Download PDF
Nuklearmedizin 2021; 60(01): 10-15
DOI: 10.1055/a-1267-9017
Original Article

An Open Source Solution for “Hands-on” teaching of PET/CT to Medical Students under the COVID-19 Pandemic

Eine Open-Source-Lösung für den Praxis-Unterricht in PET/CT für Medizinstudenten im Rahmen der COVID-19-Pandemie
Martin Biermann
1   Department of Clinical Medicine, Section for Radiology, University of Bergen Faculty of Medicine and Dentistry, Bergen, Norway
,
Salim Kanoun
2   Centre de Recherche en Cancérologie de Toulouse, France
,
Trond Davidsen
3   IT Department, University of Bergen, Norway
,
Robert Gray
4   Department of Education, University of Bergen Faculty of Humanities, Bergen, Norway
› Author Affiliations
› Further Information
Also available at
    Permissions and Reprints

Abstract

Aims Since 2017, medical students at the University of Bergen were taught PET/CT “hands-on” by viewing PET/CT cases in native format on diagnostic workstations in the hospital. Due to the COVID-19 pandemic, students were barred access. This prompted us to launch and evaluate a new freeware PET/CT viewing system hosted in the university network.

Methods We asked our students to install the multiplatform Fiji viewer with Beth Israel PET/CT plugin (http://petctviewer.org) on their personal computers and connect to a central image database in the university network based on the public domain orthanc server (https://orthanc-server.com). At the end of course, we conducted an anonymous student survey.

Results The new system was online within eight days, including regulatory approval. All 76 students (100 %) in the fifth year completed their course work, reading five anonymized PET/CT cases as planned. 41 (53 %) students answered the survey. Fiji was challenging to install with a mean score of 1.8 on a 5-point Likert scale (5 = easy, 1 = difficult). Fiji was more difficult to use (score 3.0) than the previously used diagnostic workstations in the hospital (score 4.1; p < 0.001, paired t-test). Despite the technical challenge, 47 % of students reported having learnt much (scores 4 and 5); only 11 % were negative (scores 1 and 2). 51 % found the PET/CT tasks engaging (scores 4 and 5) while 20 % and 5 % returned scores 2 and 1, respectively.

Conclusion Despite the initial technical challenge, “hands-on” learning of PET/CT based on the freeware Fiji/orthanc PET/CT-viewer was associated with a high degree of student satisfaction. We plan to continue running the system to give students permanent access to PET/CT cases in native format regardless of time or location.

Zusammenfassung

Ziel Seit 2017 wurden Medizinstudenten an der Universität Bergen in der Praxis in PET/CT unterrichtet, indem sie PET/CT-Fälle im nativen Format an diagnostischen Arbeitsplätzen im Krankenhaus betrachteten. Aufgrund der COVID-19-Pandemie wurde den Studenten dieser Zugang verwehrt. Dies veranlasste uns zur Einführung und Evaluierung eines neuen kostenlosen PET/CT-Bildbetrachtungssystems, das vom Universitätsnetz zur Verfügung gestellt wird.

Methoden Wir baten unsere Studierenden, den Multiplattform-Fiji-Viewer mit dem Beth-Israel-PET/CT-Plugin (http://petctviewer.org) auf ihren PCs zu installieren und Verbindung zur zentralen Bilddatenbank im Universitätsnetz herzustellen, die auf dem frei zugänglichen Orthanc-Server (https://orthanc-server.com) basiert. Am Ende des Kurses führten wir eine anonyme Studentenbefragung durch.

Ergebnisse Das neue System war innerhalb von 8 Tagen online, einschließlich der behördlichen Genehmigung. Alle 76 Studenten (100 %) im fünften Ausbildungsjahr schlossen ihren Kurs ab und bearbeiteten wie geplant 5 anonymisierte PET/CT-Fälle. 41 (53 %) Studenten beantworteten die Umfrage. Fiji wies bei der Installation Probleme auf mit einer durchschnittlichen Bewertung von 1,8 auf einer 5er-Likert-Skala (5 = einfach, 1 = schwierig). Fiji war schwieriger zu bedienen (Bewertung 3,0) als die zuvor im Krankenhaus verwendeten diagnostischen Arbeitsplätze (Bewertung 4,1; p < 0,001; gepaarter t-Test). Trotz der technischen Herausforderung gaben 47 % der Studierenden an, viel gelernt zu haben (Bewertung 4 und 5); nur 11 % gaben negative Bewertungen ab (Bewertung 1 und 2). 51 % fanden die PET/CT-Aufgaben ansprechend (Bewertung 4 und 5), während 20 % die Bewertung 2 und 5 % die Bewertung 1 abgaben.

Schlussfolgerung Trotz der anfänglichen technischen Herausforderung war das „praktische“ Erlernen von PET/CT auf der Basis der Freeware Fiji/Orthanc-PET/CT-Viewer mit einem hohen Grad an Zufriedenheit der Studierenden verbunden. Wir planen, das System weiter zu betreiben, um den Studenten unabhängig von Zeit und Ort einen permanenten Zugang zu PET/CT-Fällen im nativen Format zu ermöglichen.

Key words

nuclear medicine - PET/CT - e-learning - medical education

Schlüsselwörter

Nuklearmedizin - PET/CT - E-Learning - medizinische Ausbildung

Supplement



Publication History

Received: 29 June 2020

Accepted: 13 September 2020

Article published online:
26 October 2020

© 2020. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Gulati A, Schwarzlmüller T, du Plessis E. et al. Evaluation of a new e-learning framework for teaching nuclear medicine and radiology to undergraduate medical students. Acta Radiol Open 2019; 8: 2058460119860231
    CrossrefPubMedSearch in Google Scholar
  • 2 Heinzel A, Mottaghy FM, Brenner W. et al. The teaching of nuclear medicine in Germany. Nuklearmedizin 2020; 59: 38-46
    Thieme ConnectPubMedSearch in Google Scholar
  • 3 Lütje S, Marinova M, Kütting D. et al. Nuclear medicine in SARS-CoV-2 pandemia: 18F-FDG-PET/CT to visualize COVID-19. Nuklearmedizin 2020; 59: 276-280
    Thieme ConnectPubMedSearch in Google Scholar
  • 4 Freudenberg LS, Dittmer U, Herrmann K. Impact of COVID-19 on Nuclear Medicine in Germany, Austria and Switzerland: An International Survey in April 2020. Nuklearmedizin [Internet]. 2020 [cited 2020 Aug 5]; Available from: http://www.thieme-connect.de/DOI/DOI?10.1055/a-1163-3096
  • 5 Biermann M. A national e-learning platform for training specialists in Nuclear Medicine in Norway. UPED-Skrift 2016; 20156: 7
    Search in Google Scholar
  • 6 Haslerud T, Tulipan AJ, Gray RM. et al. E-learning for medical imaging specialists: introducing blended learning in a nuclear medicine specialist course. Acta Radiologica Open 2017; 6: 205846011772085
    CrossrefPubMedSearch in Google Scholar
  • 7 Cypess AM, Lehman S, Williams G. et al. Identification and Importance of Brown Adipose Tissue in Adult Humans. New England Journal of Medicine 2009; 360: 1509-1517
    CrossrefPubMedSearch in Google Scholar
  • 8 Kanoun S, Tal I, Berriolo-Riedinger A. et al. Influence of Software Tool and Methodological Aspects of Total Metabolic Tumor Volume Calculation on Baseline [18F]FDG PET to Predict Survival in Hodgkin Lymphoma. Chen C-T, editor. PLOS ONE 2015; 10: e0140830
    CrossrefPubMedSearch in Google Scholar
  • 9 Rossi C, Kanoun S, Berriolo-Riedinger A. et al. Interim 18F-FDG PET SUVmax Reduction Is Superior to Visual Analysis in Predicting Outcome Early in Hodgkin Lymphoma Patients. J Nucl Med 2014; 55: 1-5
    CrossrefPubMedSearch in Google Scholar
  • 10 Schindelin J, Arganda-Carreras I, Frise E. et al. Fiji: an open-source platform for biological-image analysis. Nature Methods 2012; 9: 676-682
    CrossrefPubMedSearch in Google Scholar
  • 11 Biermann M, Kråkenes J, Brauckhoff K. et al. Post-PET ultrasound improves specificity of 18F-FDG-PET for recurrent differentiated thyroid cancer while maintaining sensitivity. Acta Radiol 2015; 56: 1350-1360
    CrossrefPubMedSearch in Google Scholar
  • 12 Heinzel A, Marienhagen J, Yekta-Michael SS. et al. Pilot study of a newly developed eLearning tool to teach CT and PET/CT in radiology and nuclear medicine. Nuklearmedizin 2020; 59: 79-84
    Thieme ConnectPubMedSearch in Google Scholar
  • 13 Haagensen R, Olsen KA. RABILDA. Evaluation of an eLearning System for Radiology. Norwegian Informatics Conference, Oslo/Norway 2003. Available from: http://www.nik.no/2003/Bidrag/Haagensen.pdf
  • 14 Cole MT, Shelley DJ, Swartz LB. Online instruction, e-learning, and student satisfaction: A three year study. The International Review of Research in Open and Distributed Learning [Internet] 2014 [cited 2020 Jun 20]; 15. Available from: http://www.irrodl.org/index.php/irrodl/article/view/1748
  • 15 Barkley EF. Student engagement techniques: a handbook for college faculty. San Francisco: Jossey-Bass; 2010
    Search in Google Scholar
  • 16 Ke F, Kwak D. Constructs of Student-Centered Online Learning on Learning Satisfaction of a Diverse Online Student Body: A Structural Equation Modeling Approach. Journal of Educational Computing Research 2013; 48: 97-122
    CrossrefSearch in Google Scholar
  • 17 Fink LD. Creating significant learning experiences: an integrated approach to designing college courses. San Francisco: Jossey-Bass; 2013
    Search in Google Scholar
  • 18 Ravesloot CJ, van der Schaaf MF, Kruitwagen CLJJ. et al. Predictors of Knowledge and Image Interpretation Skill Development in Radiology Residents. Radiology 2017; 284: 758-765
    CrossrefPubMedSearch in Google Scholar
  • 19 Ravesloot CJ, van der Schaaf MF, van Schaik JPJ. et al. Volumetric CT-images improve testing of radiological image interpretation skills. Eur J Radiol 2015; 84: 856-861
    CrossrefPubMedSearch in Google Scholar
  • 20 Gulati A, Schwarzlmüller T, du Plessis E. et al. Evaluation of a new e-learning framework for teaching nuclear medicine and radiology to undergraduate medical students. Acta Radiologica Open 2019; 8: 205846011986023
    CrossrefPubMedSearch in Google Scholar
  • 21 den Boer L, van der Schaaf MF, Vincken KL. et al. Volumetric image interpretation in radiology: scroll behavior and cognitive processes. Advances in Health Sciences Education 2018; 23: 783-802
    CrossrefPubMedSearch in Google Scholar
  • 22 van der Gijp A, Ravesloot CJ, van der Schaaf MF. et al. Volumetric and two-dimensional image interpretation show different cognitive processes in learners. Acad Radiol 2015; 22: 632-639
    CrossrefPubMedSearch in Google Scholar