Hui; Lederman, Robert J et al. (2016) Real-time distortion correction of spiral and echo planar images using the gradient system impulse response function. Magn Reson Med 75:2278-85
Olivieri, Laura; Cross, Russell; O'Brien, Kendall J et al. (2016) Free-breathing motion-corrected late-gadolinium-enhancement imaging improves image quality in children. Pediatr Radiol 46:983-90
Cross, Russell; Olivieri, Laura; O'Brien, Kendall et al. (2016) Improved workflow for quantification of left ventricular volumes and mass using free-breathing motion corrected cine imaging. J Cardiovasc Magn Reson 18:10
Olivieri, Laura J; Cross, Russell R; O'Brien, Kendall E et al. (2015) Optimized protocols for cardiac magnetic resonance imaging in patients with thoracic metallic implants. Pediatr Radiol 45:1455-64
Campbell-Washburn, Adrienne E; Rogers, Toby; Basar, Burcu et al. (2015) Positive contrast spiral imaging for visualization of commercial nitinol guidewires with reduced heating. J Cardiovasc Magn Reson 17:114

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