US 2022/0044452 A1 · HYBRID IMAGE RECONSTRUCTION SYSTEM

Abstract

Generally, there is provided a hybrid image reconstruction system. The hybrid image reconstruction system includes a deep learning stage and a compressed sensing stage. The deep learning stage is configured to receive an input data set that includes measured tomographic data and to produce a deep learning stage output. The deep learning stage includes a mapping circuitry, and at least one artificial neural network. The mapping circuitry is configured to map image domain data to a tomographic data domain. The compressed sensing stage is configured to receive the deep learning stage output and to provide refined image data as output.

Claims20

Dependent claims

A hybrid image reconstruction system, the system comprising:

a deep learning stage configured to receive an input data set comprising measured tomographic data and to produce a deep learning stage output, the deep learning stage comprising a mapping circuitry, and at least one artificial neural network, the mapping circuitry configured to map image domain data to a tomographic data domain;
and
a compressed sensing stage configured to receive the deep learning stage output and to provide refined image data as output.

A method for hybrid image reconstruction, the method comprising:

receiving, by a deep learning stage, an input data set comprising measured tomographic data;
producing, by the deep learning stage, a deep learning stage output, the deep learning stage comprising a mapping circuitry, and at least one artificial neural network, the mapping circuitry configured to map image domain data to a tomographic data domain;
receiving, by a compressed sensing stage, the deep learning stage output;
and
providing, by the compressed sensing stage, refined image data as output.

2 more elements

Dependencies are inferred from the claim text.

Facts

Application

17/393,922Filed 4 Aug 2021

Priority

4 Aug 2020

Published

10 Feb 2022

Term ends

Not estimatedThe source records don’t support an estimate.

Classification20 codes

G06N 3/088
Learning methods: non-supervised learning, e.g. competitive learning
G06T 12/10
Tomographic reconstruction from projections: image preprocessing, e.g. calibration, positioning of sources or scatter correction
G06N 3/04
Neural networks: architecture, e.g. interconnection topology
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks

Hybrid image reconstruction system

Life of this patent

Hybrid image reconstruction system

Life of this patent

Abstract

Claims20