A system for generating a pseudo-CT prediction model, comprising: a database configured to store training data comprising Magnetic Resonance data and Computerized Tomography data of a plurality of training subjects, each training subject having at least one MR image and at least one CT image; a processor communicatively coupled to the database for accessing information stored in the database; a memory communicatively coupled to the processor, the memory storing instructions that, when executed by the processor, configures the processor to perform operations comprising: accessing the database to retrieve the training data including at least one MR image and at least one CT image for each of the plurality of training subjects; for each training subject: extracting a plurality of different types of features (212, 214, 216, 218) from each image point of the at least one MR image, the plurality of different types of features including at least one classification-based feature and at least one numerical feature, the at least one classification-based feature comprising a tissue type or a structure label; creating a feature vector for each image point based on the extracted features (212, 214, 216, 218); and extracting a CT value from each image point of the at least one CT image; and generating the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects. a database configured to store training data comprising Magnetic Resonance data and Computerized Tomography data of a plurality of training subjects, each training subject having at least one MR image and at least one CT image; a processor communicatively coupled to the database for accessing information stored in the database; a memory communicatively coupled to the processor, the memory storing instructions that, when executed by the processor, configures the processor to perform operations comprising: accessing the database to retrieve the training data including at least one MR image and at least one CT image for each of the plurality of training subjects; for each training subject: extracting a plurality of different types of features (212, 214, 216, 218) from each image point of the at least one MR image, the plurality of different types of features including at least one classification-based feature and at least one numerical feature, the at least one classification-based feature comprising a tissue type or a structure label; creating a feature vector for each image point based on the extracted features (212, 214, 216, 218); and extracting a CT value from each image point of the at least one CT image; and generating the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects. accessing the database to retrieve the training data including at least one MR image and at least one CT image for each of the plurality of training subjects; for each training subject: extracting a plurality of different types of features (212, 214, 216, 218) from each image point of the at least one MR image, the plurality of different types of features including at least one classification-based feature and at least one numerical feature, the at least one classification-based feature comprising a tissue type or a structure label; creating a feature vector for each image point based on the extracted features (212, 214, 216, 218); and extracting a CT value from each image point of the at least one CT image; and extracting a plurality of different types of features (212, 214, 216, 218) from each image point of the at least one MR image, the plurality of different types of features including at least one classification-based feature and at least one numerical feature, the at least one classification-based feature comprising a tissue type or a structure label; creating a feature vector for each image point based on the extracted features (212, 214, 216, 218); and extracting a CT value from each image point of the at least one CT image; and generating the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects.