PROBABILISTIC HR PREDICTION FOR COMPUTED TOMOGRAPHY

Abstract: methods and systems are provided to predict an upper threshold and a lower threshold of a heart rate (hr) of a patient of a computed tomography (ct) imaging system using a machine learning (ml) model, based on hr time series data collected over a duration prior to a cardiac scan. the ml model takes as an additional input a desired probabilistic certainty (e.g., statistical confidence level) that the predicted upper and lower hr thresholds will be accurate, provided by an operator of the ct imaging system. before the start of the imaging scan, the predicted upper and lower hr thresholds are used to set the start exposure and end exposure times, to ensure that the requested cardiac phases are acquired.

Inventor(s): John Londt, Arka Datta, Darin Okerlund

CPC Classification: G16H30/20 (for handling medical images, e.g. DICOM, HL7 or PACS)

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