King's College London

Cian Scannell

Amedeo Chiribiri

Examples of the present disclosure include a method and system for deriving a computer implemented trained artificial intelligence (AI) model that is capable of predicting arterial input function (AIF) from blood and myocardial signal intensity curves of a subject obtained using a magnetic resonance (MR) scanner during injection of a single high dosage bolus of contrast agent that would otherwise cause signal saturation. The method and system requires an input data set of a large number of prior obtained dual bolus or dual sequence sets of scan measurements to be used as training data for the AI model. Once the computer implemented trained AI model has been obtained, it is deployed in a further method and system which receives MR data for a particular subject for which myocardial perfusion parameters such as the AIF is to found, the MR data being obtained from an MR scan taken during injection of a single bolus of contrast agent at a concentration sufficient to give MR signal saturation. The trained AI model is then able to predict, from the single bolus saturated MR data, myocardial perfusion parameters such as the AIF for the subject. The trained AI model is also able to convert between dual bolus datasets and duals sequence datasets.

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London, United Kingdom

Cian Scannell