DIRECT INFERENCE BASED ON UNDERSAMPLED MRI DATA OF INDUSTRIAL SAMPLES

Method for automated non-invasive identification of a predetermined feature in a multitude of industrial samples (102) of a predefined sample type, the method comprising the steps of: a) conveying an industrial sample (102) of the predefined sample type into an MRI scanner (106), b) recording in an MRI measurement for at least one slice or at least a partial volume of the industrial sample undersampled MRI data (300), comprising: - undersampled raw MRI data, comprising a multitude of time dependent signals for different phases, and/or - processed MRI data, obtained from processing undersampled raw MRI data, and c) analysing the undersampled MRI data (300) with an inference module (200) for identifying a predetermined feature of the industrial sample (102) using a machine learning module (204) that is trained for identifying the predetermined feature in industrial samples (102) of the predefined sample type from undersampled MRI data (300), wherein the inference module (200) comprises a memory (202) storing the machine learning module (204) and a processor (206) for controlling the inference module (200), wherein the inference module (200) is configured to provide the undersampled MRI data (300) as an input to the machine learning module (200) and to analyse the undersampled MRI data (300) using the machine learning module (204), wherein the machine learning module (204) is trained for identifying the predetermined feature in industrial samples (102) of the predefined sample type using a training set (302) comprising undersampled MRI data (300) of different training samples of the predefined sample type, wherein a fraction of the training samples comprises the predetermined feature and a fraction of the training samples does not comprise the predetermined feature.