Sinogram-consistency learning in CT for metal artifact reduction

08/02/2017
by   Hyung Suk Park, et al.
0

This paper proposes a sinogram consistency learning method to deal with beam-hardening related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field causes an inconsistent sinogram, that does not match the range space of the Radon transform. When the mismatched data are entered into the range space during CT reconstruction, streaking and shading artifacts are generated owing to the inherent nature of the inverse Radon transform. The proposed learning method aims to repair inconsistent sinograms by removing the primary metal-induced beam-hardening factors along the metal trace in the sinogram. Taking account of the fundamental difficulty in obtaining sufficient training data in a medical environment, the learning method is designed to use simulated training data. We use a patient-type specific learning model to simplify the learning process. The quality of sinogram repair was established through data inconsistency-evaluation and acceptance checking, which were conducted using a specially designed inconsistency-evaluation function that identifies the degree and structure of mismatch in terms of projection angles. The results show that our method successfully corrects sinogram inconsistency by extracting beam-hardening sources by means of deep learning.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 3

page 4

page 5

01/06/2021

A New Weighting Scheme for Fan-beam and Circle Cone-beam CT Reconstructions

In this paper, we first present an arc based algorithm for fan-beam comp...
10/08/2020

A two-stage approach for beam hardening artifact reduction in low-dose dental CBCT

This paper presents a two-stage method for beam hardening artifact corre...
06/15/2019

An Exact and Fast CBCT Reconstruction via Pseudo-Polar Fourier Transform based Discrete Grangeat's Formula

The recent application of Fourier Based Iterative Reconstruction Method ...
10/09/2019

A cascaded dual-domain deep learning reconstruction method for sparsely spaced multidetector helical CT

Helical CT has been widely used in clinical diagnosis. Sparsely spaced m...
09/19/2019

Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories

Metal artifacts in computed tomography (CT) arise from a mismatch betwee...
11/19/2017

The process of 3D-printed skull models for the anatomy education

Objective The 3D printed medical models can come from virtual digital re...
06/29/2019

Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction

A conventional approach to computed tomography (CT) or cone beam CT (CBC...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.