Magnetic Resonance Simulation in Education: Quantitative Evaluation of an Actual Classroom Experience

Abstract

Magnetic resonance is an imaging modality that implies a high complexity for radiographers. Despite some simulators having been developed for training purposes, we are not aware of any attempt to quantitatively measure their educational performance. The present study gives an answer to the question: Does an MRI simulator built on specific functional and non-functional requirements help radiographers learn MRI theoretical and practical concepts better than traditional educational method based on lectures? Our study was carried out in a single day by a total of 60 students of a main hospital in Madrid, Spain. The experiment followed a randomized pre-test post-test design with a control group that used a traditional educational method, and an experimental group that used our simulator. Knowledge level was assessed by means of an instrument with evidence of validity in its format and content, while its reliability was analyzed after the experiment. Statistical differences between both groups were measured. Significant statistical differences were found in favor of the participants who used the simulator for both the post-test score and the gain (difference between post-test and pre-test scores). The effect size turned out to be significant as well. In this work we evaluated a magnetic resonance simulation paradigm as a tool to help in the training of radiographers. The study shows that a simulator built on specific design requirements is a valuable complement to traditional education procedures, backed up with significant quantitative results.

KomaMRI.jl: An open‐source framework for general MRI simulations with GPU acceleration

“…3,4 Moreover, simulations are an excellent tool for education and training, as hands-on experience is a great way to assimilate the theoretical and practical components of MRI. [5][6][7] MRI simulators can be application-specific or general. Application-specific simulators are efficient computationally and only consider a few relevant effects (e.g., POSSUM, 8,9 CAMINO, 10 and others [11][12][13] ).…”

“…Additionally, with the increasing use of Machine Learning models, simulation becomes even more relevant, because it can be used to generate synthetic data for training, 1,2 or to construct signal dictionaries to infer quantitative measurements from the acquired data 3,4 . Moreover, simulations are an excellent tool for education and training, as hands‐on experience is a great way to assimilate the theoretical and practical components of MRI 5‐7 …”

“… 3 , 4 Moreover, simulations are an excellent tool for education and training, as hands‐on experience is a great way to assimilate the theoretical and practical components of MRI. 5 , 6 , 7 …”