[en] A synthetic procedure for the routine preparation of [¹⁸F] N-3-fluoropropyl-2-β-carbomethoxy-3-β-(4' methylphenyl) nortropane (¹⁸F FPCMT) has been developed. The synthesis is based on alkylation of nortropane with ¹⁸F labeled fluoropropyl tosylate. Purification of the final product was achieved by a preparative HPLC procedure using Alltech Econosil column. Separation of the desired compound was achieved and the product was clean. The radiochemical yield (without decay correction) is 4 to 5%, calculated at the end of the synthesis based on the total amount of fluorine recovered from the target. Radiochemical purity was in the range of 98 to 99%

[en] A procedure for the routine preparation of [¹⁸F]FP-CIT has been developed. Purification of the final product was achieved by preparative HPLC using phenethyl column without decomposition or epimerization. [¹⁸F] labeled-N-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane was prepared and PET imaging was performed on human subjects. A high uptake into striatal regions was observed. HPLC plasma analysis using [¹⁸F]FP-CIT indicated the presence of only one metabolite. By directly comparing the behavior of these three radiotracers ([¹⁸F]DOPA, [¹²³I]FP-CIT, and [¹⁸F]FP-CIT) in the same subjects, we can enhance our understanding of the dopaminergic system as well as the relative potential of these techniques in a clinical research setting

[en] A synthetic procedure for the preparation of [¹⁸F]FPCBT, an imaging agent for the dopamine transporter (DAT), has been developed. The radiosynthesis was carried out in a two step procedure. Even though the yield was low, we were able to prepare 20 to 30mCi of the product, which was enough for two or three studies. The radiochemical purity was greater than 96%. The in vivo properties of this radiotracer were evaluated using baboon and it showed highest uptake in the striatum. The studies also revealed that the maximum uptake was reached within 7 to 10 minutes post injection. Plasma metabolite analysis indicated that there is only one metabolite and it is less lipophilic than the parent compound. [¹⁸F]FPCBT displayed good brain uptake and its high target to non target ratio indicate that it is a potential candidate for DAT imaging

[en] The purpose of this study was to identify the specific metabolic brain pattern characteristic for Parkinson's disease (PD): Parkinson's disease-related pattern (PDRP), using network analysis of [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images in a cohort of Slovenian PD patients. Twenty PD patients (age 70.1 ± 7.8 years, Movement Disorder Society Unified Parkinson's Disease Motor Rating Scale (MDS-UPDRS-III) 38.3 ± 12.2; disease duration 4.3 ± 4.1 years) and 20 age-matched normal controls (NCs) underwent FDG-PET brain imaging. An automatic voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was applied to these scans for PDRP-Slovenia identification. The pattern was characterized by relative hypermetabolism in pallidum, putamen, thalamus, brain stem, and cerebellum associated with hypometabolism in sensorimotor cortex, posterior parietal, occipital, and frontal cortices. The expression of PDRP-Slovenia discriminated PD patients from NCs (p < 0.0001) and correlated positively with patients' clinical score (MDS-UPDRS-III, p = 0.03). Additionally, its topography agrees well with the original PDRP (p < 0.001) identified in American cohort of PD patients. We validated the PDRP-Slovenia expression on additional FDG-PET scans of 20 PD patients, 20 NCs, and 25 patients with atypical parkinsonism (AP). We confirmed that the expression of PDRP-Slovenia manifests good diagnostic accuracy with specificity and sensitivity of 85-90% at optimal pattern expression cutoff for discrimination of PD patients and NCs and is not expressed in AP. PDRP-Slovenia proves to be a robust and reproducible functional imaging biomarker independent of patient population. It accurately differentiates PD patients from NCs and AP and correlates well with the clinical measure of PD progression. (orig.)

[en] Differentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used ${}^{18}$F-FDG-PET brain imaging combined with an automated image classification algorithm to classify parkinsonian patients as Parkinson’s disease (PD) or as an atypical parkinsonian syndrome (APS) at the time when the clinical diagnosis was still uncertain. In addition to validating the algorithm, we assessed its utility in a “real-life” clinical setting. One hundred thirty-seven parkinsonian patients with uncertain clinical diagnosis underwent ${}^{18}$F-FDG-PET and were classified using an automated image-based algorithm. For 66 patients in cohort A, the algorithm-based diagnoses were compared with their final clinical diagnoses, which were the gold standard for cohort A and were made 2.2 ± 1.1 years (mean ± SD) later by a movement disorder specialist. Seventy-one patients in cohort B were diagnosed by general neurologists, not strictly following diagnostic criteria, 2.5 ± 1.6 years after imaging. The clinical diagnoses were compared with the algorithm-based ones, which were considered the gold standard for cohort B. Image-based automated classification of cohort A resulted in 86.0% sensitivity, 92.3% specificity, 97.4% positive predictive value (PPV), and 66.7% negative predictive value (NPV) for PD, and 84.6% sensitivity, 97.7% specificity, 91.7% PPV, and 95.5% NPV for APS. In cohort B, general neurologists achieved 94.7% sensitivity, 83.3% specificity, 81.8% PPV, and 95.2% NPV for PD, while 88.2%, 76.9%, 71.4%, and 90.9% for APS. The image-based algorithm had a high specificity and the predictive values in classifying patients before a final clinical diagnosis was reached by a specialist. Our data suggest that it may improve the diagnostic accuracy by 10–15% in PD and 20% in APS when a movement disorder specialist is not easily available.

[en] Spatial covariance analysis has been used with FDG PET to identify a specific metabolic network associated with Parkinson's disease (PD). In the current study, we utilized a new, fully automated voxel-based method to quantify network expression in ECD SPECT images from patients with classical PD, patients with multiple system atrophy (MSA), and healthy control subjects. We applied a previously validated voxel-based PD-related covariance pattern (PDRP) to quantify network expression in the ECD SPECT scans of 35 PD patients, 15 age- and disease severity-matched MSA patients, and 35 age-matched healthy control subjects. PDRP scores were compared across groups using analysis of variance. The sensitivity and specificity of the prospectively computed PDRP scores in the differential diagnosis of individual subjects were assessed by receiver operating characteristic (ROC) analysis. PDRP scores were significantly increased (p < 0.001) in the PD group relative to the MSA and control groups. ROC analysis indicated that the overall diagnostic accuracy of the PDRP measures was 0.91 (AUC). The optimal cutoff value was consistent with a sensitivity of 0.97 and a specificity of 0.80 and 0.71 for discriminating PD patients from MSA and normal controls, respectively. Our findings suggest that fully automated voxel-based network assessment techniques can be used to quantify network expression in the ECD SPECT scans of parkinsonian patients. (orig.)

[en] Up to 25% of patients diagnosed as idiopathic Parkinson's disease (IPD) have an atypical parkinsonian syndrome (APS). We had previously validated an automated image-based algorithm to discriminate between IPD, multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). While the algorithm was accurate with respect to the final clinical diagnosis after long-term expert follow-up, its relationship to the initial referral diagnosis and to the neuropathological gold standard is not known. Patients with an uncertain diagnosis of parkinsonism were referred for ${}^{18}$F-fluorodeoxyglucose (FDG) PET to classify patients as IPD or as APS based on the automated algorithm. Patients were followed by a movement disorder specialist and subsequently underwent neuropathological examination. The image-based classification was compared to the neuropathological diagnosis in 15 patients with parkinsonism. At the time of referral to PET, the clinical impression was only 66.7% accurate. The algorithm correctly identified 80% of the cases as IPD or APS (p = 0.02) and 87.5% of the APS cases as MSA or PSP (p = 0.03). The final clinical diagnosis was 93.3% accurate (p < 0.001), but needed several years of expert follow-up. The image-based classifications agreed well with autopsy and can help to improve diagnostic accuracy during the period of clinical uncertainty.