Patent · US Active

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

US10852379B2 · kind B2 · utility

7Cited by

3References

16Claims

0Family size

Assignee

Siemens Healthcare Limited · DE

Inventors

Xiao Chen · Somerville, US
Boris Mailhe · Plainsboro, US
Benjamin L. Odry · West New York, US
Pascal Ceccaldi · New York, US
Mariappan S. Nadar · Plainsboro, US

Key dates

Filing date	Jun 7, 2018
Grant date	Dec 1, 2020
Priority date	—
Expiry date	Mar 27, 2039

Classification

Technology area (CPC G)Physics
CPC primaryG06T2210/41
WIPO fieldMeasurement
WIPO sectorInstruments

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.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.