Literature DB >> 27056436

CT dose reduction using Automatic Exposure Control and iterative reconstruction: A chest paediatric phantoms study.

Joël Greffier1, Fabricio Pereira2, Francesco Macri3, Jean-Paul Beregi4, Ahmed Larbi5.   

Abstract

PURPOSE: To evaluate the impact of Automatic Exposure Control (AEC) on radiation dose and image quality in paediatric chest scans (MDCT), with or without iterative reconstruction (IR).
METHODS: Three anthropomorphic phantoms representing children aged one, five and 10-year-old were explored using AEC system (CARE Dose 4D) with five modulation strength options. For each phantom, six acquisitions were carried out: one with fixed mAs (without AEC) and five each with different modulation strength. Raw data were reconstructed with Filtered Back Projection (FBP) and with two distinct levels of IR using soft and strong kernels. Dose reduction and image quality indices (Noise, SNR, CNR) were measured in lung and soft tissues. Noise Power Spectrum (NPS) was evaluated with a Catphan 600 phantom.
RESULTS: The use of AEC produced a significant dose reduction (p<0.01) for all anthropomorphic sizes employed. According to the modulation strength applied, dose delivered was reduced from 43% to 91%. This pattern led to significantly increased noise (p<0.01) and reduced SNR and CNR (p<0.01). However, IR was able to improve these indices. The use of AEC/IR preserved image quality indices with a lower dose delivered. Doses were reduced from 39% to 58% for the one-year-old phantom, from 46% to 63% for the five-year-old phantom, and from 58% to 74% for the 10-year-old phantom. In addition, AEC/IR changed the patterns of NPS curves in amplitude and in spatial frequency.
CONCLUSIONS: In chest paediatric MDCT, the use of AEC with IR allows one to obtain a significant dose reduction while maintaining constant image quality indices.
Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Automatic Exposure Control; Children; Iterative reconstruction; MDCT; Radiation dose

Mesh:

Year:  2016        PMID: 27056436     DOI: 10.1016/j.ejmp.2016.03.007

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  5 in total

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Authors:  Le Cao; Xiang Liu; Jianying Li; Tingting Qu; Lihong Chen; Yannan Cheng; Jieliang Hu; Jingtao Sun; Jianxin Guo
Journal:  Br J Radiol       Date:  2020-12-11       Impact factor: 3.039

2.  Quantitative and qualitative evaluation of hybrid iterative reconstruction, with and without noise power spectrum models: A phantom study.

Authors:  Kazuya Minamishima; Koichi Sugisawa; Yoshitake Yamada; Masahiro Jinzaki
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Journal:  PLoS One       Date:  2021-01-14       Impact factor: 3.240

4.  Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction.

Authors:  Haesung Yoon; Jisoo Kim; Hyun Ji Lim; Mi-Jung Lee
Journal:  BMC Med Imaging       Date:  2021-10-10       Impact factor: 1.930

5.  Optimisation of CT protocols in PET-CT across different scanner models using different automatic exposure control methods and iterative reconstruction algorithms.

Authors:  Sarah-May Gould; Jane Mackewn; Sugama Chicklore; Gary J R Cook; Andrew Mallia; Lucy Pike
Journal:  EJNMMI Phys       Date:  2021-07-31
  5 in total

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