2020
DOI: 10.1186/s41824-019-0070-6
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Competencies and training of radiographers and technologists for PET/MR imaging - a study from the UK MR-PET network

Abstract: Background: After the success of PET/CT as a clinical diagnostic tool, the introduction of PET/MRI is a natural development aimed at further improving combined diagnostic imaging and reduced ionising radiation dose for half-body imaging. As with PET and CT, the combination of PET and MRI presents a series of issues that need to be addressed regarding workforce training and education. At present, there is a lack of agreement over the competencies, training requirements and educational pathways needed for PET/MR… Show more

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Cited by 8 publications
(5 citation statements)
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“…Deep learning–based algorithms emerged as promising approaches for solving a variety of image analysis and pattern recognition problems and have been successfully implemented in the context of metal artefact reduction in CT imaging [ 18 23 ]. Deep learning–based MAR techniques could be categorized as the 4th type of MAR approaches, wherein the correction for metal-induced beam hardening is applied in either the projection [ 22 ] or image domain [ 19 ].…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning–based algorithms emerged as promising approaches for solving a variety of image analysis and pattern recognition problems and have been successfully implemented in the context of metal artefact reduction in CT imaging [ 18 23 ]. Deep learning–based MAR techniques could be categorized as the 4th type of MAR approaches, wherein the correction for metal-induced beam hardening is applied in either the projection [ 22 ] or image domain [ 19 ].…”
Section: Introductionmentioning
confidence: 99%
“…However, the focus of this scoping review was on gamma camera imaging and non-imaging procedures as a distinct area of specialist practice. Both PET and hybrid combinations with CT and MRI frequently require dedicated training pathways outside of the sphere of interest for this review [12]. An overview of the inclusion and exclusion criteria used for this study is shown in Table 1.…”
Section: Methodsmentioning
confidence: 99%
“…15 Moving forward, training standards and agreement on competency for both professions will be important to ensure this partnership between teams to deliver precision medicine does not fold under its own complexity and disagreements around ownership of roles. 16 There is the potential to improve collaborative knowledge of radiation science, image acquisition, technical challenges and clinical problem-solving, ultimately benefiting some of healthcare's most vulnerable patients.…”
Section: Professional Collaborationmentioning
confidence: 99%