Patent · US Active

Denoising medical images by learning sparse image representations with a deep unfolding approach using scan specific metadata

US10692189B2 · kind B2 · utility

4Cited by

1References

9Claims

0Family size

Assignee

Siemens Healthcare Limited · DE

Inventors

Katrin Mentl · Lawrenceville, US
Boris Mailhe · Plainsboro, US
Mariappan S. Nadar · Plainsboro, US
Niklas Baumgarten · Karlsruhe, DE

Key dates

Filing date	May 23, 2018
Grant date	Jun 23, 2020
Priority date	—
Expiry date	Dec 1, 2038

Classification

Technology area (CPC G)Physics
CPC primaryG06T2211/441
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

The present embodiments relate to denoising medical images. By way of introduction, the present embodiments described below include apparatuses and methods for machine learning sparse image representations with deep unfolding and deploying the machine learnt network to denoise medical images. Iterative thresholding is performed using a deep neural network by training each layer of the network as an iteration of an iterative shrinkage algorithm. The deep neural network is randomly initialized and trained independently with a patch-based approach to learn sparse image representations for denoising image data. The different layers of the deep neural network are unfolded into a feed-forward network trained end-to-end.

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