Image Quality Score Using A Deep Generative Machine-Learning Model
AssigneeSiemens Healthcare GmbH
InventorsBenjamin L. Odry, Boris Mailhe, Hasan Ertan Cetingul, Xiao Chen, Mariappan S. Nadar
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Abstract
For image quality scoring of an image from a medical scanner, a generative model of an expected good quality image may be created using deep machine-learning. The deviation of an input image from the generative model is used as an input feature vector for a discriminative model. The discriminative model may also operate on another input feature vector derived from the input image. Based on these input feature vectors, the discriminative model outputs an image quality score.
Claims20
A method for image quality scoring of an image from a medical scanner, the method comprising:
- generating, by the medical scanner, the image representing a patient, the image having a level of artifacts due to the generating by the medical scanner;
- determining, by a machine, a probability map of artifacts as a function of location for the image with a deep generative machine-learnt model;
- assigning, by the machine, a quality score for the image with application of the probability map to a discriminative machine-learnt classifier;
- and
- transmitting the quality score for the image of the patient.
A method for training a machine to determine an image quality score, the method comprising:
- training, by the machine, a deep generative model using a piecewise-differentiable function, the deep generative model trained to output a spatial distribution of probability in response to an input image;
- and
- training, by the machine, a discriminative classifier, the discriminative classifier trained to output a score of image quality as a function of input of the spatial distribution of probability.
A method for image quality scoring of an image from a medical scanner, the method comprising:
- generating, by the medical scanner, the image representing a patient, the image having a level of artifacts due to the generating by the medical scanner;
- determining, by a machine, a probability map of artifacts as a function of location for the image with a deep generative machine-learnt model;
- assigning, by the machine, a quality score for the image with application of the probability map to a discriminative machine-learnt classifier, the probability map comprising a first input vector and features of the image comprising a second input vector;
- and
- transmitting the quality score for the image of the patient.
Dependencies are inferred from the claim text.