Benjamin A Thomas

The importance of appropriate partial volume correction for PET quantification in Alzheimer's disease.

PurposeAlzheimer’s disease (AD) is the most common form of dementia. Clinically, it is characterized by progressive cognitive and functional impairment with structural hallmarks of cortical atrophy and ventricular expansion. Amyloid plaque aggregation is also known to occur in AD subjects. In-vivo imaging of amyloid plaques is now possible with positron emission tomography (PET) radioligands. PET imaging suffers from a degrading phenomenon known as the partial volume effect (PVE). The quantitative accuracy of PET images is reduced by PVEs primarily due to the limited spatial resolution of the scanner. The degree of PVE is influenced by structure size, with smaller structures tending to suffer from more severe PVEs such as atrophied grey matter regions. The aims of this paper were to investigate the effect of partial volume correction (PVC) on the quantification of amyloid PET and to highlight the importance of selecting an appropriate PVC technique.MethodsAn improved PVC technique, region-based voxel-wise (RBV) correction, was compared against existing Van-Cittert (VC) and Müller-Gärtner (MG) methods using amyloid PET imaging data. Digital phantom data were produced using segmented MRI scans from a control subject and an AD subject. Typical tracer distributions were generated for each of the phantom anatomies. Also examined were 70 clinical PET scans acquired using [18F]flutemetamol. Volume of interest (VOI) analysis was performed for corrected and uncorrected images.ResultsPVC was shown to improve the quantitative accuracy of regional analysis performed on amyloid PET images. Of the corrections applied, VC deconvolution demonstrated the worst recovery of grey matter values. MG PVC was shown to induce biases in some grey matter regions due to grey matter variability. In addition, white matter variability was shown to influence the accuracy of MG PVC in cortical grey matter and also cerebellar grey matter, a typical reference region for amyloid PET normalization in sporadic AD. RBV was shown to be more accurate than MG in terms of grey matter and white matter uptake. An increase in within-group variability after PVC was observed and is believed to be a genuine, more accurate representation of the data rather than a correction-induced error. The standardized uptake value ratio (SUVR) threshold for classifying subjects as either amyloid-positive or amyloid-negative was found to be 1.64 in the uncorrected dataset, rising to 2.25 after PVC.ConclusionCare should be taken when applying PVC to amyloid PET images. Assumptions made in existing PVC strategies can induce biases that could lead to erroneous inferences about uptake in certain regions. The proposed RBV PVC technique accounts for within-compartment variability, with the potential to reduce errors of this kind.

Implementation and Validation of an Adaptive Template Registration Method for 18F-Flutemetamol Imaging Data

The spatial normalization of PET amyloid imaging data is challenging because different white and gray matter patterns of negative (Aβ−) and positive (Aβ+) uptake could lead to systematic bias if a standard method is used. In this study, we propose the use of an adaptive template registration method to overcome this problem. Methods: Data from a phase II study (n = 72) were used to model amyloid deposition with the investigational PET imaging agent 18F-flutemetamol. Linear regression of voxel intensities on the standardized uptake value ratio (SUVR) in a neocortical composite region for all scans gave an intercept image and a slope image. We devised a method where an adaptive template image spanning the uptake range (the most Aβ− to the most Aβ+ image) can be generated through a linear combination of these 2 images and where the optimal template is selected as part of the registration process. We applied the method to the 18F-flutemetamol phase II data using a fixed volume of interest atlas to compute SUVRs. Validation was performed in several steps. The PET-only adaptive template registration method and the MR imaging–based method used in statistical parametric mapping were applied to spatially normalize PET and MR scans, respectively. Resulting transformations were applied to coregistered gray matter probability maps, and the quality of the registrations was assessed visually and quantitatively. For comparison of quantification results with an independent patient-space method, FreeSurfer was used to segment each subject’s MR scan and the parcellations were applied to the coregistered PET scans. We then correlated SUVRs for a composite neocortical region obtained with both methods. Furthermore, to investigate whether the 18F-flutemetamol model could be generalized to 11C-Pittsburgh compound B (11C-PIB), we applied the method to Australian Imaging, Biomarkers and Lifestyle (AIBL) 11C-PIB scans (n = 285) and compared the PET-only neocortical composite score with the corresponding score obtained with a semimanual method that made use of the subject’s MR images for the positioning of regions. Results: Spatial normalization was successful on all scans. Visual and quantitative comparison of the new PET-only method with the MR imaging–based method of statistical parametric mapping indicated that performance was similar in the cortical regions although the new PET-only method showed better registration in the cerebellum and pons reference region area. For the 18F-flutemetamol quantification, there was a strong correlation between the PET-only and FreeSurfer SUVRs (Pearson r = 0.96). We obtained a similar correlation for the AIBL 11C-PIB data (Pearson r = 0.94). Conclusion: The derived adaptive template registration method allows for robust, accurate, and fully automated quantification of uptake for 18F-flutemetamol and 11C-PIB scans without the use of MR imaging data.

Areas of normal pulmonary parenchyma on HRCT exhibit increased FDG PET signal in IPF patients

PurposePatients with idiopathic pulmonary fibrosis (IPF) show increased PET signal at sites of morphological abnormality on high-resolution computed tomography (HRCT). The purpose of this investigation was to investigate the PET signal at sites of normal-appearing lung on HRCT in IPF.MethodsConsecutive IPF patients (22 men, 3 women) were prospectively recruited. The patients underwent 18F-FDG PET/HRCT. The pulmonary imaging findings in the IPF patients were compared to the findings in a control population. Pulmonary uptake of 18F-FDG (mean SUV) was quantified at sites of morphologically normal parenchyma on HRCT. SUVs were also corrected for tissue fraction (TF). The mean SUV in IPF patients was compared with that in 25 controls (patients with lymphoma in remission or suspected paraneoplastic syndrome with normal PET/CT appearances).ResultsThe pulmonary SUV (mean ± SD) uncorrected for TF in the controls was 0.48 ± 0.14 and 0.78 ± 0.24 taken from normal lung regions in IPF patients (p < 0.001). The TF-corrected mean SUV in the controls was 2.24 ± 0.29 and 3.24 ± 0.84 in IPF patients (p < 0.001).ConclusionIPF patients have increased pulmonary uptake of 18F-FDG on PET in areas of lung with a normal morphological appearance on HRCT. This may have implications for determining disease mechanisms and treatment monitoring.

Markov random field and Gaussian mixture for segmented MRI-based partial volume correction in PET.

In this paper we propose a segmented magnetic resonance imaging (MRI) prior-based maximum penalized likelihood deconvolution technique for positron emission tomography (PET) images. The model assumes the existence of activity classes that behave like a hidden Markov random field (MRF) driven by the segmented MRI. We utilize a mean field approximation to compute the likelihood of the MRF. We tested our method on both simulated and clinical data (brain PET) and compared our results with PET images corrected with the re-blurred Van Cittert (VC) algorithm, the simplified Guven (SG) algorithm and the region-based voxel-wise (RBV) technique. We demonstrated our algorithm outperforms the VC algorithm and outperforms SG and RBV corrections when the segmented MRI is inconsistent (e.g. mis-segmentation, lesions, etc) with the PET image.

PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography

Positron emission tomography (PET) images are degraded by a phenomenon known as the partial volume effect (PVE). Approaches have been developed to reduce PVEs, typically through the utilisation of structural information provided by other imaging modalities such as MRI or CT. These methods, known as partial volume correction (PVC) techniques, reduce PVEs by compensating for the effects of the scanner resolution, thereby improving the quantitative accuracy. The PETPVC toolbox described in this paper comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data. Eight core PVC techniques are available. These core methods can be combined to create a total of 22 different PVC techniques. Simulated brain PET data are used to demonstrate the utility of toolbox in idealised conditions, the effects of applying PVC with mismatched point-spread function (PSF) estimates and the potential of novel hybrid PVC methods to improve the quantification of lesions. All anatomy-based PVC techniques achieve complete recovery of the PET signal in cortical grey matter (GM) when performed in idealised conditions. Applying deconvolution-based approaches results in incomplete recovery due to premature termination of the iterative process. PVC techniques are sensitive to PSF mismatch, causing a bias of up to 16.7% in GM recovery when over-estimating the PSF by 3 mm. The recovery of both GM and a simulated lesion was improved by combining two PVC techniques together. The PETPVC toolbox has been written in C++, supports Windows, Mac and Linux operating systems, is open-source and publicly available.

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.

A comparison of the options for brain partial volume correction using PET/MRI

Partial volume effects affect the quantitative accuracy of PET images. Many approaches to partial volume correction (PVC) have been proposed, however most rely on additional, patient-specific anatomical information from structural imaging modalities such as MRI. In order to utilize anatomical data, image registration is required. With the recent advent of simultaneous PET/MRI scanners comes the ability to acquire accurately registered data. In this study, applied eight different PVC techniques to Monte Carlo simulated data, derived from a clinical brain FDG PET/MRI study. Reconstruction-based and post-reconstruction PVC methods were evaluated. Their performance was investigated in terms of bias vs. noise, lesion contrast and when faced with registration errors. Excellent quantification, with reduced noise, can be achieved by applying PVC when accurately aligned data are available. Reconstruction-based methods produced images with low bias and reduced noise. Post-reconstruction techniques appeared to be more sensitive to registration and segmentation errors. All PVC techniques improved recovery compared to the uncorrected data.

Evaluation of an OSEM-based PVC method for SPECT with clinical data

Partial volume correction (PVC) can compensate for cross-talk between different anatomical regions in images from a functional imaging system with poor spatial resolution, such as PET or SPECT, by utilizing data from a high-resolution structural imaging modality, such as CT or MRI. We have developed a new PVC method for SPECT, and here we present an evaluation of this method using clinical data from [123I]-FP-CIT (DATscan) studies. PVC was performed based on MRI data, which were segmented and co-registered to the SPECT data. The correction was applied during OSEM reconstruction, taking into account the distance dependent resolution of the SPECT system. We compared our new method (OSEM-PVC) with OSEM reconstruction with resolution recovery (OSEM-RR). Qualitatively, OSEM-PVC resulted in images with visually improved structural definition and, quantitatively, it gave similar contrast as OSEM-RR but lower intra-region variability.