Patent ID: 11944463
Assignee: ELEKTA LIMITED
Field: Medical technology (Instruments)
Classification: CPC A  G | IPC A  G

Claim 10:
11. A non-transitory computer readable medium comprising non-transitory computer readable instructions for instructing processing circuitry to perform operations comprising:
receiving one or more multi-channel Magnetic Resonance (MR) images of a subject;
converting the one or more multi-channel MR images into one or more tissue parameter maps based on a fitting technique that expresses MR signal intensity as a function of a set of intrinsic tissue parameters; and
applying a machine learning technique to the one or more tissue parameter maps, the machine learning technique trained by:
receiving a first MR image;
extracting a plurality of features from a plurality of image points of the first MR image;
receiving a Computerized Tomography (CT) image associated with the first MR image;
extracting a CT value for the image point of the plurality of image points of the CT image;
aligning the first MR image and the CT image;
generating, based on the machine learning technique, a pseudo-CT image from the extracted plurality of features of the first MR image, the machine learning technique trained to model a relationship between a Computerized Tomography (CT) CT value parameter H, a tissue value parameter, and a β parameter by performing a data fitting technique on the CT value parameter H and the tissue value parameter to compute the β parameter, the CT value parameter H being represented by a mathematical combination of the β parameter and the tissue value parameter, the pseudo-CT image being generated as a function of the β parameter and the extracted plurality of features of the first MR image;
denoting the CT value parameter H to be the extracted CT value and the tissue value parameter to be a feature vector comprising the extracted plurality of features; and
storing the plurality of extracted features in a respective dimension of a plurality of dimensions of the feature vector for the image point of the plurality of image points of the first MR image; and

generating a pseudo-Computerized Tomography (CT) image for the subject in response to applying the machine learning technique to the one or more tissue parameter maps, the machine learning technique predicting a CT number at each location of the one or more MR images of the subject.