Validation of a combined image derived input function and venous sampling approach for the quantification of [18F]GE-179 PET binding in the brain.

Blood-based kinetic analysis of PET data relies on an accurate estimate of the arterial plasma input function (PIF). An alternative to invasive measurements from arterial sampling is an image-derived input function (IDIF). However, an IDIF provides the whole blood radioactivity concentration, rather than the required free tracer radioactivity concentration in plasma. To estimate the tracer PIF, we corrected an IDIF from the carotid artery with estimates of plasma parent fraction (PF) and plasma-to-whole blood (PWB) ratio obtained from five venous samples. We compared the combined IDIF+venous approach to gold standard data from arterial sampling in 10 healthy volunteers undergoing [18F]GE-179 brain PET imaging of the NMDA receptor. Arterial and venous PF and PWB ratio estimates determined from 7 patients with traumatic brain injury (TBI) were also compared to assess the potential effect of medication. There was high agreement between areas under the curves of the estimates of PF (r = 0.99, p<0.001), PWB ratio (r = 0.93, p<0.001), and the PIF (r = 0.92, p<0.001) as well as total distribution volume (VT) in 11 regions across the brain (r = 0.95, p<0.001). IDIF+venous VT had a mean bias of -1.7% and a comparable regional coefficient of variation (arterial: 21.3 ± 2.5%, IDIF+venous: 21.5 ± 2.0%). Simplification of the IDIF+venous method to use only one venous sample provided less accurate VT estimates (mean bias 9.9%; r = 0.71, p<0.001). A version of the method that avoids the need for blood sampling by combining the IDIF with population-based PF and PWB ratio estimates systematically underestimated VT (mean bias -20.9%), and produced VT estimates with a poor correlation to those obtained using arterial data (r = 0.45, p<0.001). Arterial and venous blood data from 7 TBI patients showed high correlations for PF (r = 0.92, p = 0.003) and PWB ratio (r = 0.93, p = 0.003). In conclusion, the IDIF+venous method with five venous samples provides a viable alternative to arterial sampling for quantification of [18F]GE-179 VT.

Sub-clinical abnormalities detected by PET/MRI in household tuberculosis contacts.

BACKGROUND: The understanding of early events following TB exposure is limited by traditional tests that rely on detection of an immune response to infection, which is delayed, or on imaging tests with low sensitivity for early disease. We investigated for evidence of lung abnormalities in heavily exposed TB contacts using PET/MRI. METHODS: 30 household contacts of 20 index patients underwent clinical assessment, IGRA testing, chest x-ray and PET/MRI scan using 18-F-FDG. MRI images were examined by a radiology/nuclear medicine dual-qualified physician using a standardised report form, while PET/MRI images were examined independently by another radiology/nuclear medicine dual-qualified physician using a similar form. Standardised uptake value (SUV) was quantified for each abnormal lesion. RESULTS: IGRA was positive in 40%. PET/MRI scan was abnormal in 30%, predominantly FDG uptake in hilar or mediastinal lymph nodes and lung apices. We did not identify any relationship between PET/MRI findings and degree of exposure or IGRA status. CONCLUSION: PET-based imaging may provide important insights into the natural history following exposure to TB that may not be available from traditional tests of TB immune response or imaging. The clinical significance of the abnormalities is uncertain and merits further investigation in longitudinal studies.

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

PURPOSE: Patients 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. METHODS: Consecutive IPF patients (22 men, 3 women) were prospectively recruited. The patients underwent (18)F-FDG PET/HRCT. The pulmonary imaging findings in the IPF patients were compared to the findings in a control population. Pulmonary uptake of (18)F-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). RESULTS: The 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). CONCLUSION: IPF patients have increased pulmonary uptake of (18)F-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.

Operationalizing the centiloid scale for [18F]florbetapir PET studies on PET/MRI.

INTRODUCTION: The Centiloid scale aims to harmonize amyloid beta (Aß) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI). METHODS: We transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian-mixture-modelling-derived cutpoints for Aß PET positivity were converted. RESULTS: The Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM-based Centiloids. Linear adjustment produced a WM-based cutpoint of 18.1. DISCUSSION: Transformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed. HIGHLIGHTS: Centiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results.Centiloid values can be influenced by differences in acquisition.We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort.Whole cerebellum referenced values could be reliably transformed to Centiloids.White matter referenced values may be less generalizable between datasets.

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

PURPOSE: Alzheimer'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. METHODS: An 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 [(18)F]flutemetamol. Volume of interest (VOI) analysis was performed for corrected and uncorrected images. RESULTS: PVC 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. CONCLUSION: Care 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.

A comparison of 18F-FDG PET/MR with PET/CT in pulmonary tuberculosis.

PURPOSE: PET/computed tomography (CT) has been shown to detect lesions in patients with pulmonary tuberculosis (PTB) and may be useful for assessing PTB disease in clinical research studies. However, radiation dose is of concern for clinical research in individuals with an underlying curable disease. This study aimed to determine whether PET/MR is equivalent to PET/CT in PTB. MATERIALS AND METHODS: Ten patients with microbiologically confirmed PTB were recruited. Patients received 129.0±4.1 MBq of fluorine-18-fluorodeoxyglucose. Five of the 10 patients underwent a PET/MR scan, followed by PET/CT. The remaining five were first imaged on the PET/CT, followed by the PET/MRI. PET acquisition began at 66.7±14.4 min (mean±SD) after injection when performing PET/MR first (PET/CT: 117.2±5.6 min) and 92.4±7.6 min when patients were imaged on PET/MR second (PET/CT: 61.1±3.9 min). PET data were reconstructed iteratively with Ordinary-Poisson Ordered-Subset Expectation-Maximization and reconstruction parameters were matched across the two scanners. A visual lesion detection task and a standardized uptake value (SUV) analysis were carried out. The CT Hounsfield unit values of PTB lesions were also compared with MR-based attenuation correction mu-map tissue classes. RESULTS: A total of 108 PTB lesions were detected on PET/MR and 112 on PET/CT. SUV analysis was carried out on 50 of these lesions that were observed with both modalities. Mean standardized uptake value (SUVmean) and maximum standardized uptake value (SUVmax) were significantly lower on PET/MR (SUVmean: 2.6±1.4; SUVmax: 4.3±2.5) than PET/CT (SUVmean: 3.5±1.5; SUVmax: 5.3±2.4). CONCLUSION: PET/MR visual performance was shown to be comparable to PET/CT in terms of the number of PTB lesions detected. SUVs were significantly lower on PET/MR. Dixon-based attenuation correction underestimates the linear attenuation coefficient of PTB lesions, resulting in lower SUVs compared with PET/CT. However, the use of PET/MR to measure the response of lung lesions to assess response to treatment in research studies is unlikely to be affected by these differences in quantification.

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

PURPOSE: To 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. METHODS: We 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. RESULTS: In 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. CONCLUSION: Presented PVC methods are straight-forward to implement but the corrected images require careful interpretation as different methods result in different levels of recovery.

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.

Implementation and validation of an adaptive template registration method for 18F-flutemetamol imaging data.

UNLABELLED: The spatial normalization of PET amyloid imaging data is challenging because different white and gray matter patterns of negative (Aß-) and positive (Aß+) 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 (18)F-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 Aß- to the most Aß+ 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 (18)F-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 (18)F-flutemetamol model could be generalized to (11)C-Pittsburgh compound B ((11)C-PIB), we applied the method to Australian Imaging, Biomarkers and Lifestyle (AIBL) (11)C-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 (18)F-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 (11)C-PIB data (Pearson r = 0.94). CONCLUSION: The derived adaptive template registration method allows for robust, accurate, and fully automated quantification of uptake for (18)F-flutemetamol and (11)C-PIB scans without the use of MR imaging data.

A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology.

Accurate quantification in PET and SPECT requires correction for a number of physical factors, such as photon attenuation, Compton scattering and random coincidences (in PET). Another factor affecting quantification is the limited spatial resolution. While considerable effort has gone into development of routine correction techniques for the former factors, less attention has been paid to the latter. Spatial resolution-related effects, referred to as 'partial volume effects' (PVEs), depend not only on the characteristics of the imaging system but also on the object and activity distribution. Spatial and/or temporal variations in PVE can often be confounding factors. Partial volume correction (PVC) could in theory be achieved by some kind of inverse filtering technique, reversing the effect of the system PSF. However, these methods are limited, and usually lead to noise-amplification or image artefacts. Some form of regularization is therefore needed, and this can be achieved using information from co-registered anatomical images, such as CT or MRI. The purpose of this paper is to enhance understanding of PVEs and to review possible approaches for PVC. We also present a review of clinical applications of PVC within the fields of neurology, cardiology and oncology, including specific examples.

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.