Clinical Positron Emission Tomography Data Research Articles

Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising

Deep image prior (DIP) has been successfully applied to positron emission tomography (PET) image restoration, enabling represent implicit prior using only convolutional neural network architecture without training dataset, whereas the general supervised approach requires massive low-and high-quality PET image pairs. To answer the increased need for PET imaging with DIP, it is indispensable to improve the performance of the underlying DIP itself. Here, we propose a self-supervised pre-training model to improve the DIP-based PET image denoising performance. Our proposed pre-training model acquires transferable and generalizable visual representations from only unlabeled PET images by restoring various degraded PET images in a self-supervised approach. We evaluated the proposed method using clinical brain PET data with various radioactive tracers (18F-florbetapir, 11C-Pittsburgh compound-B, 18F-fluoro-2-deoxy-D-glucose, and 15O-CO2) acquired from different PET scanners. The proposed method using the self-supervised pre-training model achieved robust and state-of-the-art denoising performance while retaining spatial details and quantification accuracy compared to other unsupervised methods and pre-training model. These results highlight the potential that the proposed method is particularly effective against rare diseases and probes and helps reduce the scan time or the radiotracer dose without affecting the patients.

Comparison study of olmesartan and valsartan on myocardial metabolism in patients with dilated cardiomyopathy trial

Abstract Background Patients with dilated cardiomyopathy (DCMP) have overactivation of the renin-angiotensin-aldosterone systems, which can adversely affect myocardial metabolism. The impairment of myocardial metabolism can contribute to the progression of left ventricular remodeling and contractile dysfunction in heart failure. The objective of this study is to compare the effects of olmesartan and valsartan on myocardial metabolism in patients with DCMP. Methods The OVOID study (a comparison study of Olmesartan and Valsartan On myocardial metabolism In patients with Dilated cardiomyopathy) was a non-blinded, open-label, parallel-group, prospective, randomized, controlled, multicenter clinical trial. that 40 patients with DCMP. Patients with DCMP were randomized into the olmesartan or the valsartan group. To measure myocardial metabolism, 18F-fluoro-2-deoxyglucose (FDG) cardiac positron emission tomography (PET) was performed at baseline and six months after receiving the study agent. The primary endpoint was myocardial metabolism as measured by standardized uptake value ratio (SUVR) at 6 months after treatment. Results The baseline clinical characteristics and 18F-FDG PET data did not differ between the olmesartan and valsartan groups. LVEF rose significantly from baseline in both groups to a similar degree between the two groups (both P < 0.01). The level of N-terminal pro hormone B-type natriuretic peptide (NT-proBNP) decreased significantly from baseline in the both high- and usual-dose groups (both P < 0.01). The magnitude of NT-proBNP change was not significantly different between the two groups (P = 0.33). However, six-month follow-up 18F-FDG PET showed that SUVG value was significantly lower in the olmesartan group than the valsartan (3.76±2.12 versus 7.33±4.08, P = 0.01). Conclusions Compared to valsartan, six months of olmesartan therapy significantly improved myocardial metabolism in DCMP patients.

A Fast Convergent Ordered-Subsets Algorithm With Subiteration-Dependent Preconditioners for PET Image Reconstruction.

We investigated the imaging performance of a fast convergent ordered-subsets algorithm with subiteration-dependent preconditioners (SDPs) for positron emission tomography (PET) image reconstruction. In particular, we considered the use of SDP with the block sequential regularized expectation maximization (BSREM) approach with the relative difference prior (RDP) regularizer due to its prior clinical adaptation by vendors. Because the RDP regularization promotes smoothness in the reconstructed image, the directions of the gradients in smooth areas more accurately point toward the objective function's minimizer than those in variable areas. Motivated by this observation, two SDPs have been designed to increase iteration step-sizes in the smooth areas and reduce iteration step-sizes in the variable areas relative to a conventional expectation maximization preconditioner. The momentum technique used for convergence acceleration can be viewed as a special case of SDP. We have proved the global convergence of SDP-BSREM algorithms by assuming certain characteristics of the preconditioner. By means of numerical experiments using both simulated and clinical PET data, we have shown that the SDP-BSREM algorithms substantially improve the convergence rate, as compared to conventional BSREM and a vendor's implementation as Q.Clear. Specifically, SDP-BSREM algorithms converge 35%-50% faster in reaching the same objective function value than conventional BSREM and commercial Q.Clear algorithms. Moreover, we showed in phantoms with hot, cold and background regions that the SDP-BSREM algorithms approached the values of a highly converged reference image faster than conventional BSREM and commercial Q.Clear algorithms.

DRGAN: a deep residual generative adversarial network for PET image reconstruction

Positron emission tomography (PET) image reconstruction from low-count projection data and physical effects is challenging because the inverse problem is ill-posed and the resultant image is usually noisy. Recently, generative adversarial networks (GANs) have also shown their superior performance in many computer vision tasks and attracted growing interests in medical imaging. In this work, the authors proposed a novel model [deep residual generative adversarial network (DRGAN)] based on GANs for the reduction of streaking artefacts and the improvement of PET image quality. An innovative feature of the proposed method is that the authors trained a generator to produce `residual PET map' (RPM) for image representation, rather than generate PET images directly. DRGAN used two discriminators (critics) to enforce anatomically realistic PET images and RPM. To better boost the contextual information, the authors designed residual dense connections followed with pixel shuffle operations (RDPS blocks) that encourage feature reuse and prevent losing resolution. Both simulation data and real clinical PET data are used to evaluate the proposed method. Compared with other state-of-the-art methods, the quantification results show that DRGAN can achieve better performance in bias-variance trade-off and provide comparable image quality. Their results were rigorously evaluated by one radiologist at the Shanxi Cancer Hospital.

Faster PET reconstruction with non-smooth priors by randomization and preconditioning

Uncompressed clinical data from modern positron emission tomography (PET) scanners are very large, exceeding 350 million data points (projection bins). The last decades have seen tremendous advancements in mathematical imaging tools many of which lead to non-smooth (i.e. non-differentiable) optimization problems which are much harder to solve than smooth optimization problems. Most of these tools have not been translated to clinical PET data, as the state-of-the-art algorithms for non-smooth problems do not scale well to large data. In this work, inspired by big data machine learning applications, we use advanced randomized optimization algorithms to solve the PET reconstruction problem for a very large class of non-smooth priors which includes for example total variation, total generalized variation, directional total variation and various different physical constraints. The proposed algorithm randomly uses subsets of the data and only updates the variables associated with these. While this idea often leads to divergent algorithms, we show that the proposed algorithm does indeed converge for any proper subset selection. Numerically, we show on real PET data (FDG and florbetapir) from a Siemens Biograph mMR that about ten projections and backprojections are sufficient to solve the MAP optimisation problem related to many popular non-smooth priors; thus showing that the proposed algorithm is fast enough to bring these models into routine clinical practice.

Functional Data Analysis of Dynamic PET Data

ABSTRACTOne application of positron emission tomography (PET), a nuclear imaging technique, in neuroscience involves in vivo estimation of the density of various proteins (often, neuroreceptors) in the brain. PET scanning begins with the injection of a radiolabeled tracer that binds preferentially to the target protein; tracer molecules are then continuously delivered to the brain via the bloodstream. By detecting the radioactive decay of the tracer over time, dynamic PET data are constructed to reflect the concentration of the target protein in the brain at each time. The fundamental problem in the analysis of dynamic PET data involves estimating the impulse response function (IRF), which is necessary for describing the binding behavior of the injected radiotracer. Virtually all existing methods have three common aspects: summarizing the entire IRF with a single scalar measure; modeling each subject separately; and the imposition of parametric restrictions on the IRF. In contrast, we propose a functional data analytic approach that regards each subject’s IRF as the basic analysis unit, models multiple subjects simultaneously, and estimates the IRF nonparametrically. We pose our model as a linear mixed effect model in which population level fixed effects and subject-specific random effects are expanded using a B-spline basis. Shrinkage and roughness penalties are incorporated in the model to enforce identifiability and smoothness of the estimated curves, respectively, while monotonicity and nonnegativity constraints impose biological information on estimates. We illustrate this approach by applying it to clinical PET data with subjects belonging to three diagnosic groups. We explore differences among groups by means of pointwise confidence intervals of the estimated mean curves based on bootstrap samples. Supplementary materials for this article are available online.

Representativeness of clinical PET study participants with schizophrenia: A systematic review

While positron emission tomography (PET) studies have provided invaluable data on antipsychotic effects, selection bias remains a serious concern. A systematic review of PET studies that measured dopamine D2 receptor blockade with antipsychotics was conducted to examine their inclusion/exclusion criteria, using PubMed, EMBASE, and ClinicalTrials.gov (last search, September 2016). PET studies were included if they measured D2 receptor occupancy in patients with schizophrenia and included introduction of antipsychotic treatment or antipsychotic regimen change in a systematic manner. Twenty-six studies were identified. Age limit was included in 13 studies; one study solely included geriatric patients while others targeted younger adults. Eleven, 6, and 3 studies specifically targeted clinically stable patients, patients with severe psychopathology, and antipsychotic-free patients, respectively. Nineteen and 18 studies excluded patients with physical comorbidity and substance abuse, respectively. As a result, the mean age of subjects ranged from 23 to 42 years when one study that targeted geriatric patients was excluded. Mean Positive and Negative Syndrome Scale total scores ranged from 54 to 95. No comparison active-drug or placebo arm was employed in 24 studies. Blind assessment of symptomatology was performed in 5 studies. In general, subjects participating in clinical PET studies were relatively young, presented with mild symptomatology, and were free from substance abuse or physical comorbidities. These characteristics need to be taken into account when clinical PET data are interpreted. On the other hand, it should also be noted that this study was only qualitative and conservative interpretation is necessary for possibility of subjective bias.

Towards tracer dose reduction in PET studies: Simulation of dose reduction by retrospective randomized undersampling of list-mode data.

Optimization of tracer dose regimes in positron emission tomography (PET) imaging is a trade-off between diagnostic image quality and radiation exposure. The challenge lies in defining minimal tracer doses that still result in sufficient diagnostic image quality. In order to find such minimal doses, it would be useful to simulate tracer dose reduction as this would enable to study the effects of tracer dose reduction on image quality in single patients without repeated injections of different amounts of tracer. The aim of our study was to introduce and validate a method for simulation of low-dose PET images enabling direct comparison of different tracer doses in single patients and under constant influencing factors. (18)F-fluoride PET data were acquired on a combined PET/magnetic resonance imaging (MRI) scanner. PET data were stored together with the temporal information of the occurrence of single events (list-mode format). A predefined proportion of PET events were then randomly deleted resulting in undersampled PET data. These data sets were subsequently reconstructed resulting in simulated low-dose PET images (retrospective undersampling of list-mode data). This approach was validated in phantom experiments by visual inspection and by comparison of PET quality metrics contrast recovery coefficient (CRC), background-variability (BV) and signal-to-noise ratio (SNR) of measured and simulated PET images for different activity concentrations. In addition, reduced-dose PET images of a clinical (18)F-FDG PET dataset were simulated using the proposed approach. (18)F-PET image quality degraded with decreasing activity concentrations with comparable visual image characteristics in measured and in corresponding simulated PET images. This result was confirmed by quantification of image quality metrics. CRC, SNR and BV showed concordant behavior with decreasing activity concentrations for measured and for corresponding simulated PET images. Simulation of dose-reduced datasets based on clinical (18)F-FDG PET data demonstrated the clinical applicability of the proposed data. Simulation of PET tracer dose reduction is possible with retrospective undersampling of list-mode data. Resulting simulated low-dose images have equivalent characteristics with PET images actually measured at lower doses and can be used to derive optimal tracer dose regimes.

Comparison of [18F]-FMISO, [18F]-FAZA and [18F]-HX4 for PET imaging of hypoxia – a simulation study

ABSTRACTBackground. To investigate the effect of hypoxia tracer properties on positron emission tomography (PET) image quality for three tracers [18F]-fluoromisonidazole (FMISO), [18F]-fluoroazomycinarabinoside (FAZA) and [18F]-flortanidazole (HX4), using mathematical simulations based on microscopic tumor tissue sections.Material and methods. Oxygen distribution and tracer binding was mathematically simulated on immunohistochemically stained cross-sections of tumor xenografts. Tracer diffusion properties were determined based on available literature. Blood activity and clearance over a four-hour period post-injection (p.i.) were derived from clinical dynamic PET scans of patients suffering from head and neck or bronchial cancer. Simulations were performed both for average patient blood activities and for individual patients, and image contrast between normoxic and hypoxic tissue areas was determined over this four-hour period p.i.Results. On average, HX4 showed a six-fold higher clearance than FMISO and an almost three-fold higher clearance than FAZA based on the clinical PET data. The absolute variation in clearance was significantly higher for HX4 than for FMISO (standard deviations of 5.75 *10-5 s-1 vs. 1.55 *10-5 s-1). The absolute tracer activity in these scans at four hours p.i. was highest for FMISO and lowest for HX4. Simulated contrast at four hours p.i. was highest for HX4 (2.39), while FMISO and FAZA were comparable (1.67 and 1.75, respectively). Variations in contrast of 7–11% were observed for each tracer depending on the vascularization patterns of the chosen tissue. Higher variations in clearance for HX4 resulted in an increased inter-patient variance in simulated contrast at four hours p.i.Conclusions. In line with recent experimental and clinical data, the results suggest that HX4 is a promising new tracer that provides high image contrast four hours p.i., though inter-patient variance can be very high. Nevertheless, the widely used tracer FMISO provides a robust and reproducible signal four hours p.i., but with a lower contrast. The simulations revealed tracer clearance to be the key factor in determining image contrast.

Model-free quantification of dynamic PET data using nonparametric deconvolution.

Dynamic positron emission tomography (PET) data are usually quantified using compartment models (CMs) or derived graphical approaches. Often, however, CMs either do not properly describe the tracer kinetics, or are not identifiable, leading to nonphysiologic estimates of the tracer binding. The PET data are modeled as the convolution of the metabolite-corrected input function and the tracer impulse response function (IRF) in the tissue. Using nonparametric deconvolution methods, it is possible to obtain model-free estimates of the IRF, from which functionals related to tracer volume of distribution and binding may be computed, but this approach has rarely been applied in PET. Here, we apply nonparametric deconvolution using singular value decomposition to simulated and test-retest clinical PET data with four reversible tracers well characterized by CMs ([(11)C]CUMI-101, [(11)C]DASB, [(11)C]PE2I, and [(11)C]WAY-100635), and systematically compare reproducibility, reliability, and identifiability of various IRF-derived functionals with that of traditional CMs outcomes. Results show that nonparametric deconvolution, completely free of any model assumptions, allows for estimates of tracer volume of distribution and binding that are very close to the estimates obtained with CMs and, in some cases, show better test-retest performance than CMs outcomes.

Surface-based partial-volume correction for high-resolution PET

Tissue radioactivity concentrations, measured with positron emission tomography (PET) are subject to partial volume effects (PVE) due to the limited spatial resolution of the scanner. Last generation high-resolution PET cameras with a full width at half maximum (FWHM) of 2–4mm are less prone to PVEs than previous generations. Corrections for PVEs are still necessary, especially when studying small brain stem nuclei or small variations in cortical neuroreceptor concentrations which may be related to cytoarchitectonic differences. Although several partial-volume correction (PVC) algorithms exist, these are frequently based on a priori assumptions about tracer distribution or only yield corrected values of regional activity concentrations without providing PVE corrected images. We developed a new iterative deconvolution algorithm (idSURF) for PVC of PET images that aims to overcome these limitations by using two innovative techniques: 1) the incorporation of anatomic information from a cortical gray matter surface representation, extracted from magnetic resonance imaging (MRI) and 2) the use of anatomically constrained filtering to attenuate noise. PVE corrected images were generated with idSURF implemented into a non-interactive processing pipeline. idSURF was validated using simulated and clinical PET data sets and compared to a frequently used standard PVC method (Geometric Transfer Matrix: GTM). The results on simulated data sets show that idSURF consistently recovers accurate radiotracer concentrations within 1–5% of true values. Both radiotracer concentrations and non-displaceable binding potential (BPnd) values derived from clinical PET data sets with idSURF were highly correlated with those obtained with the standard PVC method (R2=0.99, error=0%–3.2%). These results suggest that idSURF is a valid and potentially clinically useful PVC method for automatic processing of large numbers of PET data sets.

Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification

The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.

Respiratory motion blur identification and reduction in ungated thoracic PET imaging

Respiratory motion results in significant motion blur in thoracic positron emission tomography (PET) imaging. Existing approaches to correct the blurring artifact involve acquiring the images in gated mode and using complicated reconstruction algorithms. In this paper, we propose a post-reconstruction framework to estimate respiratory motion and reduce the motion blur of PET images acquired in ungated mode. Our method includes two steps: one is to use minmax directional derivative analysis and local auto-correlation analysis to identify the two parameters blur direction and blur extent, respectively, and another is to employ WRL, à trous wavelet-denoising modified Richardson–Lucy (RL) deconvolution, to reduce the motion blur based on identified parameters. The mobile phantom data were first used to test the method before it was applied to 32 cases of clinical lung tumor PET data. Results showed that the blur extent of phantom images in different directions was accurately identified, and WRL can remove the majority of motion blur within ten iterations. The blur extent of clinical images was estimated to be 12.1 ± 3.7 mm in the direction of 74 ± 3° relative to the image horizontal axis. The quality of clinical images was significantly improved, both from visual inspection and quantitative evaluation after deconvolution. It was demonstrated that WRL outperforms RL and a Wiener filter in reducing the motion blur with one to two more iterations. The proposed method is easy to implement and thus could be a useful tool to reduce the effect of respiration in ungated thoracic PET imaging.

Controlling Familywise Error Rate for Matched Subspace Detection in Dynamic FDG PET

Detection of small lesions in fluorodeoxyglucose (FDG) positron emission tomography (PET) is limited by image resolution and low signal to noise ratio. We have previously described a matched subspace detection method that uses the time activity curve to distinguish tumors from background in dynamic FDG PET. Applying this algorithm on a voxel by voxel basis throughout the dynamic image produces a test statistic image or "map" which on thresholding indicates the potential locations of secondary or metastatic tumors. In this paper, we describe a thresholding method that controls familywise error rate (FWER) for the matched subspace detection statistical map. The method involves three steps. First, the PET image is segmented into several homogeneous regions. Then, the statistical map is normalized to a zero mean unit variance Gaussian random field. Finally, the images are thresholded at a fixed FWER by estimating their spatial smoothness and applying a random field theory maximum statistic approach. We evaluate this thresholding method using digital phantoms generated from clinical dynamic images. We also present an application of the proposed approach to clinical PET data from a breast cancer patient with metastatic disease.

Lesion Detection in Dynamic FDG-PET Using Matched Subspace Detection

We describe a matched subspace detection algorithm to assist in the detection of small tumors in dynamic positron emission tomography (PET) images. The algorithm is designed to differentiate tumors from background using the time activity curves (TACs) that characterize the uptake of PET tracers. TACs are modeled using linear subspaces with additive Gaussian noise. Using TACs from a primary tumor region of interest (ROI) and one or more background ROIs, each identified by a human observer, two linear subspaces are identified. Applying a matched subspace detector to these identified subspaces on a voxel-by-voxel basis throughout the dynamic image produces a test statistic at each voxel which on thresholding indicates potential locations of secondary or metastatic tumors. The detector is derived for three cases: using a single TAC with white noise of unknown variance, using a single TAC with known noise covariance, and detection using multiple TACs within a small ROI with known noise covariance. The noise covariance is estimated for the reconstructed image from the observed sinogram data. To evaluate the proposed method, a simulation-based receiver operating characteristic (ROC) study for dynamic PET tumor detection is designed. The detector uses a dynamic sequence of frame-by-frame 2-D reconstructions as input. We compare the performance of the subspace detectors with that of a Hotelling observer applied to a single frame image and of the Patlak method applied to the dynamic data. We also show examples of the application of each detection approach to clinical PET data from a breast cancer patient with metastatic disease.

Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model

The widespread application of positron emission tomography (PET) in clinical oncology has driven this imaging technology into a number of new research and clinical arenas. Increasing numbers of patient scans have led to an urgent need for efficient data handling and the development of new image analysis techniques to aid clinicians in the diagnosis of disease and planning of treatment. Automatic quantitative assessment of metabolic PET data is attractive and will certainly revolutionize the practice of functional imaging since it can lower variability across institutions and may enhance the consistency of image interpretation independent of reader experience. In this paper, a novel automated system for the segmentation of oncological PET data aiming at providing an accurate quantitative analysis tool is proposed. The initial step involves expectation maximization (EM)-based mixture modeling using a k-means clustering procedure, which varies voxel order for initialization. A multiscale Markov model is then used to refine this segmentation by modeling spatial correlations between neighboring image voxels. An experimental study using an anthropomorphic thorax phantom was conducted for quantitative evaluation of the performance of the proposed segmentation algorithm. The comparison of actual tumor volumes to the volumes calculated using different segmentation methodologies including standard k-means, spatial domain Markov Random Field Model (MRFM), and the new multiscale MRFM proposed in this paper showed that the latter dramatically reduces the relative error to less than 8% for small lesions (7 mm radii) and less than 3.5% for larger lesions (9 mm radii). The analysis of the resulting segmentations of clinical oncologic PET data seems to confirm that this methodology shows promise and can successfully segment patient lesions. For problematic images, this technique enables the identification of tumors situated very close to nearby high normal physiologic uptake. The use of this technique to estimate tumor volumes for assessment of response to therapy and to delineate treatment volumes for the purpose of combined PET/CT-based radiation therapy treatment planning is also discussed.

Joint estimation of dynamic PET images and temporal basis functions using fully 4D ML-EM

A fully 4D joint-estimation approach to reconstruction of temporal sequences of 3D positron emission tomography (PET) images is proposed. The method estimates both a set of temporal basis functions and the corresponding coefficient for each basis function at each spatial location within the image. The joint estimation is performed through a fully 4D version of the maximum likelihood expectation maximization (ML-EM) algorithm in conjunction with two different models of the mean of the Poisson measured data. The first model regards the coefficients of the temporal basis functions as the unknown parameters to be estimated and the second model regards the temporal basis functions themselves as the unknown parameters. The fully 4D methodology is compared to the conventional frame-by-frame independent reconstruction approach (3D ML-EM) for varying levels of both spatial and temporal post-reconstruction smoothing. It is found that using a set of temporally extensive basis functions (estimated from the data by 4D ML-EM) significantly reduces the spatial noise when compared to the independent method for a given level of image resolution. In addition to spatial image quality advantages, for smaller regions of interest (where statistical quality is often limited) the reconstructed time–activity curves show a lower level of bias and a lower level of noise compared to the independent reconstruction approach. Finally, the method is demonstrated on clinical 4D PET data.

Accurate attenuation correction in PET using short transmission scans and consistency information

The authors show how the consistency conditions of the Radon transform can be used to aid attenuation correction in positron emission tomography (PET) using short transmission scans. The technique is expected to be useful in situations where limited time is available for transmission imaging (e.g., whole-body PET). The proposed method uses 2-min transmission scans that are reconstructed and then segmented into regions of uniform attenuation. Consistency information is used to determine the thresholds for segmentation of the transmission image and the attenuation coefficients. In particular, a downhill simplex algorithm is used to find the parameters that are most consistent with the measured emission data. One potential advantage of the proposed technique over conventional segmentation methods (which work purely on the transmission data) is that the emission data are used to drive the segmentation process. Therefore, the technique should produce the attenuation image that is most appropriate for attenuation correction. The algorithm is tested using simulated data and compared with an adaptive thresholding technique using clinical PET data. The results show that the method produces reconstructed images with similar accuracy and noise levels to those obtained using a 10 min transmission scan. The results also show that the proposed technique has some advantages over the adaptive thresholding technique when imaging the abdomen. The authors conclude that the proposed technique may provide a viable means of producing quantitatively accurate whole-body PET images in a clinically feasible time.

Integrated image analysis solutions for PET datasets in damaged brain.

To identify and discuss the problems inherent in the processing of multiparametric functional imaging datasets from patients with acute brain injury, using "triple oxygen" positron emission tomography (PET) as an example. To present an integrated imaging solution for analysis of such datasets and report on its use in practice. Structured analysis of the steps in image analysis for triple oxygen studies in patients with acute brain injury, subarachnoid haemorrhage and carotid artery disease was undertaken. Systematic identification of the drawbacks of conventional manual processing techniques, which make use of modules from different commercially available software packages was carried out. This analysis informed the construction of an image analysis gateway that addressed these issues. We describe an integrated image analysis gateway (PETAn) that provides for the automated integration of all processing steps, along with outputs that detail analysis stages, error logs and results. Implementation of PETAn allowed batch processing of clinical PET data sets, reduced the level of technical expertise required for analysis, improved the quality of results and achieved significant reductions in operator analysis time. While modules from several imaging software suites can be usefully combined to allow analysis of multiparametric PET data sets from patients with acute brain injury, sequential use of these modules for various steps in image analysis is fraught with difficulty. Careful automation and integration of these steps greatly facilitates the interrogation of information-rich data sets and increases research productivity.

Comparison of 3-D reconstruction with 3D-OSEM and with FORE+OSEM for PET

The combination of Fourier rebinning (FORE) and the ordered subsets expectation-maximization (OSEM), a fast statistical algorithm, appears as a promising alternative to the fully three-dimensional (3-D) iterative approach for clinical positron emission tomography (PET) data. In this paper, we evaluated the properties of FORE+OSEM and compared it with fully 3-D OSEM using both simulations and data acquired by commercial scanners. The aim is to determine to what extent the speed advantage of FORE+OSEM is paid for by a possible degradation of image quality in the case of noisy clinical PET data. A forward- and back-projection pair based on a line integral model was used in two-dimensional OSEM and 3-D OSEM (3D-OSEM) instead of a system matrix. Different variants of both approaches have been studied with simulations in terms of contrast-noise tradeoff. Two variants--FORE+OSEM with attenuation weighting (AW) [FORE+OSEM(AW)] and 3D-OSEM with attenuation-normalization weighting (ANSP) and a shifted-Poisson (SP) model [3D-OSEM(ANSP)]--were compared with measured phantom data and patient data. Based on the results from both simulations and measured data, we conclude that: 1) both attenuation (-normalization) weighting and the SP model improve the image quality but slow down the convergence and 2) despite its approximate nature, FORE+OSEM does not show apparent image degradation compared with 3D-OSEM for data with a noise level typical of a whole-body FDG scan.