Patent · US Active

Magnetic resonance imaging quality classification based on deep machine-learning to account for less training data

US10991092B2 · kind B2 · utility

3Cited by

2References

15Claims

0Family size

Assignee

Siemens Healthcare Limited · DE

Inventors

Sandro Braun · Karlsruhe, DE
Boris Mailhe · Plainsboro, US
Xiao Chen · Somerville, US
Benjamin L. Odry · West New York, US
Mariappan S. Nadar · Plainsboro, US

Key dates

Filing date	Dec 10, 2018
Grant date	Apr 27, 2021
Priority date	—
Expiry date	May 13, 2039

Classification

Technology area (CPC G)Physics
CPC primaryG06V2201/03
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

For classifying magnetic resonance image quality or training to classify magnetic resonance image quality, deep learning is used to learn features distinguishing between corrupt images base on simulation and measured similarity. The deep learning uses synthetic data without quality annotation, allowing a large set of training data. The deep-learned features are then used as input features for training a classifier using training data annotated with ground truth quality. A smaller training data set may be needed to train the classifier due to the use of features learned without the quality annotation.

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