Validation of the educational effectiveness of a mobile learning app to improve knowledge about MR image quality optimisation and artefact reduction

Title: Validation of the educational effectiveness of a mobile learning app to improve knowledge about MR image quality optimisation and artefact reduction
Authors: Alsharif, WalaaDavis, MichaelaRainford, LouiseCradock, AndreaMcGee, Allison
Permanent link: http://hdl.handle.net/10197/10562
Date: 14-Jun-2018
Online since: 2019-05-21T08:45:43Z
Abstract: Aim: The aim was to design an app-based eLearning tool to provide radiographers with information about the physical basis of MR artefacts and practical elimination or/and minimisation strategies to optimise image quality, and to evaluate the impact of a smartphone app on radiographers’ knowledge. Methods: The study used the comparison-experimental approach (pre- and post-test). Thirty-five MR radiographers independently reviewed a prepared series of MR images (n = 25). The participants were requested to identify image quality related errors, to specify error-correction strategies and to score how confident they were in their responses. Participants were then divided into experimental (n = 19) and control cohorts (n = 16). The app was provided to the experimental cohort for 3 months; after this period both cohorts re-reviewed the MR image datasets and repeated their identification of image quality errors. Results: The results showed a statistically significant difference between control and experimental cohorts relative to participants’ pre- to post-test knowledge level. For the experimental cohort, years of experience, qualification and type of hospital were not associated with radiographer knowledge level and confidence in recognising the presence of an image quality error, naming the error and specifying appropriate correction strategies (p > 0.05). Conclusion: The study identified the potential of the smartphone app as an effective educational tool to support MR radiographers’ knowledge in recognising and characterising MR image quality errors.
Type of material: Journal Article
Publisher: Springer
Journal: Insights into Imaging
Volume: 9
Issue: 5
Start page: 721
End page: 730
Copyright (published version): 2018 the Authors
Keywords: AppMRIImage qualityKnowledgeConfidence
DOI: 10.1007/s13244-018-0635-0
Language: en
Status of Item: Peer reviewed
Appears in Collections:Medicine Research Collection

Show full item record

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.