Work

On decay chains and bathtubs: Democratising computational modelling for the radiation sciences

Year: 2026

Type: article

Abstract: INTRODUCTION: Computational modelling can deepen understanding of topics such as radiotracer pharmacokinetics, internal dosimetry, and decay-chain processes. However, modelling is often regarded as the exclusive domain of medical physicists and is not routinely included in the training of other medical professionals, including those in the radiation sciences such as radiographers, physicians, radiation therapists and nuclear medicine technologists. Using the intuitive bathtub analogy of system dynamics, we facilitate the inclusion of computational modelling in the training of healthcare professionals. METHODS: Tc generator in an examination question. RESULTS: Parent and daughter activity-time curves generated with system dynamics agree closely with the predictions of the Bateman equations. Student performance in modelling transient equilibrium under examination conditions using system dynamics (bathtub dynamics), demonstrates the accessibility of this approach. DISCUSSION: The highly visual and intuitive system dynamics approach facilitates modelling of complex processes including multi-compartment dynamics, tracer kinetics, internal dosimetry, and radionuclide generators. This is particularly relevant for students who lack the requisite quantitative training to engage with differential equations. CONCLUSION: System dynamics is a promising tool for democratising access to computational modelling among trainees and professionals in the radiation sciences. PLAIN LANGUAGE SUMMARY: Computer models can help people understand how radioactive medicines behave in the body. This study explored a simple teaching approach that uses the familiar example of a bathtub filling and emptying to help students learn modelling concepts, and tested it using an example from nuclear medicine education. This study found that students were able to use this approach to model complex radioactive decay processes, with results that closely matched established scientific methods. This matters because it could help a wider range of healthcare students and professionals learn important modelling skills without needing advanced mathematics.

Source: Journal of medical imaging and radiation sciences

DOI: 10.1016/j.jmir.2026.102551