Because of the progressive loss of nigro-striatal dopaminergic terminals in Parkinson's disease (PD), in vivo quantitative imaging of dopamine (DA) containing neurons in animal models of PD is of critical importance in the preclinical evaluation of highly awaited disease-modifying therapies. Among existing methods, the high sensitivity of positron emission tomography (PET) is attractive to achieve that goal. The aim of this study was to perform a quantitative comparison of brain images obtained in 6-hydroxydopamine (6-OHDA) lesioned rats using two dopaminergic PET radiotracers, namely [F-18]fluoro-3,4-dihydroxyphenyl-L-alanine ([F-18]FDOPA) and 6-[F-18]fluoro-L-m-tyrosine ([F-18]FMT). Because the imaging signal is theoretically less contaminated by metabolites, we hypothesized that the latter would show stronger relationship with behavioural and post-mortem measures of striatal dopaminergic deficiency. We used a within-subject design to measure striatal [F-18]FMT and [F-18]FDOPA uptake in eight partially lesioned, eight fully lesioned and ten sham-treated rats. Animals were pretreated with an L-aromatic amino acid decarboxylase inhibitor. A catechol-O-methyl transferase inhibitor was also given before [F-18]FDOPA PET. Quantitative estimates of striatal uptake were computed using conventional graphical Patlak method. Striatal dopaminergic deficiencies were measured with apomorphine-induced rotations and post-mortem striatal DA content. We observed a strong relationship between [F-18]FMT and [F-18]FDOPA estimates of decreased uptake in the denervated striatum using the tissue-derived uptake rate constant K-c. However, only [F-18]FMT K-c succeeded to discriminate between the partial and the full 6-OHDA lesion and correlated well with the post-mortem striatal DA content. This study indicates that the [F-18]FMT could be more sensitive, with respect of [F-18]FDOPA, to investigate DA terminals loss in 6-OHDA rats, and open the way to invivo L-aromatic amino acid decarboxylase activity targeting in future investigations on progressive PD models.