Objective.To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [¹¹C]raclopride positron emission tomography (PET) and T1-weighted magnetic resonance imaging (MRI) data.Approach.The biological heterogeneity of Parkinsonian syndromes renders their statistical classification a challenge. The unique combination of structural and molecular imaging data allowed different classifier designs to be tested. Datasets from dynamic [¹¹C]raclopride PET and T1-weighted MRI scans were acquired from six groups of participants. There were healthy controls (CTRLn= 15), patients with Parkinson’s disease (PDn= 27), multiple system atrophy (MSAn= 8), corticobasal degeneration (CBDn= 6), and dementia with Lewy bodies (DLBn= 5). MSA, CBD, and DLB patients were classified into one category designated as atypical Parkinsonism (AP). The distribution volume ratio (DVR) kinetic parameters obtained from the PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). The grey matter (GM) volumes obtained from the MRI data were used to quantify GM atrophy across cortical, subcortical, and cerebellar ROI.Results.The classifiers CTRL vs PD and CTRL vs AP achieved the highest balanced accuracy combining DVR and GM (DVR-GM) features (96.7%, 92.1%, respectively), followed by the classifiers designed with DVR features (93.3%, 88.8%, respectively), and GM features (69.6%, 86.1%, respectively). In contrast, the classifier PD vs AP showed the highest balanced accuracy (78.9%) using DVR features only. The integration of DVR-GM (77.9%) and GM features (72.7%) produced inferior performances. The classifier CTRL vs PD vs AP showed high weighted balanced accuracy when DVR (80.5%) or DVR-GM features (79.9%) were integrated. GM features revealed poorer performance (59.5%).Significance.This work was unique in its combination of structural and molecular imaging features in binary and triple category classifications. We were able to demonstrate improved binary classification of healthy/diseased status (concerning both PD and AP) and equate performance to DVR features in multiclass classifications.

Positron emission tomography (PET) neuroimaging with the Pittsburgh Compound_B (PiB) is widely used to assess amyloid plaque burden. Standard quantification approaches normalize PiB-PET by mean cerebellar gray matter uptake. Previous studies suggested similar pons and white-matter uptake in Alzheimer's disease (AD) and healthy controls (HC), but lack exhaustive comparison of normalization across the three regions, with data-driven diagnostic classification. We aimed to compare the impact of distinct reference regions in normalization, measured by data-driven statistical analysis, and correlation with cerebrospinal fluid (CSF) amyloid β (Aβ) species concentrations. 243 individuals with clinical diagnosis of AD, HC, mild cognitive impairment (MCI) and other dementias, from the Biomarkers for Alzheimer's/Parkinson's Disease (BIOMARKAPD) initiative were included. PiB-PET images and CSF concentrations of Aβ38, Aβ40 and Aβ42 were submitted to classification using support vector machines. Voxel-wise group differences and correlations between normalized PiB-PET images and CSF Aβ concentrations were calculated. Normalization by cerebellar gray matter and pons yielded identical classification accuracy of AD (accuracy-96%, sensitivity-96%, specificity-95%), and significantly higher than Aβ concentrations (best accuracy 91%). Normalization by the white-matter showed decreased extent of statistically significant multivoxel patterns and was the only method not outperforming CSF biomarkers, suggesting statistical inferiority. Aβ38 and Aβ40 correlated negatively with PiB-PET images normalized by the white-matter, corroborating previous observations of correlations with non-AD-specific subcortical changes in white-matter. In general, when using the pons as reference region, higher voxel-wise group differences and stronger correlation with Aβ42, the Aβ42/Aβ40 or Aβ42/Aβ38 ratios were found compared to normalization based on cerebellar gray matter.

Two years after the initial diagnosis, disease relapse was documented by a sudden rise in serum chromogranin A (588.1 ng/mL, n<85) and a positive [⁶⁸Ga]Ga-DOTA-NOC ((1,4,7,10-tetraazacyclododecane-N,N’,N’’,N’’’-tetraacetic acid)-1-NaI3-octreotide) PET/CT scan that showed a 25 mm high uptake liver lesion in segment II with a maximum standardized uptake value (SUVmax:) of 80.5. Elective liver metastasectomy was performed and histological examination confirmed a secondary neuroendocrine lesion with a more aggressive Ki67 proliferation index (Ki67=15%, 7 mitoses/10 HPF). Subsequently, a decrease in chromogranin A was observed.

Usually, PET images are evaluated statically and qualitatively, seeking for regions with abnormally high or low uptake. Quantification allows for a more thorough understanding of the physiological process, benefiting not only diagnoses but also allowing for longitudinal studies and inter subject normalization. Quantification can be performed using compartmental analysis that requires the knowledge of concentration of the radiotracer present in the arterial blood at each instant - the arterial input function (AIF). Measuring the AIF is usually an invasive and challenging process, making this a real obstacle to performing in patients and in healthy volunteers. In this work, a prototype for measuring the AIF non-invasively is presented. The system is composed by several gamma detectors that are placed near superficial arteries. The main idea is to obtain signals from arteries and their surroundings using different locations and combining the signals in order to accurately estimate the AIF. We assembled proportional radiation detectors comprising essentially a bismuth germanium oxide (BGO) crystal and a photodiode. The first prototypes are being tested for energy resolution, efficiency, linearity and adaptation to the patient body during real PET exams. Energy resolution was estimated to be 14% FWHM (Full Width at Half Maximum), efficiency 1.4% and proportionality between response and activity of radioactivity sources was observed. The detectors were placed on the wrist and close to the ankle in two patients that underwent PET scans. Measures from the wrist are compatible to the AIF but movements of the patients show that shielding the detectors is necessary to prevent contamination from other sources other than the radial artery. Regarding the ankle detector, it is difficult to have it fixed during the exam, which has yet to be tackled. Results so far permit to conclude that the system exhibits suitable characteristics for measuring the AIF for PET quantification.