Keisuke Matsubara

Impact of spillover from white matter by partial volume effect on quantification of amyloid deposition with [(11)C]PiB PET.

High non-specific uptake of [11C]Pittsburgh compound B ([11C]PiB) in white matter and signal spillover from white matter, due to partial volume effects, confound radioactivity measured in positron emission tomography (PET) with [11C]PiB. We aimed to reveal the partial volume effect in absolute values of kinetic parameters for [11C]PiB, in terms of spillover from white matter. Dynamic data acquired in [11C]PiB PET scans with five healthy volunteers and eight patients with Alzheimers disease were corrected with region-based and voxel-based partial volume corrections. Binding potential (BPND) was estimated using the two-tissue compartment model analysis with a plasma input function. Partial volume corrections significantly decreased cortical BPND values. The degree of decrease in healthy volunteers (-52.7±5.8%) was larger than that in Alzheimers disease patients (-11.9±4.2%). The simulation demonstrated that white matter spillover signals due to the partial volume effect resulted in an overestimation of cortical BPND, with a greater degree of overestimation for lower BPND values. Thus, an overestimation due to partial volume effects is more severe in healthy volunteers than in Alzheimers disease patients. Partial volume corrections may be useful for accurately quantifying Aβ deposition in cortical regions.

A comparison of five partial volume correction methods for Tau and Amyloid PET imaging with [18F]THK5351 and [11C]PIB

PurposeTo suppress partial volume effect (PVE) in brain PET, there have been many algorithms proposed. However, each methodology has different property due to its assumption and algorithms. Our aim of this study was to investigate the difference among partial volume correction (PVC) method for tau and amyloid PET study.MethodsWe investigated two of the most commonly used PVC methods, Müller-Gärtner (MG) and geometric transfer matrix (GTM) and also other three methods for clinical tau and amyloid PET imaging. One healthy control (HC) and one Alzheimer’s disease (AD) PET studies of both [18F]THK5351 and [11C]PIB were performed using a Eminence STARGATE scanner (Shimadzu Inc., Kyoto, Japan). All PET images were corrected for PVE by MG, GTM, Labbé (LABBE), Regional voxel-based (RBV), and Iterative Yang (IY) methods, with segmented or parcellated anatomical information processed by FreeSurfer, derived from individual MR images. PVC results of 5 algorithms were compared with the uncorrected data.ResultsIn regions of high uptake of [18F]THK5351 and [11C]PIB, different PVCs demonstrated different SUVRs. The degree of difference between PVE uncorrected and corrected depends on not only PVC algorithm but also type of tracer and subject condition.ConclusionPresented PVC methods are straight-forward to implement but the corrected images require careful interpretation as different methods result in different levels of recovery.

Reliability of CT Perfusion-Derived CBF in Relation to Hemodynamic Compromise in Patients with Cerebrovascular Steno-Occlusive Disease: A Comparative Study with 15O PET

In the bolus tracking technique with computed tomography (CT) or magnetic resonance imaging, cerebral blood flow (CBF) is computed from deconvolution analysis, but its accuracy is unclear. To evaluate the reliability of CT perfusion (CTP)-derived CBF, we examined 27 patients with symptomatic or asymptomatic unilateral cerebrovascular steno-occlusive disease. Results from three deconvolution algorithms, standard singular value decomposition (sSVD), delay-corrected SVD (dSVD), and block-circulant SVD (cSVD), were compared with 15O positron emission tomography (PET) as a reference standard. To investigate CBF errors associated with the deconvolution analysis, differences in lesion-to-normal CBF ratios between PET and CTP were correlated with prolongation of arterial-tissue delay (ATD) and mean transit time (MTT) in the lesion hemisphere. Computed tomography perfusion results strongly depended on the deconvolution algorithms used. Standard singular value decomposition showed ATD-dependent underestimation of CBF ratio, whereas cSVD showed overestimation of the CBF ratio when MTT was severely prolonged in the lesions. The computer simulations reproduced the trend observed in patients. Deconvolution by dSVD can provide lesion-to-normal CBF ratios less dependent on ATD and MTT, but requires accurate ATD maps in advance. A practical and accurate method for CTP is required to assess CBF in patients with MTT-prolonged regions.

Influence of O-Methylated Metabolite Penetrating the Blood–Brain Barrier to Estimation of Dopamine Synthesis Capacity in Human L-[β-11C]DOPA PET

O-methyl metabolite (L-[β-11C]OMD) of 11C-labeled L-3,4-dihydroxyphenylalanine (L-[β-11C]DOPA) can penetrate into brain tissue through the blood–brain barrier, and can complicate the estimation of dopamine synthesis capacity by positron emission tomography (PET) study with L-[β-11C]DOPA. We evaluated the impact of L-[β-11C]OMD on the estimation of the dopamine synthesis capacity in a human L-[β-11C]DOPA PET study. The metabolite correction with mathematical modeling of L-[β-11C]OMD kinetics in a reference region without decarboxylation and further metabolism, proposed by a previous [18F]FDOPA PET study, were implemented to estimate radioactivity of tissue L-[β-11C]OMD in 10 normal volunteers. The component of L-[β-11C]OMD in tissue time-activity curves (TACs) in 10 regions were subtracted by the estimated radioactivity of L-[β-11C]OMD. To evaluate the influence of omitting blood sampling and metabolite correction, relative dopamine synthesis rate (kref) was estimated by Gjedde–Patlak analysis with reference tissue input function, as well as the net dopamine synthesis rate (Ki) by Gjedde–Patlak analysis with the arterial input function and TAC without and with metabolite correction. Overestimation of Ki was observed without metabolite correction. However, the kref and Ki with metabolite correction were significantly correlated. These data suggest that the influence of L-[β-11C]OMD is minimal for the estimation of kref as dopamine synthesis capacity.

Bootstrap methods for estimating PET image noise: experimental validation and an application to evaluation of image reconstruction algorithms

ObjectiveAccurate and validated methods for estimating regional PET image noise are helpful for optimizing image processing. The bootstrap is a data-based simulation method for statistical inference, which can be used to estimate the PET image noise without repeated measurements. The aim of this study was to experimentally validate bootstrap-based methods as a tool for estimating PET image noise and demonstrate its usefulness for evaluating image reconstruction algorithms.MethodsTwo bootstrap-based method, the list-mode data bootstrap (LMBS) and the sinogram bootstrap (SNBS), were implemented on a clinical PET scanner. A uniform cylindrical phantom filled with 18F solution was scanned using list-mode acquisition. A reference standard deviation (SD) map was calculated from 60 statistically independent measured list-mode data. Using one of the 60 list-mode data, 60 bootstrap replicates were generated and used to calculate bootstrap SD maps. Brain 18F-FDG data from a healthy volunteer were also processed as an example of the bootstrap application. Three reconstruction algorithms, FBP 2D and both 2D and 3D versions of dynamic row-action maximum likelihood algorithm (DRAMA), were assessed.ResultsFor all the reconstruction algorithms used, the bootstrap SD maps agreed well with the reference SD map, confirming the validity of the bootstrap methods for assessing image noise. The two bootstrap methods were equivalent with respect to the performance of image noise estimation. The bootstrap analysis of the FDG data showed the better contrast–noise relation curve for DRAMA 3D compared to DRAMA 2D and FBP 2D.ConclusionsThe bootstrap methods provide the estimates of image noise for various reconstruction algorithms with reasonable accuracy, require only a single measurement, not repeated measures, and are, therefore, applicable for a human PET study.

Erratum to “Impact of spillover from white matter by partial volume effect on quantification of amyloid deposition with [ 11 C]PiB PET” [NeuroImage 143 (2016) 316–324]

a Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita, Japan b Department of Functional Brain Imaging Research (DOFI), National Institute of Radiological Sciences (NIRS), National Institute for Quantum and Radiological Science and Technology (QST), Japan c Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences (NIRS), National Institute for Quantum and Radiological Science and Technology (QST), Japan d Department of Radiology and Nuclear Medicine, Fukushima Medical University, Japan

Sensitivity of FDOPA kinetic macro-parameters to changes in Parkinson's disease: Evaluation for noise influence in [18F]FDOPA PET data

Department of Bioinfomatics and Genomics, Graduate School of Information Science, Nara Institute of Science and Technology, Japan Department of Molecular Imaging in Medicine, Graduate School of Medicine, Osaka University, Japan Functional Probe Research Laboratory, RIKEN Center for Molecular Imaging Science, Japan Department of Investigative Radiology, Advanced-Medical Engineering Center, National Cardiovascular Center, Japan