The impact of hypoglycaemia awareness status on regional brain responses to acute hypoglycaemia in men with type 1 diabetes.

AIMS/HYPOTHESIS: Impaired awareness of hypoglycaemia (IAH) in type 1 diabetes increases the risk of severe hypoglycaemia sixfold and can be resistant to intervention. We explored the impact of IAH on central responses to hypoglycaemia to investigate the mechanisms underlying barriers to therapeutic intervention. METHODS: We conducted [15O]water positron emission tomography studies of regional brain perfusion during euglycaemia (target 5 mmol/l), hypoglycaemia (achieved level, 2.4 mmol/l) and recovery (target 5 mmol/l) in 17 men with type 1 diabetes: eight with IAH, and nine with intact hypoglycaemia awareness (HA). RESULTS: Hypoglycaemia with HA was associated with increased activation in brain regions including the thalamus, insula, globus pallidus (GP), anterior cingulate cortex (ACC), orbital cortex, dorsolateral frontal (DLF) cortex, angular gyrus and amygdala; deactivation occurred in the temporal and parahippocampal regions. IAH was associated with reduced catecholamine and symptom responses to hypoglycaemia vs HA (incremental AUC: autonomic scores, 26.2 ± 35.5 vs 422.7 ± 237.1; neuroglycopenic scores, 34.8 ± 88.8 vs 478.9 ± 311.1; both p < 0.002). There were subtle differences (p < 0.005, k ≥ 50 voxels) in brain activation at hypoglycaemia, including early differences in the right central operculum, bilateral medial orbital (MO) cortex, and left posterior DLF cortex, with additional differences in the ACC, right GP and post- and pre-central gyri in established hypoglycaemia, and lack of deactivation in temporal regions in established hypoglycaemia. CONCLUSIONS/INTERPRETATION: Differences in activation in the post- and pre-central gyri may be expected in people with reduced subjective responses to hypoglycaemia. Alterations in the activity of regions involved in the drive to eat (operculum), emotional salience (MO cortex), aversion (GP) and recall (temporal) suggest differences in the perceived importance and urgency of responses to hypoglycaemia in IAH compared with HA, which may be key to the persistence of the condition.

Impact of respiratory motion correction on SPECT myocardial perfusion imaging using a mechanically moving phantom assembly with variable cardiac defects.

BACKGROUND: The aim of this study was to determine the impact of respiratory motion correction on SPECT MPI and on defect detection using a phantom assembly. METHODS: SPECT/CT data were acquired using an anthropomorphic phantom with inflatable lungs and with an ECG beating and moving cardiac compartment. The heart motion followed the respiratory pattern in the cranio-caudal direction to simulate normal or deep breathing. Small or large transmural defects were inserted into the myocardial wall of the left ventricle. SPECT/CT images were acquired for each of the four respiratory phases, from exhale to inhale. A respiratory motion correction was applied using an image-based method with transformation parameters derived from the SPECT data by a non-rigid registration algorithm. A report on defect detection from two physicians and a quantitative analysis on MPI data were performed before and after applying motion correction. RESULTS: Respiratory motion correction eliminated artifacts present in the images, resulting in a uniform uptake and reduction of motion blurring, especially in the inferior and anterior regions of the LV myocardial walls. The physicians' report after motion correction showed that images were corrected for motion. CONCLUSIONS: A combination of motion correction with attenuation correction reduces artifacts in SPECT MPI. AC-SPECT images with and without motion correction should be simultaneously inspected to report on small defects.

Characterization of attenuation and respiratory motion artifacts and their influence on SPECT MP image evaluation using a dynamic phantom assembly with variable cardiac defects.

BACKGROUND: A phantom assembly that simulates the respiratory motion of the heart was used to investigate artifacts and their impact on defect detection. METHODS: SPECT/CT images were acquired for phantoms with and without small and large cardiac defects during normal and deep breathing, and also at four static respiratory phases. Acquisitions were reconstructed with and without AC, and with misalignment of transmission and emission scans. A quantitative analysis was performed to assess artifacts. Two physicians reported on defect presence or absence and their results were evaluated. RESULTS: All large defects were correctly reported. Attenuation reduced uptake in the basal LV walls, increasing FN physicians' reports for small defects. Respiratory motion reduced uptake mainly in the anterior and inferior walls increasing FP and FN reports on images without and with small defects, respectively. Artifacts due to misalignment between CT and SPECT scans in normal breathing phantoms did not influence the physicians' reports. CONCLUSIONS: Attenuation and respiratory motion correction should be applied to reduce artifacts before reporting on small defects in deep breathing conditions. Artifacts due to misalignment between CT and SPECT scans do not affect defect detection in normal breathing when the LV is co-registered in SPECT and CT images prior to AC.

Respiratory motion correction of PET using MR-constrained PET-PET registration.

BACKGROUND: Respiratory motion in positron emission tomography (PET) is an unavoidable source of error in the measurement of tracer uptake, lesion position and lesion size. The introduction of PET-MR dual modality scanners opens a new avenue for addressing this issue. Motion models offer a way to estimate motion using a reduced number of parameters. This can be beneficial for estimating motion from PET, which can otherwise be difficult due to the high level of noise of the data. METHOD: We propose a novel technique that makes use of a respiratory motion model, formed from initial MR scan data. The motion model is used to constrain PET-PET registrations between a reference PET gate and the gates to be corrected. For evaluation, PET with added FDG-avid lesions was simulated from real, segmented, ultrashort echo time MR data obtained from four volunteers. Respiratory motion was included in the simulations using motion fields derived from real dynamic 3D MR volumes obtained from the same volunteers. RESULTS: Performance was compared to an MR-derived motion model driven method (which requires constant use of the MR scanner) and to unconstrained PET-PET registration of the PET gates. Without motion correction, a median drop in uncorrected lesion [Formula: see text] intensity to [Formula: see text] and an increase in median head-foot lesion width, specified by a minimum bounding box, to [Formula: see text] was observed relative to the corresponding measures in motion-free simulations. The proposed method corrected these values to [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]) respectively, with notably improved performance close to the diaphragm and in the liver. Median lesion displacement across all lesions was observed to be [Formula: see text] without motion correction, which was reduced to [Formula: see text] ([Formula: see text]) with motion correction. DISCUSSION: This paper presents a novel technique for respiratory motion correction of PET data in PET-MR imaging. After an initial 30 second MR scan, the proposed technique does not require use of the MR scanner for motion correction purposes, making it suitable for MR-intensive studies or sequential PET-MR. The accuracy of the proposed technique was similar to both comparative methods, but robustness was improved compared to the PET-PET technique, particularly in regions with higher noise such as the liver.

Kinetic model-informed deep learning for multiplexed PET image separation.

BACKGROUND: Multiplexed positron emission tomography (mPET) imaging can measure physiological and pathological information from different tracers simultaneously in a single scan. Separation of the multiplexed PET signals within a single PET scan is challenging due to the fact that each tracer gives rise to indistinguishable 511 keV photon pairs, and thus no unique energy information for differentiating the source of each photon pair. METHODS: Recently, many applications of deep learning for mPET image separation have been concentrated on pure data-driven methods, e.g., training a neural network to separate mPET images into single-tracer dynamic/static images. These methods use over-parameterized networks with only a very weak inductive prior. In this work, we improve the inductive prior of the deep network by incorporating a general kinetic model based on spectral analysis. The model is incorporated, along with deep networks, into an unrolled image-space version of an iterative fully 4D PET reconstruction algorithm. RESULTS: The performance of the proposed method was evaluated on a simulated brain image dataset for dual-tracer [ 18 F]FDG+[ 11 C]MET PET image separation. The results demonstrate that the proposed method can achieve separation performance comparable to that obtained with single-tracer imaging. In addition, the proposed method outperformed the model-based separation methods (the conventional voxel-wise multi-tracer compartment modeling method (v-MTCM) and the image-space dual-tracer version of the fully 4D PET image reconstruction algorithm (IS-F4D)), as well as a pure data-driven separation [using a convolutional encoder-decoder (CED)], with fewer training examples. CONCLUSIONS: This work proposes a kinetic model-informed unrolled deep learning method for mPET image separation. In simulation studies, the method proved able to outperform both the conventional v-MTCM method and a pure data-driven CED with less training data.

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [18F]FDG PET-CT images.

Utilisation of whole organ volumes to extract anatomical and functional information from computed tomography (CT) and positron emission tomography (PET) images may provide key information for the treatment and follow-up of cancer patients. However, manual organ segmentation, is laborious and time-consuming. In this study, a CT-based deep learning method and a multi-atlas method were evaluated for segmenting the liver and spleen on CT images to extract quantitative tracer information from Fluorine-18 fluorodeoxyglucose ([18F]FDG) PET images of 50 patients with advanced Hodgkin lymphoma (HL). Manual segmentation was used as the reference method. The two automatic methods were also compared with a manually defined volume of interest (VOI) within the organ, a technique commonly performed in clinical settings. Both automatic methods provided accurate CT segmentations, with the deep learning method outperforming the multi-atlas with a DICE coefficient of 0.93 ± 0.03 (mean ± standard deviation) in liver and 0.87 ± 0.17 in spleen compared to 0.87 ± 0.05 (liver) and 0.78 ± 0.11 (spleen) for the multi-atlas. Similarly, a mean relative error of -3.2% for the liver and -3.4% for the spleen across patients was found for the mean standardized uptake value (SUVmean) using the deep learning regions while the corresponding errors for the multi-atlas method were -4.7% and -9.2%, respectively. For the maximum SUV (SUVmax), both methods resulted in higher than 20% overestimation due to the extension of organ boundaries to include neighbouring, high-uptake regions. The conservative VOI method which did not extend into neighbouring tissues, provided a more accurate SUVmaxestimate. In conclusion, the automatic, and particularly the deep learning method could be used to rapidly extract information of the SUVmeanwithin the liver and spleen. However, activity from neighbouring organs and lesions can lead to high biases in SUVmaxand current practices of manually defining a volume of interest in the organ should be considered instead.

In vivo real-time positron emission particle tracking (PEPT) and single particle PET.

Positron emission particle tracking (PEPT) enables 3D localization and tracking of single positron-emitting radiolabelled particles with high spatiotemporal resolution. The translation of PEPT to the biomedical imaging field has been limited due to the lack of methods to radiolabel biocompatible particles with sufficient specific activity and protocols to isolate a single particle in the sub-micrometre size range, below the threshold for capillary embolization. Here we report two key developments: the synthesis and 68Ga-radiolabelling of homogeneous silica particles of 950 nm diameter with unprecedented specific activities (2.1 ± 1.4 kBq per particle), and the isolation and manipulation of a single particle. We have combined these developments to perform in vivo PEPT and dynamic positron emission tomography (PET) imaging of a single radiolabelled sub-micrometre size particle using a pre-clinical positron emission tomography/computed tomography scanner. This work opens possibilities for quantitative assessment of haemodynamics in vivo in real time, at the whole-body level using minimal amounts of injected radioactive dose and material.

Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis.

(18)F-Fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) is now routinely used in oncological imaging for diagnosis and staging and increasingly to determine early response to treatment, often employing semiquantitative measures of lesion activity such as the standardized uptake value (SUV). However, the ability to predict the behaviour of a tumour in terms of future therapy response or prognosis using SUVs from a baseline scan prior to treatment is limited. It is recognized that medical images contain more useful information than may be perceived with the naked eye, leading to the field of "radiomics" whereby additional features can be extracted by computational postprocessing techniques. In recent years, evidence has slowly accumulated showing that parameters obtained by texture analysis of radiological images, reflecting the underlying spatial variation and heterogeneity of voxel intensities within a tumour, may yield additional predictive and prognostic information. It is hoped that measurement of these textural features may allow better tissue characterization as well as better stratification of treatment in clinical trials, or individualization of future cancer treatment in the clinic, than is possible with current imaging biomarkers. In this review we focus on the literature describing the emerging methods of texture analysis in (18)FDG PET/CT, as well as other imaging modalities, and how the measurement of spatial variation of voxel grey-scale intensity within an image may provide additional predictive and prognostic information, and postulate the underlying biological mechanisms.

Estimation of regional bone metabolism from whole-body 18F-fluoride PET static images.

PURPOSE: We evaluate a new quantitative method of acquiring and analysing (18)F positron emission tomography (PET) studies that enables regional bone plasma clearance (K ( i )) to be estimated from static scans acquired at multiple sites in the skeleton following a single injection of tracer. METHODS: Dynamic lumbar spine (18)F PET data from two clinical trials were used to simulate a series of static scans acquired 30-60 min after injection. Venous blood samples were taken at 30, 40, 50 and 60 min and K ( i ) evaluated by Patlak analysis and the static scan method. The data were used to evaluate the precision errors of the Patlak and static scan methods expressed as the percentage coefficient of variation (%CV) and compare their response to 6 months of treatment with the bone anabolic agent teriparatide. RESULTS: Static scan K ( i ) measurements 30-60 min after injection were highly correlated with the Patlak results (r > 0.99). The %CV for the static scan method was 17.5% 30 min after injection, decreasing to 14.5% at 60 min, compared with 13.0% for Patlak analysis. Response to teriparatide treatment was +25.2% for the static scan method compared with +24.3% for Patlak analysis. The mean ratio (SD) of the static scan and Patlak K ( i ) results was 1.006 (0.015) at 30 min after injection decreasing to 0.965 (0.015) at 60 min. CONCLUSION: (18)F-Fluoride bone plasma clearance can be estimated from a static scan and venous blood samples acquired 30-60 min after injection. The method enables K ( i ) to be estimated at multiple skeletal sites with a single injection of tracer.

Recommendations for measurement of tumour vascularity with positron emission tomography in early phase clinical trials.

The evaluation of drug pharmacodynamics and early tumour response are integral to current clinical trials of novel cancer therapeutics to explain or predict long term clinical benefit or to confirm dose selection. Tumour vascularity assessment by positron emission tomography could be viewed as a generic pharmacodynamic endpoint or tool for monitoring response to treatment. This review discusses methods for semi-quantitative and quantitative assessment of tumour vascularity. The radioligands and radiotracers range from direct physiological functional tracers like [(15)O]-water to macromolecular probes targeting integrin receptors expressed on neovasculature. Finally we make recommendations on ways to incorporate such measurements of tumour vascularity into early clinical trials of novel therapeutics. Key Points • [ ( 15 ) O]-water is the gold standard for blood flow/tissue perfusion with PET • In some instances dynamic [ ( 18 ) F]-FDG uptake may be used to estimate perfusion • Radiopharmaceuticals that target integrins are now being evaluated for measuring tumour vascularity.

Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning.

Objective.In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value (SUV). For small objects, the accuracy of SUV estimates is limited by the noise properties of PET images and the partial volume effect. There is need for methods that provide more accurate and reproducible quantification of radiotracer uptake.Approach.In this work, we present a deep learning approach with the aim of improving quantification of lung tumour radiotracer uptake and tumour shape definition. A set of simulated tumours, assigned with 'ground truth' radiotracer distributions, are used to generate realistic PET raw data which are then reconstructed into PET images. In this work, the ground truth images are generated by placing simulated tumours characterised by different sizes and activity distributions in the left lung of an anthropomorphic phantom. These images are then used as input to an analytical simulator to simulate realistic raw PET data. The PET images reconstructed from the simulated raw data and the corresponding ground truth images are used to train a 3D convolutional neural network.Results.When tested on an unseen set of reconstructed PET phantom images, the network yields improved estimates of the corresponding ground truth. The same network is then applied to reconstructed PET data generated with different point spread functions. Overall the network is able to recover better defined tumour shapes and improved estimates of tumour maximum and median activities.Significance.Our results suggest that the proposed approach, trained on data simulated with one scanner geometry, has the potential to restore PET data acquired with different scanners.

Standardisation of conventional and advanced iterative reconstruction methods for Gallium-68 multi-centre PET-CT trials.

PURPOSE: To assess the applicability of the Fluorine-18 performance specifications defined by EANM Research Ltd (EARL), in Gallium-68 multi-centre PET-CT trials using conventional (ordered subset expectation maximisation, OSEM) and advanced iterative reconstructions which include the systems' point spread function (PSF) and a Bayesian penalised likelihood algorithm (BPL) commercially known as Q.CLEAR. The possibility of standardising the two advanced reconstruction methods was examined. METHODS: The NEMA image quality phantom was filled with Gallium-68 and scanned on a GE PET-CT system. PSF and BPL with varying post-reconstruction Gaussian filter width (2-6.4 mm) and penalisation factor (200-1200), respectively, were applied. The average peak-to-valley ratio from six profiles across each sphere was estimated to inspect any edge artefacts. Image noise was assessed using background variability and image roughness. Six GE and Siemens PET-CT scanners provided Gallium-68 images of the NEMA phantom using both conventional and advanced reconstructions from which the maximum, mean and peak recoveries were drawn. Fourteen patients underwent 68Ga-PSMA PET-CT imaging. BPL (200-1200) reconstructions of the data were compared against PSF smoothed with a 6.4-mm Gaussian filter. RESULTS: A Gaussian filter width of approximately 6 mm for PSF and a penalisation factor of 800 for BPL were needed to suppress the edge artefacts. In addition, those reconstructions provided the closest agreement between the two advanced iterative reconstructions and low noise levels with the background variability and the image roughness being lower than 7.5% and 11.5%, respectively. The recoveries for all methods generally performed at the lower limits of the EARL specifications, especially for the 13- and 10-mm spheres for which up to 27% (conventional) and 41% (advanced reconstructions) lower limits are suggested. The lesion standardised uptake values from the clinical data were significantly different between BPL and PSF smoothed with a Gaussian filter of 6.4 mm wide for all penalisation factors except for 800 and 1000. CONCLUSION: It is possible to standardise the advanced reconstruction methods with the reconstruction parameters being also sufficient for minimising the edge artefacts and noise in the images. For both conventional and advanced reconstructions, Gallium-68 specific recovery coefficient limits were required, especially for the smallest phantom spheres.

Production of copper-64 using a hospital cyclotron: targetry, purification and quality analysis.

OBJECTIVES: To construct and evaluate a 64Cu production system that minimises the amount of costly 64Ni, radionuclidic impurities and nonradioactive metal contamination and maximises radiochemical and radionuclidic purity and molar activity; and to report analytical and quality control methods that can be used within typical PET radiochemistry production facilities to measure metal ion concentrations and radiometal molar activities. METHODS: Low volume was ensured by dissolving the irradiated nickel in a low volume of hydrochloric acid (<1 mL) using the concave gold target backing as a reaction vessel in a custom-built target holder. Removal of contaminating 55Co and nonradioactive trace metals was ensured by adding an intermediate hydrochloric acid concentration step during the conventional ion-exchange elution process. The radionuclidic purity of the product was determined by half-life measurements, gamma spectroscopy and ion radiochromatography. Trace metal contamination and molar activity were determined by ion chromatography. RESULTS AND CONCLUSIONS: On a small scale, suitable for preclinical research, the process produced typically 3.2 GBq 64Cu in 2 mL solution from 9.4 ± 2.1 mg nickel-64 electroplated onto a gold target backing. The product had high molar activity (121.5 GBq/µmol), was free of trace metal contamination detectable by ion chromatography and has been used for many preclinical and clinical PET imaging applications.

Radiobiological effects of hypoxia-dependent uptake of 64Cu-ATSM: enhanced DNA damage and cytotoxicity in hypoxic cells.

PURPOSE: Hypoxia occurs frequently in cancers and can lead to therapeutic resistance due to poor perfusion and loss of the oxygen enhancement effect. (64)Cu-ATSM has shown promise as a hypoxia diagnostic agent due to its selective uptake and retention in hypoxic cells and its emission of positrons for PET imaging. (64)Cu also emits radiotoxic Auger electrons and beta(-) particles and may therefore exhibit therapeutic potential when concentrated in hypoxic tissue. METHODS: MCF-7 cells were treated with 0-10 MBq/ml (64)Cu-ATSM under differing oxygen conditions ranging from normoxia to severe hypoxia. Intracellular response to hypoxia was measured using Western blotting for expression of HIF-1alpha, while cellular accumulation of (64)Cu was measured by gamma counting. DNA damage and cytotoxicity were measured with, respectively, the Comet assay and clonogenic survival. RESULTS: (64)Cu-ATSM uptake in MCF-7 cells increased as atmospheric oxygen decreased (up to 5.6 Bq/cell at 20.9% oxygen, 10.4 Bq/cell at 0.1% oxygen and 26.0 Bq/cell at anoxia). Toxicity of (64)Cu-ATSM in MCF-7 cells also increased as atmospheric oxygen decreased, with survival of 9.8, 1.5 and 0% in cells exposed to 10 MBq/ml at 20.9, 0.1 and 0% oxygen. The Comet assay revealed a statistically significant increase in (64)Cu-ATSM-induced DNA damage under hypoxic conditions. CONCLUSION: The results support a model in which hypoxia-enhanced uptake of radiotoxic (64)Cu induces sufficient DNA damage and toxicity to overcome the documented radioresistance in hypoxic MCF-7 cells. This suggests that (64)Cu-ATSM and related complexes have potential for targeted radionuclide therapy of hypoxic tumours.

Synthesis and biological evaluation of [(18)F]tetrafluoroborate: a PET imaging agent for thyroid disease and reporter gene imaging of the sodium/iodide symporter.

PURPOSE: The human sodium/iodide symporter (hNIS) is a well-established target in thyroid disease and reporter gene imaging using gamma emitters (123)I-iodide, (131)I-iodide and (99m)Tc-pertechnetate. However, no PET imaging agent is routinely available. The aim of this study was to prepare and evaluate (18)F-labelled tetrafluoroborate ([(18)F]TFB) for PET imaging of hNIS. METHODS: [(18)F]TFB was prepared by isotopic exchange of BF (4) (-) with [(18)F]fluoride in hot hydrochloric acid and purified using an alumina column. Its identity, purity and stability in serum were determined by HPLC, thin-layer chromatography (TLC) and mass spectrometry. Its interaction with NIS was assessed in vitro using FRTL-5 rat thyroid cells, with and without stimulation by thyroid-stimulating hormone (TSH), in the presence and absence of perchlorate. Biodistribution and PET imaging studies were performed using BALB/c mice, with and without perchlorate inhibition. RESULTS: [(18)F]TFB was readily prepared with specific activity of 10 GBq/mg. It showed rapid accumulation in FRTL-5 cells that was stimulated by TSH and inhibited by perchlorate, and rapid specific accumulation in vivo in thyroid (SUV = 72 after 1 h) and stomach that was inhibited 95% by perchlorate. CONCLUSION: [(18)F]TFB is an easily prepared PET imaging agent for rodent NIS and should be evaluated for hNIS PET imaging in humans.

FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0.

The aim of this guideline is to provide a minimum standard for the acquisition and interpretation of PET and PET/CT scans with [18F]-fluorodeoxyglucose (FDG). This guideline will therefore address general information about[18F]-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) and is provided to help the physician and physicist to assist to carrying out,interpret, and document quantitative FDG PET/CT examinations,but will concentrate on the optimisation of diagnostic quality and quantitative information.

Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical Image Datasets.

Manifold alignment (MA) is a technique to map many high-dimensional datasets to one shared low-dimensional space. Here we develop a pipeline for using MA to reconstruct high-resolution medical images. We present two key contributions. First, we develop a novel MA scheme in which each high-dimensional dataset can be differently weighted preventing noisier or less informative data from corrupting the aligned embedding. We find that this generalisation improves performance in our experiments in both supervised and unsupervised MA problems. Second, we use the wave kernel signature as a graph descriptor for the unsupervised MA case finding that it significantly outperforms the current state-of-the-art methods and provides higher quality reconstructed magnetic resonance volumes than existing methods.

Evaluation of [13N]ammonia positron emission tomography as a potential method for quantifying glutamine synthetase activity in the human brain.

PURPOSE: The conversion of synaptic glutamate to glutamine in astrocytes by glutamine synthetase (GS) is critical to maintaining healthy brain activity and may be disrupted in several brain disorders. As the GS catalysed conversion of glutamate to glutamine requires ammonia, we evaluated whether [13N]ammonia positron emission tomography (PET) could reliability quantify GS activity in humans. METHODS: In this test-retest study, eight healthy volunteers each received two dynamic [13N]ammonia PET scans on the morning and afternoon of the same day. Each [13N]ammonia scan was preceded by a [15O]water PET scan to account for effects of cerebral blood flow (CBF). RESULTS: Concentrations of radioactive metabolites in arterial blood were available for both sessions in five of the eight subjects. Our results demonstrated that kinetic modelling was unable to reliably distinguish estimates of the kinetic rate constant k3 (related to GS activity) from K1 (related to [13N]ammonia brain uptake), and indicated a non-negligible back-flux of [13N] to blood (k2). Model selection favoured a reversible one-tissue compartmental model, and [13N]ammonia K1 correlated reliably (r2 = 0.72-0.92) with [15O]water CBF. CONCLUSION: The [13N]ammonia PET method was unable to reliably estimate GS activity in the human brain but may provide an alternative index of CBF.

Retrospective data-driven respiratory gating for PET/CT.

Respiratory motion can adversely affect both PET and CT acquisitions. Respiratory gating allows an acquisition to be divided into a series of motion-reduced bins according to the respiratory signal, which is typically hardware acquired. In order that the effects of motion can potentially be corrected for, we have developed a novel, automatic, data-driven gating method which retrospectively derives the respiratory signal from the acquired PET and CT data. PET data are acquired in listmode and analysed in sinogram space, and CT data are acquired in cine mode and analysed in image space. Spectral analysis is used to identify regions within the CT and PET data which are subject to respiratory motion, and the variation of counts within these regions is used to estimate the respiratory signal. Amplitude binning is then used to create motion-reduced PET and CT frames. The method was demonstrated with four patient datasets acquired on a 4-slice PET/CT system. To assess the accuracy of the data-derived respiratory signal, a hardware-based signal was acquired for comparison. Data-driven gating was successfully performed on PET and CT datasets for all four patients. Gated images demonstrated respiratory motion throughout the bin sequences for all PET and CT series, and image analysis and direct comparison of the traces derived from the data-driven method with the hardware-acquired traces indicated accurate recovery of the respiratory signal.

Long-term precision of 18F-fluoride PET skeletal kinetic studies in the assessment of bone metabolism.

UNLABELLED: (18)F-Fluoride PET allows noninvasive evaluation of regional bone metabolism and has the potential to become a useful tool for assessing patients with metabolic bone disease and evaluating novel drugs being developed for these diseases. The main PET parameter of interest, termed K(i), reflects regional bone metabolism. The aim of this study was to compare the long-term precision of (18)F-fluoride PET with that of biochemical markers of bone turnover assessed over 6 mo. METHODS: Sixteen postmenopausal women with osteoporosis or significant osteopenia and a mean age of 64 y underwent (18)F-fluoride PET of the lumbar spine and measurements of biochemical markers of bone formation (bone-specific alkaline phosphatase and osteocalcin) and bone resorption (urinary deoxypyridinoline) at baseline and 6 mo later. Four different methods for analyzing the (18)F-fluoride PET data were compared: a 4k 3-compartmental model using nonlinear regression analysis (K(i-4k)), a 3k 3-compartmental model using nonlinear regression analysis (K(i-3k)), Patlak analysis (K(i-PAT)), and standardized uptake values. RESULTS: With the exception of a small but significant decrease in K(i-3k) at 6 mo, there were no significant differences between the baseline and 6-mo values for the PET parameters or biochemical markers. The long-term precision, expressed as the coefficient of variation (with 95% confidence interval in parentheses), was 12.2% (9%-19%), 13.8% (10%-22%), 14.4% (11%-22%), and 26.6% (19%-40%) for K(i-3k), K(i-PAT), mean standardized uptake value, and K(i-4k), respectively. For comparison, the precision of the biochemical markers was 10% (7%-15%), 18% (13%-27%), and 14% (10%-21%) for bone-specific alkaline phosphatase, osteocalcin, and urinary deoxypyridinoline, respectively. Intraclass correlation between the baseline and 6-mo values ranged from 0.44 for K(i-4k) to 0.85 for K(i-3k). No significant correlation was found between the repeated mean standardized uptake value measurements. CONCLUSION: The precision and intraclass correlation observed for K(i-3k) and K(i-PAT) was equivalent to that observed for biochemical markers. This study provided initial data on the long-term precision of (18)F-fluoride PET measured at the lumbar spine, which will aid in the accurate interpretation of changes in regional bone metabolism in response to treatment.