Artifact reduction by image-to-image network in magnetic resonance imaging

Patent metadata

Field	Value
Publication number	US-10852379-B2
Application number	US-201816002447-A
Country	US
Kind code	B2
Filing date	Jun 7, 2018
Priority date	Jun 7, 2018
Publication date	Dec 1, 2020
Grant date	Dec 1, 2020

Abstract

For artifact reduction in a magnetic resonance imaging system, deep learning trains an image-to-image neural network to generate an image with reduced artifact from input, artifacted MR data. For application, the image-to-image network may be applied in real time with a lower computational burden than typical post-processing methods. To handle a range of different imaging situations, the image-to-image network may (a) use an auxiliary map as an input with the MR data from the patient, (b) use sequence metadata as a controller of the encoder of the image-to-image network, and/or (c) be trained to generate contrast invariant features in the encoder using a discriminator that receives encoder features.

First claim

What is claimed is:

1. A method for artifact reduction in a magnetic resonance imaging system, the method comprising:
scanning a patient by the magnetic resonance imaging system, the scanning providing at least first and second sets of magnetic resonance data, the first and second sets representing response to the scanning from first and second distributions of locations;
inputting the first and second sets of the magnetic resonance data to a neural network machine trained as an image-to-image network;
generating an image by the neural network in response to the inputting of the first and second sets; and
displaying the image,
wherein inputting comprises inputting the second set as k-space data or as image data reconstructed from the k-space data, the second set including artifact information, and wherein the neural network was trained from third sets including artifacts and ground truth images with a reduced contribution from the artifacts.

2. The method of claim 1 wherein scanning comprises acquiring the first set with a pre-scan of the patient and acquiring the second set as an imaging scan of the patient based on the pre-scan.

3. The method of claim 2 wherein acquiring the first set comprises acquiring a coil correction pre-scan or a noise map.

4. The method of claim 1 wherein scanning comprises scanning the patient and a background region around the patient, the first set being for the background region and the second set being for the patient.

5. The method of claim 1 wherein inputting comprises inputting the first and second sets on separate input channels of the neural network, wherein the first and second distributions of locations comprise either a same location or two different locations.

6. The method of claim 1 wherein the first set comprises an auxiliary map, and the second set comprises the magnetic resonance data for representing the patient in imaging and including contribution from the artifact, and wherein generating the image comprises generating the image with an artifact level reduced relative to the contribution from the artifact.

7. The method of claim 1 wherein scanning comprises scanning with a scan sequence, and wherein inputting comprises inputting information about the scan sequence to an encoder of the image-to-image network.

8. The method of claim 1 wherein scanning comprises scanning with a scan sequence, and wherein the image-to-image network includes an encoder having been trained with a discriminator of scan sequences.

9. A method for artifact reduction in a magnetic resonance imaging system, the method comprising:
scanning a patient by the magnetic resonance imaging system, the scanning providing a set of magnetic resonance data based on a scan sequence;
inputting the set of the magnetic resonance data to a neural network machine trained as an image-to-image network;
inputting information about the scan sequence to an encoder or decoder of the image-to-image network;
generating an image of the patient by the neural network in response to the inputting of the set and the information; and
displaying the image,
wherein inputting the information comprises inputting parameters characterizing the sequence as a conditional matrix configured to tune features of the encoder.

10. The method of claim 9 wherein inputting the set comprises inputting the set as k-space data or as image data reconstructed from the k-space data, the set including artifact information, and wherein the neural network was trained from training sets of magnetic resonance data including artifacts and ground truth images with a reduced contribution from the artifacts.

11. The method of claim 9 wherein inputting the information comprises inputting sequence metadata.

12. The method of claim 11 wherein inputting the sequence metadata comprises inputting a sequence type and/or sequence protocol values.

13. The method of claim 9 wherein inputting the information comprises inputting the information in a conditional matrix.

14. The method of claim 9 wherein the magnetic resonance data includes contribution from the artifact, and wherein generating the image comprises generating the image with an artifact level reduced relative to the contribution from the artifact.

15. The method of claim 9 wherein inputting the set comprises inputting the set and an auxiliary map on separate input channels of the encoder or decoder.

16. The method of claim 9 wherein the image-to-image network includes the encoder having been trained with a discriminator of scan sequences.

Assignees

• Siemens Healthcare Gmbh

Inventors

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

Classifications

• G06T12/30

  Image post-processing, e.g. metal artefact correction · CPC title

• G01R33/56509

  due to motion, displacement or flow, e.g. gradient moment nulling (G01R33/567 takes precedence) · CPC title

• G01R33/565 Primary

  Correction of image distortions, e.g. due to magnetic field inhomogeneities · CPC title

• G06T2210/41

  Medical · CPC title

• G01R33/5608 Primary

  Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T) · CPC title