Patent · US Active

Motion determination for volumetric magnetic resonance imaging using a deep machine-learning model

US11255943B2 · kind B2 · utility

0Cited by

1References

19Claims

0Family size

Assignee

Siemens Healthcare Limited · DE

Inventors

Luoluo Liu · Baltimore, US
Xiao Chen · Somerville, US
Silvia Bettina Arroyo Camejo · Nuremberg, DE
Benjamin L. Odry · West New York, US
Mariappan S. Nadar · Plainsboro, US

Key dates

Filing date	Oct 17, 2018
Grant date	Feb 22, 2022
Priority date	—
Expiry date	Apr 17, 2039

Classification

Technology area (CPC G)Physics
CPC primaryG06T2207/30168
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

For determination of motion artifact in MR imaging, motion of the patient in three dimensions is used with a measurement k-space line order based on one or more actual imaging sequences to generate training data. The MR scan of the ground truth three-dimensional (3D) representation subjected to 3D motion is simulated using the realistic line order. The difference between the resulting reconstructed 3D representation and the ground truth 3D representation is used in machine-based deep learning to train a network to predict motion artifact or level given an input 3D representation from a scan of a patient. The architecture of the network may be defined to deal with anisotropic data from the MR scan.

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