  Recent works have demonstrated the added value of dynamic amino acid positron
emission tomography (PET) for glioma grading and genotyping, biopsy targeting,
and recurrence diagnosis. However, most of these studies are exclusively based
on hand-crafted qualitative or semi-quantitative dynamic features extracted
from the mean time activity curve (TAC) within predefined volumes. Voxelwise
dynamic PET data analysis could instead provide a better insight into
intra-tumour heterogeneity of gliomas. In this work, we investigate the ability
of the widely used principal component analysis (PCA) method to extract
meaningful quantitative dynamic features from high-dimensional motion-corrected
dynamic [S-methyl-11C]methionine PET data in a first cohort of 20 glioma
patients. By means of realistic numerical simulations, we demonstrate the
robustness of our methodology to noise. In a second cohort of 13 glioma
patients, we compare the resulting parametric maps to these provided by
standard one- and two-tissue compartment pharmacokinetic (PK) models. We show
that our PCA model outperforms PK models in the identification of intra-tumour
uptake dynamics heterogeneity while being much less computationally expensive.
Such parametric maps could be valuable to assess tumour aggressiveness locally
with applications in treatment planning as well as in the evaluation of tumour
progression and response to treatment. This work also provides further
encouraging results on the added value of dynamic over static analysis of
[S-methyl-11C]methionine PET data in gliomas, as previously demonstrated for
O-(2-[18F]fluoroethyl)-L-tyrosine.
