Title: Autonomous cardiac MR Acquisition
Funder: Department of Health and Social Care

Abstract: Background Cardiac magnetic resonance (CMR) is the gold standard for the diagnosis and evaluation of many diseases, but its long acquisition time makes it a time-consuming and expensive test that is under-utilised in the UK. Current practice is for a radiographer to prescribe successive imaging planes through which the heart is scanned, but this introduces imprecision and further limits CMR to large centres with relevant expertise. Aims and Objective We aim to build on our previous work on deep learning and of developing rapid CMR to automate the scanning process, halving acquisition time, removing the need for specialist radiographers, democratising the technology, and increasing precision. Project plan and methods used We will develop a deep learning algorithm to automate the cardiac MR slice prescription process and implement it directly on the scanner. A scan-rescan dataset will be created to quantify the algorithm s reproducibility and measure its performance against a cardiac radiographer. Timelines for delivery This is a 12 month project proposal with training data collection taking three months, six months for algorithm development and deployment, and the final three months spent evaluating it in a clinical setting. Anticipated impact and dissemination We are confident that we can halve scanning time, reducing the cost of the investigation as well as waiting lists and lowering barriers to use. Precision will be increased, reducing the need for unnecessary follow-up and repeat tests. Early dissemination will be through collaborating institutions within the NHS and the algorithm will be implemented directly on their scanners.

Lay abstract: Images of the heart (e.g. from MRI, CT, or ultrasound) are key to clinical decision-making in cardiology. It is vital that these images are appropriately interpreted so that the correct clinical decision is made. Cardiac MRI is the best method of measuring heart structure and function. But it is a scarce resource in the UK due to the expense of the investigation, the length of time that it takes and the need for a specialist cardiac radiographer. Furthermore, variability in the way radiographers acquire images introduces errors that can have knock-on effects for clinical decision making. We aim to solve this problem using artificial intelligence to automate cardiac MR scanning. Fully automated acquisition will speed up the scanning process, reducing waiting times, saving money, freeing up a valuable resource and increasing access to a leading technology. The scans will also be more consistent, allowing clinicians to make the right decisions in the right patients and instil confidence to the results, reducing unnecessary follow-ups. We are in a unique position to deliver this project. We have the means to interact with the MR scanner. Bart s is Europe's highest volume cardiac MR centre so we have unprecedented access to consented patient data. We are a team of clinicians and technicians who share the goal of delivering the best technology directly into clinical care. Our AI approach is explainable and can explain why it has made the decisions that it has. The algorithms will be independently validated and have continued quality control, fulfilling ethical and regulatory frameworks so doctors and patients can trust them.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CARDIOVASCULAR