Image reconstruction: Part 1 - understanding filtered back projection, noise and image acquisition.
Cardiovascular CT
Filtered back projection
Iterative reconstruction
Sinogram
Journal
Journal of cardiovascular computed tomography
ISSN: 1876-861X
Titre abrégé: J Cardiovasc Comput Tomogr
Pays: United States
ID NLM: 101308347
Informations de publication
Date de publication:
Historique:
received:
31
01
2019
revised:
04
04
2019
accepted:
15
04
2019
pubmed:
27
4
2019
medline:
8
9
2020
entrez:
27
4
2019
Statut:
ppublish
Résumé
Image reconstruction is an increasingly complex field in CT. Iterative Reconstruction (IR) is at present an adjunct to standard Filtered Back Projection (FBP) reconstruction, but could become a replacement for it. Due to its potential for scanning at lower radiation doses, IR has received a lot of attention in the medical literature and all vendors offer commercial solutions. Its use in cardiovascular CT has been driven in part due to concerns about radiation dose and image quality. This paper is the first manuscript of a pair. It aims to review the basic principles of CT scanning, to describe image reconstruction using Filtered Back Projection, and to identify the physical processes that contribute to image noise which IR may be able to compensate for. The aim is to enable cardiovascular imagers to understand what happens to the raw data prior to the reconstruction process so they may have a better appreciation of the strengths and weaknesses of the various reconstruction techniques available. The second manuscript of this pair will discuss the various vendor permutations of IR in more detail, including the most recent machine learning based offerings, and critically appraise the current clinical research available on the various IR techniques used in cardiovascular CT.
Identifiants
pubmed: 31023632
pii: S1934-5925(19)30060-7
doi: 10.1016/j.jcct.2019.04.008
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
219-225Informations de copyright
Crown Copyright © 2020. Published by Elsevier Inc. All rights reserved.