Purpose
To investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour
data and to ascertain reliable perfusion parameters through a model selection process
and a stability test.
Methods
DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts
model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the
extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to
assess overfitting of data by the other models.
For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model
selection map. The variability of each pharmacokinetic parameter extracted from this
map was assessed with a noise propagation procedure, resulting in voxel-wise
distributions of the coefficient of variation (CV).
Results
The model selection map over all patients showed NEM had the best fit in 35.5% of
voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (<0.1%). In
analysing the reliability of Ktrans, when considering regions with a CV<20%, ≈25% of
voxels were found to be stable across all patients. The remaining 75% of voxels were
considered unreliable.
Conclusions
The majority of studies quantifying DCE-MRI data in brain tumours only consider a
single model and whole-tumour statistics for the output parameters. Appropriate model
selection, considering tissue biology and its effects on blood brain barrier permeability
and exchange conditions, together with an analysis on the reliability and stability of the
calculated parameters, is critical in processing robust brain tumour DCE-MRI data.