Generating synthetic electron density images from magnetic resonance images

Abstract

A conversion device is operable to perform a learning-based method of generating a synthetic electron density image (sCT) of an anatomical portion based on one or more magnetic resonance (MR) images. The method is processing-efficient and capable of producing highly accurate sCT images irrespective of misalignment in the underlying training set. The conversion device receives and installs a machine-learning model trained to predict coefficients of an image transfer function. The conversion device then receives a current set of MR images of the anatomical portion, computes current coefficients of the image transfer function by operating the machine-learning model on the current set of MR images, and computes a current sCT image of the anatomical portion by operating the current coefficients, in accordance with the image transfer function, on the current set of MR images.

CPC Classifications