System and method for denoising in magnetic resonance imaging
AssigneeThe Chinese University of Hong Kong
InventorsShutian Zhao, Weitian Chen
Life of this patent
Estimating the term…
Abstract
Denoising of magnetic resonance (MR) images can be achieved using a deep neural network and an image acquisition process that uses multi-NEX (Number of Excitations) or multi-NSA (Number of Signal Averages or Acquisitions) to produce two or more complex-valued images of a region of interest. The set of images resulting from the image acquisition process can be input to a deep neural network that has been trained to produce a denoised MR image from a set of multi-NEX or multi-NSA images. The deep neural network can be implemented using a two-dimensional or three-dimensional convolutional neural network to match the dimensionality of the input images. Training of the denoising neural network can use real MR images.
Claims24
A method for generating a magnetic resonance (MR) image, the method comprising:
- obtaining a set of input images from a magnetic resonance imaging (MRI) system wherein the input images are obtained using a multi-NEX (Number of EXcitations) or multi-NSA (Number of Signal Averages or Acquisitions) protocol and the set of input images includes a number of images equal to the number of NEX or NSA;
- inputting the set of images to a denoising neural network that has been trained to perform denoising on a set of input images, wherein the denoising neural network includes two stages of residual learning wherein:
- in a first stage, a first residual difference map is calculated and a skip connection is built on the average of the set of input images, thereby producing an intermediate residual-learning output map;
- and
- in a second stage, features extracted from all of the input images and the intermediate residual-learning output map are used to generate a second residual difference map;
- and
- obtaining a denoised output image from the denoising neural network, wherein the denoised output image is obtained from the second residual difference map and the intermediate residual-learning output map.
A magnetic resonance imaging (MRI) system comprising:
- an MRI apparatus having a magnet, a gradient coil, and one or more radiofrequency (RF) coils;
- and
- a computer communicably coupled to the MRI apparatus, the computer having a processor, a memory, and a user interface, the processor being configured to:
- obtain a set of input images from the magnetic resonance imaging (MRI) apparatus, wherein the input images are obtained using a multi-NEX (Number of EXcitations) or multi-NSA (Number of Signal Averages or Acquisitions) protocol and the set of input images includes a number of images equal to the number of NEX or NSA;
- input the set of images to a denoising neural network that has been trained to perform denoising on a set of input images, wherein the denoising neural network includes two stages of residual learning wherein:
- in a first stage, a first residual difference map is calculated and a skip connection is built on an average of the set of input images, thereby producing an intermediate residual-learning output map;
- and
- in a second stage, features extracted from all of the input images and the intermediate residual-learning output map are used to generate a second residual difference map;
- and
- obtain a denoised output image from the denoising neural network, wherein the denoised output image is obtained from the second residual difference map and the intermediate residual-learning output map.
A computer-readable storage medium having stored thereon program code instructions that, when executed by a processor in a computer communicably coupled to a magnetic resonance imaging (MRI) apparatus, cause the processor to perform a method comprising:
- obtaining a set of input images from a magnetic resonance imaging (MRI) system wherein the input images are obtained using a multi-NEX (Number of EXcitations) or multi-NSA (Number of Signal Averages or Acquisitions) protocol and the set of input images includes a number of images equal to the number of NEX or NSA;
- inputting the set of images to a denoising neural network that has been trained to perform denoising on a set of input images, wherein the denoising neural network includes two stages of residual learning wherein:
- in a first stage, a first residual difference map is calculated and a skip connection is built on an average of the set of input images, thereby producing an intermediate residual-learning output map;
- and
- in a second stage, features extracted from all of the input images and the intermediate residual-learning output map are used to generate a second residual difference map;
- and
- obtaining a denoised output image from the denoising neural network, wherein the denoised output image is obtained from the second residual difference map and the intermediate residual-learning output map.
Dependencies are inferred from the claim text.