Literature DB >> 31176431

The Use of Digital Side Markers (DSMs) and Cropping in Digital Radiography.

Christopher M Hayre1, Shane Blackman2, Alison Eyden2, Kevin Carlton2.   

Abstract

INTRODUCTION: This article explores two phenomena in the general radiography environment-the increasing use of digital side markers (DSMs) by radiographers and the possibility for radiographers to "crop" radiographs post-exposure. This article offers originality by identifying the rationales of radiographers when using digital equipment.
METHODS: This study formed part of a larger ethnographic study undertaken in the United Kingdom (UK). Participant observation and semi-structured interviews were used. Overt participant observation identified cropping and DSM placement within the X-ray room. Semi-structured interviews later supported and/or refuted the rationale for DSMs and cropping clinically.
RESULTS: Two themes are discussed. First, radiographers support the use of DSMs by suggesting that mistakes will happen regardless of using an ASM and/or DSM. Furthermore, it is proposed that ASMs and DSMs can be interchangeably used in practice. Second, radiographers acknowledge the use of cropping ensuring their radiographs resemble "a textbook image." This leads to question the optimum use of collimation in the clinical environment and how it may go unnoticed.
CONCLUSION: This article concludes by recognizing some challenges digital radiography currently provides. The rationale and continuing use of DSMs and cropping of radiographs by radiographers highlights alternate complexities with digital technology in the clinical environment and how we may best overcome such challenges that influence the profession.
Copyright © 2019. Published by Elsevier Inc.

Entities:  

Keywords:  Cropping; X-ray collimation; anatomical side markers; collimation creep; digital radiography; digital side markers; qualitative

Mesh:

Year:  2019        PMID: 31176431     DOI: 10.1016/j.jmir.2018.11.001

Source DB:  PubMed          Journal:  J Med Imaging Radiat Sci        ISSN: 1876-7982


  1 in total

1.  Determining the anatomical site in knee radiographs using deep learning.

Authors:  Anton S Quinsten; Lale Umutlu; Michael Forsting; Kai Nassenstein; Aydin Demircioğlu
Journal:  Sci Rep       Date:  2022-03-07       Impact factor: 4.379

  1 in total

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