Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network.

ABSTRACT:

Background

Dual-energy computed tomography (DECT) has been widely used due to improved substances identification from additional spectral information. The quality of material-specific image produced by DECT attaches great importance to the elaborated design of the basis material decomposition method.

Objective

The aim of this work is to develop and validate a data-driven algorithm for the image-based decomposition problem.

Methods

A deep neural net, consisting of a fully convolutional net (FCN) and a fully connected net, is proposed to solve the material decomposition problem. The former net extracts the feature representation of input reconstructed images, and the latter net calculates the decomposed basic material coefficients from the joint feature vector. The whol

SUBMITTER: Xu Y 

PROVIDER: S-EPMC6145159 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature