Why the study?
Parameter estimates from multi-compartment exchange models in myocardial perfusion MR are often unreliable due to model complexity, low signal-to-noise ratio, limited temporal resolution, acquisition length, and imaging artefacts.
Population
In silico data and in vivo patient dynamic contrast-enhanced MR data
Comparison
Hierarchical Bayesian inference scheme vs standard non-linear least squares fitting
Design
Methodological development and validation study in silico and in vivo
Authors
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May warrant caution applying complex kinetic models to CMR perfusion data; leaves open need for robust estimation methods.
A novel Bayesian inference scheme improves the reliability of parameter estimation for two-compartment exchange models in myocardial perfusion MRI compared to standard fitting methods.
Scannell et al. (2019) studied this question.
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