Literature DB >> 29685544

Over-scanning in chest CT: Comparison of practice among six hospitals and its impact on radiation dose.

Fides Schwartz1, Bram Stieltjes2, Zsolt Szucs-Farkas3, André Euler4.   

Abstract

OBJECTIVES: Compare incidence of over-scanning in chest CT among six hospitals and impact on effective and organ effective radiation dose.
METHODS: Scout images of 600 chest CTs from six hospitals (A-F) were retrospectively reviewed using a radiation dose tracking software (RTS). Optimal scan range was determined and compared to the actual scan range. Incidence of cranial and caudal over-scanning was assessed and changes in total and organ effective dose were calculated. Descriptive statistics, Tukey- and Wilcoxon matched pairs test were applied.
RESULTS: Simultaneous cranial and caudal over-scanning occurred in 29 of 600 scans (A = 0%, B = 1%, C = 12%, D = 3%, E = 11%, F = 2%). Effective radiation dose increased on average by 0.29 mSv (P < 0.001). Cranial over-scanning was observed in 45 of 600 scans (A = 0%, B = 8%, C = 2%, D = 15%, E = 17%, F = 3%) and increased organ effective dose by 0.35 mSv in the thyroid gland (P < 0.001). Caudal over-scanning occurred in 147 of 600 scans (A = 7%, B = 9%, C = 35%, D = 4%, E = 32%, F = 60%) and increased organ effective doses in the upper abdomen by up to 14% (P < 0.001 for all organs).
CONCLUSIONS: Substantial differences in the incidence of over-scanning in chest CT exist among different hospitals. These differences result in excessive effective radiation dose and increased individual organ effective doses in patients.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Multidetector computed tomography; Over-scanning; Radiation exposure; Radiation monitoring; Thorax

Mesh:

Year:  2018        PMID: 29685544     DOI: 10.1016/j.ejrad.2018.03.005

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  2 in total

1.  Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program.

Authors:  Sihwan Kim; Woo Kyoung Jeong; Jin Hwa Choi; Jong Hyo Kim; Minsoo Chun
Journal:  PLoS One       Date:  2022-09-29       Impact factor: 3.752

2.  Automatic Scan Range Delimitation in Chest CT Using Deep Learning.

Authors:  Aydin Demircioğlu; Moon-Sung Kim; Magdalena Charis Stein; Nika Guberina; Lale Umutlu; Kai Nassenstein
Journal:  Radiol Artif Intell       Date:  2021-02-10
  2 in total

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