AI Article Synopsis
- Tracer kinetic modeling through dynamic PET is crucial for accurate functional imaging in Nuclear Medicine, but it's often limited by complexity and high computational costs.
- Machine learning can enhance this process by predicting arterial input function, kinetic modeling parameters, and supporting model selection, ultimately speeding up the workflow in clinical and preclinical studies.
- This review outlines the fundamentals of tracer kinetic modeling and summarizes existing literature on the application of machine learning techniques in this area.
Article Abstract
Tracer kinetic modelling based on dynamic PET is an important field of Nuclear Medicine for quantitative functional imaging. Yet, its implementation in clinical routine has been constrained by its complexity and computational costs. Machine learning poses an opportunity to improve modelling processes in terms of arterial input function prediction, the prediction of kinetic modelling parameters and model selection in both clinical and preclinical studies while reducing processing time. Moreover, it can help improving kinetic modelling data used in downstream tasks such as tumor detection. In this review, we introduce the basics of tracer kinetic modelling and present a literature review of original works and conference papers using machine learning methods in this field.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10709136 | PMC |
| http://dx.doi.org/10.1055/a-2179-5818 | DOI Listing |
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