Training a machine learning model for simulating images at higher dose of contrast agent in medical imaging applications

Abstract

A solution is proposed for training a machine learning model () for use in medical imaging applications. A corresponding method () comprises providing (--) sample sets, each comprising a sample baseline image, a sample target image (acquired from a corresponding body-part of a subject to which a contrast agent at a certain dose has been administered) and a sample source dose (corresponding to a different dose of the contrast agent). The machine learning model () is trained (-) so as to optimize its capability of generating each sample target image from the corresponding sample baseline image and sample source image. One or more of the sample sets are incomplete, missing their sample source images. Each incomplete sample set is completed (--) by simulating the sample source image from at least the sample baseline image and the sample target image of the sample set. A computer programs () and a computer program products for implementing the method () are proposed. Moreover, a computing system () for performing the method () is proposed.