Protocol independent image processing with adversarial networks

Patent metadata

Field	Value
Publication number	US-10624558-B2
Application number	US-201816055546-A
Country	US
Kind code	B2
Filing date	Aug 6, 2018
Priority date	Aug 10, 2017
Publication date	Apr 21, 2020
Grant date	Apr 21, 2020

Abstract

Systems and methods are provided for generating a protocol independent image. A deep learning generative framework learns to recognize the boundaries and classification of tissues in an MRI image. The deep learning generative framework includes an encoder, a decoder, and a discriminator network. The encoder is trained using the discriminator network to generate a latent space that is invariant to protocol and the decoder is trained to generate the best output possible for brain and/or tissue extraction.

First claim

What is claimed is:

1. A method for generating domain independent magnetic resonance images in a magnetic resonance imaging system, the method comprising:
scanning a patient by the magnetic resonance imaging system to acquire magnetic resonance data;
inputting the magnetic resonance data to a machine learnt generator network trained to extract features from input magnetic resonance data and reconstruct domain independent images using the extracted features;
generating, by the machine learnt generator network, a domain independent magnetic resonance image from the input magnetic resonance data; and
displaying the domain independent magnetic resonance image,
wherein the machine learnt generator network comprises an encoder configured to generate a compact representation of the input magnetic resonance data and a decoder configured to reconstruct the domain independent image from the compact representation,
wherein the machine learnt generator network is trained using a loss function that is calculated as a combination of a first value, computed from a first loss function provided by the decoder and a second value, computed from a second loss function provided by a first adversarial learnt network trained to classify concatenated features from the compact representation as either from a first domain or a second domain.

2. The method of claim 1 , wherein the second loss function is calculated as a function of a Wasserstein distance.

3. The method of claim 1 , wherein the second loss function is calculated as a function of a Cramer distance.

4. The method of claim 1 , wherein the first domain represents ground truth data.

5. The method of claim 1 , wherein the machine learnt generator network is further trained using a second adversarial learnt network trained to classify generated domain independent images as generated by the machine learnt generator network or ground truth images.

6. The method of claim 1 , wherein the domain independent magnetic resonance image is a segmented image.

7. The method of claim 6 , wherein the segmented image comprises a segmented brain image including boundaries for at least white matter, grey matter, and cerebrospinal fluid.

Assignees

• Siemens Healthcare Gmbh

Inventors

• Ceccaldi Pascal
• Odry Benjamin L
• Mailhe Boris
• Nadar Mariappan S

Classifications

• G06T7/10

  Segmentation; Edge detection (motion-based segmentation G06T7/215) · CPC title

• G06T2207/10088

  Magnetic resonance imaging [MRI] · CPC title

• G06T7/11

  Region-based segmentation · CPC title

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title

• G06T2207/20081

  Training; Learning · CPC title