Abstract: The present invention discloses a deep learning-based method for generating 7T magnetic resonance (MR) images from 3T MR images. The method comprises the following steps: (1) Constructing a training dataset of paired 3T and 7T images; (2) Constructing a deep learning model for generating 7T images from 3T images; (3) Defining a loss function for the deep learning model for generating 7T images from 3T images; (4) Training the deep learning model to obtain optimal model parameters; and (5) Synthesizing 7T images from 3T images using the trained deep learning model. The invention employs a spatial alignment network to estimate and compensate for spatial mismatches between 3T and 7T images, thereby achieving superior 7T image synthesis.

Abstract: The present invention discloses a deep learning-based method for generating 7T magnetic resonance (MR) images from 3T MR images. The method comprises the following steps: (1) Constructing a training dataset of paired 3T and 7T images; (2) Constructing a deep learning model for generating 7T images from 3T images; (3) Defining a loss function for the deep learning model for generating 7T images from 3T images; (4) Training the deep learning model to obtain optimal model parameters; and (5) Synthesizing 7T images from 3T images using the trained deep learning model. The invention employs a spatial alignment network to estimate and compensate for spatial mismatches between 3T and 7T images, thereby achieving superior 7T image synthesis.