RESUMEN
PURPOSE: The analysis of the [11C]PiB-PET amyloid images of a unique Asian cohort of 186 participants featuring overlapping vascular diseases raised the question about the validity of current standards for amyloid quantification under abnormal conditions. In this work, we implemented a novel pipeline for improved amyloid PET quantification of this atypical cohort. METHODS: The investigated data correction and amyloid quantification methods included motion correction, standardized uptake value ratio (SUVr) quantification using the parcellated MRI (standard method) and SUVr quantification without MRI. We introduced a novel amyloid analysis method yielding 2 biomarkers: AßL which quantifies the global Aß burden and ns that characterizes the non-specific uptake. Cut-off points were first determined using visual assessment as ground truth and then using unsupervised classification techniques. RESULTS: Subject's motion impacts the accuracy of the measurement outcome but has however a limited effect on the visual rating and cut-off point determination. SUVr computation can be reliably performed for all the subjects without MRI parcellation while, when required, the parcellation failed or was of mediocre quality in 10% of the cases. The novel biomarker AßL showed an association increase of 29.5% with the cognitive tests and increased effect size between positive and negative scans compared with SUVr. ns was found sensitive to cerebral microbleeds, white matter hyperintensity, volume, and age. The cut-off points for SUVr using parcellated MRI, SUVr without parcellation, and AßL were 1.56, 1.39, and 25.5. Finally, k-means produced valid cut-off points without the requirement of visual assessment. CONCLUSION: The optimal processing for the amyloid quantification of this atypical cohort allows the quantification of all the subjects, producing SUVr values and two novel biomarkers: AßL, showing important increased in their association with various cognitive tests, and ns, a parameter sensitive to non-specific retention variations caused by age and cerebrovascular diseases.
Asunto(s)
Enfermedad de Alzheimer , Trastornos Cerebrovasculares , Amiloide , Péptidos beta-Amiloides , Compuestos de Anilina , Biomarcadores , Trastornos Cerebrovasculares/diagnóstico por imagen , Humanos , Tomografía de Emisión de PositronesRESUMEN
Head motion occurring during brain PET studies leads to image blurring and to bias in measured local quantities. The objective of this work was to implement a correction method for PET data acquired with the mMR synchronous PET/MR scanner. Methods: A list-mode-based motion-correction approach has been designed. The developed rebinner chronologically reads the recorded events from the Siemens list-mode file, applies the estimated geometric transformations, and frames the detected counts into sinograms. The rigid-body motion parameters were estimated from an initial dynamic reconstruction of the PET data. We then optimized the correction for 11C-Pittsburgh compound B (11C-PIB) scans using simulated and actual data with well-controlled motion. Results: An efficient list-mode-based motion correction approach has been implemented, fully optimized, and validated using simulated and actual PET data. The average spatial resolution loss induced by inaccuracies in motion parameter estimates and by the rebinning process was estimated to correspond to a 1-mm increase in full width at half maximum with motion parameters estimated directly from the PET data with a temporal frequency of 20 s. The results show that the rebinner can be safely applied to the 11C-PIB scans, allowing almost complete removal of motion-induced artifacts. The application of the correction method to a large cohort of 11C-PIB scans led to the following observations: first, that more than 21% of the scans were affected by motion greater than 10 mm (39% for subjects with Mini-Mental State Examination scores below 20), and second, that the correction led to quantitative changes in Alzheimer-specific cortical regions of up to 30%. Conclusion: The rebinner allows accurate motion correction at a cost of minimal resolution reduction. Application of the correction to a large cohort of 11C-PIB scans confirmed the necessity of systematically correcting for motion to obtain quantitative results.
Asunto(s)
Benzotiazoles , Tomografía de Emisión de Positrones/estadística & datos numéricos , Radiofármacos , Compuestos de Anilina , Encéfalo/diagnóstico por imagen , Radioisótopos de Carbono , Estudios de Cohortes , Simulación por Computador , Movimientos de la Cabeza , Humanos , Interpretación de Imagen Asistida por Computador/estadística & datos numéricos , Imagen por Resonancia Magnética/instrumentación , Imagen por Resonancia Magnética/estadística & datos numéricos , Movimiento (Física) , Imagen Multimodal/instrumentación , Imagen Multimodal/estadística & datos numéricos , Neuroimagen/instrumentación , Neuroimagen/estadística & datos numéricos , Tomografía de Emisión de Positrones/instrumentación , TiazolesRESUMEN
We characterized the relationship between the plasma concentration of the phospodiesterase (PDE)-4 inhibitor GSK356278 and occupancy of the PDE4 enzyme in the brain of healthy volunteers, using the positron emission tomography (PET) tracer [11C](R)-rolipram. To this end, PET scans were acquired in eight male volunteers before and at 3 and 8 h after a single 14 mg oral dose of GSK356278. A metabolite-corrected arterial input function was used in conjunction with the dynamic PET emission data to estimate volumes of distribution (VT) from a two-tissue compartment model. The administration of GSK356278 reduced [11C](R)-rolipram whole brain VT by 17% at 3 h post-dose (p = 0.01) and by 4% at 8 h post-dose. The mean plasma Cmax was 42.3 ng/ml, leading to a PDE4 occupancy of 48% at Tmax. The in vivo affinity of GSK356278 was estimated as EC50 = 46 ± 3.6 ng/ml. We present the first report of a direct estimation of PDE4 blockade in the living human brain. In vivo affinity of GSK356278 for the PDE4, estimated in this early phase study, was combined with GSK356278 safety and tolerability data to decide on a therapeutic dose for future clinical development.