New detector approaches in Positron Emission Tomography imaging will play an important role in reducing costs, lowering administered radiation doses, and improving overall performance. PETALO employs liquid xenon as the active scintillating medium and UV-sensitive silicon photomultipliers for scintillation readout. The scintillation time in liquid xenon is fast enough to register time-of-flight information for each detected coincidence, and sufficient scintillation is produced with low enough fluctuations to obtain good energy resolution. The present simulation study examines a full-body-sized PETALO detector and evaluates its potential performance in PET image reconstruction.

The quality of reconstructed dynamic PET images, as well as the statistical reliability of the estimated pharmacokinetic parameters is often compromised by high levels of statistical noise, particularly at the voxel level. Many denoising strategies have been proposed, both in the temporal and spatial domain, which substantially improve the signal to noise ratio of the reconstructed dynamic images. However, although most filtering approaches are fairly successful in reducing the spatio-temporal inter-voxel variability, they may also average out or completely eradicate the critically important temporal signature of a transient neurotransmitter activation response that may be present in a non-steady state dynamic PET study. In this work, we explore an approach towards temporal denoising of non-steady state dynamic PET images using an artificial neural network, which was trained to identify the temporal profile of a time-activity curve, while preserving any potential activation response. We evaluated the performance of a feed-forward perceptron neural network to improve the signal to noise ratio of dynamic [11C]raclopride activation studies and compared it with the widely used highly constrained back projection (HYPR) filter. Results on both simulated Geant4 Application for Tomographic Emission data of a realistic rat brain phantom and experimental animal data of a freely moving animal study showed that the proposed neural network can efficiently improve the noise characteristics of dynamic data in the temporal domain, while it can lead to a more reliable estimation of voxel-wise activation response in target region. In addition, improvements in signal-to-noise ratio achieved by denoising the dynamic data using the proposed neural network led to improved accuracy and precision of the estimated model parameters of the lp-ntPET model, compared to the HYPR filter. The performance of the proposed denoising approach strongly depends on the amount of noise in the dynamic PET data, with higher noise leading to substantially higher variability in the estimated parameters of the activation response. Overall, the feed-forward network led to a similar performance as the HYPR filter in terms of spatial denoising, but led to notable improvements in terms of temporal denoising, which in turn improved the estimation activation parameters.

A deep learning pipeline was developed and used to localize and classify a variety of implants in the femur contained in whole-body post-mortem computed tomography (PMCT) scans. The results provide a proof-of-principle approach for labelling content not described in medical/autopsy reports. The pipeline, which incorporated residual networks and an autoencoder, was trained and tested using n = 450 full-body PMCT scans. For the localization component, Dice scores of 0.99, 0.96, and 0.98 and mean absolute errors of 3.2, 7.1, and 4.2 mm were obtained in the axial, coronal, and sagittal views, respectively. A regression analysis found the orientation of the implant to the scanner axis and also the relative positioning of extremities to be statistically significant factors. For the classification component, test cases were properly labelled as nail (N+), hip replacement (H+), knee replacement (K+) or without-implant (I-) with an accuracy >97%. The recall for I- and H+ cases was 1.00, but fell to 0.82 and 0.65 for cases with K+ and N+. This semi-automatic approach provides a generalized structure for image-based labelling of features, without requiring time-consuming segmentation.

AIM:Oxidative stress is strongly implicated in many psychiatric disorders, which has resulted in the development of new interventions to attempt to perturb this pathology. A great deal of attention has been paid to glutathione, which is the brain's dominant antioxidant and plays a fundamental role in removing free radicals and other reactive oxygen species. Measurement of glutathione concentration in the brain in vivo can provide information on redox status and potential for oxidative stress to develop. Glutathione might also represent a marker to assess treatment response.METHODS:This paper systematically reviews studies that assess glutathione concentration (measured using magnetic resonance spectroscopy) in various mental health conditions.RESULTS:There is limited evidence showing altered brain glutathione concentration in mental disorders; the best evidence suggests glutathione is decreased in depression, but is not altered in bipolar disorder. The review then outlines the various methodological options for acquiring glutathione data using spectroscopy.CONCLUSIONS:Analysis of the minimum effect size measurable in existing studies indicates that increased number of participants is required to measure subtle but possibly important differences and move the field forward.

In order to extract the most powerful predictive models from data collected within the PRONIA study, diverse information sources must be combined. PRONIA aims to combine information from a range of study sites across Europe as well as from a diverse range of information sources. For each subject, neurocognitive, neuroimaging and clinically observed data has been collected that is intended to provide the basis for the development of predictive models for use in individualised diagnosis and prediction. However, as yet it is unclear as to which elements (or combination) of the measured data provide optimal predictive capacity and which features will generalize best. In order to combine data from a diverse range of sources a number of approaches may be considered. While it is initially attractive to concatenate the features gathered from each modality, this approach is problematic in two ways. Not only do the appropriate pre-processing steps differ between modalities, but the high dimensionality of imaging data (in comparison to neurocognitive measures) may alter the way each modality contributes to the decision function during learning. Instead, we investigate more simplistic learning approaches in an initial step that produces a single outcome for each modality considered. In a second step these outcomes are combined to generate a final estimate of the target class. In this investigation neurocognitive and neuroimaging data, collected as part of the PRONIA study, were considered as features for prediction of clinically observed global function, measured at the same time-point. Each neurocognitive test, applied as part of the PRONIA battery, was considered as an independent modality, as were each of a range of MRI-based neuroimaging measures (from structural, functional and diffusion imaging). Support Vector Classification (SVC) was conducted for each modality, with the target class defined as a score of 65 or less on the Global Assessment of Function. Both linear classification and the use of radial basis functions were explored within the initial modality-independent learning phase as well as during modality fusion as part of the second learning phase. Repeated, nested, cross-validation was employed in both stages in order ensure robust estimates of generalisation. Because each modality is reduced to a single measure in the first stage, each can contribute on an equal basis to the predictive outcome in the second while allowing inter-modality interaction. While SVC models do not naturally provide probabilistic outcomes, the distance of each point to the separating hyperplane can be scaled to represent the relative class probabilities. Predictions obtained at the first stage not only provide for the second phase of learning, but also provide a means to assess each modality for predictive accuracy. Correlations between the predictions from each mode provide information as to which combination of data may contribute constructively to the final outcome while learning approaches within the second phase can also be used to identify the most useful predictors. The two-stage learning framework provides a useful approach to learning that allows assessment of each separate data stream as well as the fused-prediction outcome. The contribution of each data stream to the final prediction may be explored while interactions between data streams can also be contextualised. However, more subtle interactions between data, particularly at the initial input stage, may be difficult to observe and so the extension of this approach to more structured data-fusion and is considered.

Optical motion tracking and motion compensation reconstruction algorithms enable the acquisition of quantitative measurements of brain function on conscious and freely moving rodents. However, motion corrected images often exhibit reduced resolution when compared with their stationary counterparts. This apparent loss of resolution can be attributed, among others, to jitter/noise in the measured motion estimates and brief periods of fast animal motion with insufficient motion sampling rate. In this paper we propose a novel methodology to experimentally characterise the residual blurring in the motion corrected images by measuring the motion-dependent point spread function (PSF) in image space using a point source rigidly attached on the moving object. We evaluated the proposed methodology using experimental phantom measurements acquired on the microPET Focus220 scanner. The motion dependent point spread function was extracted from the point source attached on the moving phantom, after motion correcting the images and modelling the point source in image space using an Expectation Maximisation algorithm as a weighted sum of two Gaussian distributions. Finally, the fitted blurring kernels were used within an iterative Lucy-Richardson algorithm to mitigate the deblurring in the motion corrected images. For motion typically encountered in an awake rat study, results showed that unprocessed motion corrected images suffer from lower resolution compared to a stationary acquisition. The shape of the measured blurring kernel, correlated well with the motion trajectory, while the width of the kernel was proportional to the speed/acceleration of the object. Post-processed images using the corresponding motion dependent blurring kernel appeared not only qualitatively, but also quantitatively (in terms of contrast) more similar to their stationary counterpart. We conclude that it is possible to experimentally measure the residual motion-dependent blurring kernel and use it within a post reconstruction deconvolution framework to improve resolution and quantification of motion corrected images.

Computational methods, such as the linear parametric neurotransmitter PET (lp-ntPET) method, have been developed to characterize the transient changes in radiotracer kinetics in the target tissue during endogenous neurotransmitter release. In this paper, we describe and evaluate a parametric reconstruction algorithm that uses an expectation maximization framework, along with the lp-ntPET model, to estimate the endogenous neurotransmitter response to stimuli directly from the measured PET data. Computer simulations showed that the proposed direct reconstruction method offers improved accuracy and precision for the estimated timing parameters of the neurotransmitter response at the voxel level ( td=1±2 min, for activation onset bias and standard deviation) compared with conventional post reconstruction modeling ( td=4±7 min). In addition, we applied the proposed direct parameter estimation methodology to a [11C]raclopride displacement study of an awake rat and generated parametric maps illustrating the magnitude of ligand displacement from striatum. Although the estimated parametric maps of activation magnitude obtained from both direct and post reconstruction methodologies suffered from false positive activations, the proposed direct reconstruction framework offered more reliable parametric maps when the activation onset parameter was constrained.

Importance:Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses.Objective:To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning.Design, Setting, and Participants:This multisite naturalistic study followed up patients in CHR states, with ROD, and with recent-onset psychosis, and healthy control participants for 18 months in 7 academic early-recognition services in 5 European countries. Participants were recruited between February 2014 and May 2016, and data were analyzed from April 2017 to January 2018.ain Outcomes and Measures:Performance and generalizability of prognostic models.Results:A total of 116 individuals in CHR states (mean [SD] age, 24.0 [5.1] years; 58 [50.0%] female) and 120 patients with ROD (mean [SD] age, 26.1 [6.1] years; 65 [54.2%] female) were followed up for a mean (SD) of 329 (142) days. Machine learning predicted the 1-year social-functioning outcomes with a balanced accuracy of 76.9% of patients in CHR states and 66.2% of patients with ROD using clinical baseline data. Balanced accuracy in models using structural neuroimaging was 76.2% in patients in CHR states and 65.0% in patients with ROD, and in combined models, it was 82.7% for CHR states and 70.3% for ROD. Lower functioning before study entry was a transdiagnostic predictor. Medial prefrontal and temporo-parieto-occipital gray matter volume (GMV) reductions and cerebellar and dorsolateral prefrontal GMV increments had predictive value in the CHR group; reduced mediotemporal and increased prefrontal-perisylvian GMV had predictive value in patients with ROD. Poor prognoses were associated with increased risk of psychotic, depressive, and anxiety disorders at follow-up in patients in the CHR state but not ones with ROD. Machine learning outperformed expert prognostication. Adding neuroimaging machine learning to clinical machine learning provided a 1.9-fold increase of prognostic certainty in uncertain cases of patients in CHR states, and a 10.5-fold increase of prognostic certainty for patients with ROD.Conclusions and Relevance:Precision medicine tools could augment effective therapeutic strategies aiming at the prevention of social functioning impairments in patients with CHR states or with ROD.

Image quality of 68Ga Positron Emission Tomography (PET) studies is impacted by its relatively high positron range compared with 18F, thus limiting spatial resolution. This simulation study describes a post-reconstruction 2-dimensional (2D) positron range correction (post-PRC) using the Richardson-Lucy (RL) de-blurring method for 68Ga. The method proposed is based on a 2-dimensional de-blurring kernel derived in bone, soft-tissue and lung medium. and the kernel was applied to the simulated image of the NEMA Image Quality phantom geometry with a target-to-background ratio of 8:1. The post-PRC method can recover spatial resolution loss by approximately 1%, 9% and 56% in bone, water and lung medium, respectively. However, the proposed de-blurring method increases image noise, particularly in the lung medium. This convenient post-processing de-blurring approach does not require raw data or modification to the reconstruction algorithm allowing it to be adopted to the reconstructed volumes of any PET scanner.

In emission tomographic imaging, the stochastic origin ensembles algorithm provides unique information regarding the detected counts given the measured data. Precision in both voxel and region-wise parameters may be determined for a single data set based on the posterior distribution of the count density allowing uncertainty estimates to be allocated to quantitative measures. Uncertainty estimates are of particular importance in awake animal neurological and behavioral studies for which head motion, unique for each acquired data set, perturbs the measured data. Motion compensation can be conducted when rigid head pose is measured during the scan. However, errors in pose measurements used for compensation can degrade the data and hence quantitative outcomes. In this investigation motion compensation and detector resolution models were incorporated into the basic origin ensembles algorithm and an efficient approach to computation was developed. The approach was validated against maximum liklihood-expectation maximisation and tested using simulated data. The resultant algorithm was then used to analyse quantitative uncertainty in regional activity estimates arising from changes in pose measurement precision. Finally, the posterior covariance acquired from a single data set was used to describe correlations between regions of interest providing information about pose measurement precision that may be useful in system analysis and design. The investigation demonstrates the use of origin ensembles as a powerful framework for evaluating statistical uncertainty of voxel and regional estimates. While in this investigation rigid motion was considered in the context of awake animal PET, the extension to arbitrary motion may provide clinical utility where respiratory or cardiac motion perturb the measured data.

Awake and/or freely moving small animal single photon emission imaging allows the continuous study of molecules exhibiting slow kinetics without the need to restrain or anaesthetise the animals. Estimating motion free projections in freely moving small animal planar imaging can be considered as a limited angle tomography problem, except that we wish to estimate the 2D planar projections rather than the 3D volume, where the angular sampling in all three axes depends on the rotational motion of the animal. In this study, we hypothesise that the motion corrected planar projections estimated by reconstructing an estimate of the 3D volume using an iterative motion compensating reconstruction algorithm and integrating it along the projection path, will closely match the true, motion-less, planar distribution regardless of the object motion. We tested this hypothesis for the case of rigid motion using Monte-Carlo simulations and experimental phantom data based on a dual opposed detector system, where object motion was modelled with 6 degrees of freedom. In addition, we investigated the quantitative accuracy of the regional activity extracted from the geometric mean of opposing motion corrected planar projections. Results showed that it is feasible to estimate qualitatively accurate motion-corrected projections for a wide range of motions around all 3 axes. Errors in the geometric mean estimates of regional activity were relatively small and within 10% of expected true values. In addition, quantitative regional errors were dependent on the observed motion, as well as on the surrounding activity of overlapping organs. We conclude that both qualitatively and quantitatively accurate motion-free projections of the tracer distribution in a rigidly moving object can be estimated from dual opposed detectors using a correction approach within an iterative reconstruction framework and we expect this approach can be extended to the case of non-rigid motion.

A high-resolution PET system, which incorporates a silicon detector probe into a conventional PET scanner, has been proposed to obtain increased image quality in a limited region of interest. Detailed simulation studies have previously shown that the additional probe information improves the spatial resolution of the reconstructed image and increases lesion detectability, with no cost to other image quality measures. The current study expands on the previous work by using a laboratory prototype of the silicon PET-probe system to examine the resolution improvement in an experimental setting. Two different versions of the probe prototype were assessed, both consisting of a back-to-back pair of 1-mm thick silicon pad detectors, one arranged in 32 × 16 arrays of 1.4 mm × 1.4 mm pixels and the other in 40 × 26 arrays of 1.0 mm × 1.0 mm pixels. Each detector was read out by a set of VATAGP7 ASICs and a custom-designed data acquisition board which allowed trigger and data interfacing with the PET scanner, itself consisting of BGO block detectors segmented into 8 × 6 arrays of 6 mm × 12 mm × 30 mm crystals. Limited-angle probe data was acquired from a group of Na-22 point-like sources in order to observe the maximum resolution achievable using the probe system. Data from a Derenzo-like resolution phantom was acquired, then scaled to obtain similar statistical quality as that of previous simulation studies. In this case, images were reconstructed using measurements of the PET ring alone and with the inclusion of the probe data. Images of the Na-22 source demonstrated a resolution of 1.5 mm FWHM in the probe data, the PET ring resolution being approximately 6 mm. Profiles taken through the image of the Derenzo-like phantom showed a clear increase in spatial resolution. Improvements in peak-to-valley ratios of 50% and 38%, in the 4.8 mm and 4.0 mm phantom features respectively, were observed, while previously unresolvable 3.2 mm features were brought to light by the addition of the probe. These results support the possibility of improving the image resolution of a clinical PET scanner using the silicon PET-probe.

Current positron emission tomography (PET) systems use temporally localised coincidence events discriminated by energy and time-of-flight information. The two annihilation photons are in an entangled polarisation state and, in principle, additional information from the polarisation correlation of photon pairs could be used to improve the accuracy of coincidence classification. In a previous study, we demonstrated that in principle, the polarisation correlation information could be transferred to an angular correlation in the distribution of scattered photon pairs in a planar Compton camera system. In the present study, we model a source-phantom-detector system using Geant4 and we develop a coincidence classification scheme that exploits the angular correlation of scattered annihilation quanta to improve the accuracy of coincidence detection. We find a [Formula: see text] image quality improvement in terms of the peak signal-to-noise ratio when scattered coincidence events are discriminated solely by their angular correlation, thus demonstrating the feasibility of this novel classification scheme. By integrating scatter events (both single-single and single-only) with unscattered coincidence events discriminated using conventional methods, our results suggest that Compton-PET may be a promising candidate for optimal emission tomographic imaging.

Regional quantification is often considered the end-point of many studies in Positron Emission Tomography (PET). However, in neurological small animal imaging, features can be sufficiently small that device resolution and voxel granularity inhibit the definition of segmentation boundaries that closely conform to organs or regions of interest. Even if well defined, estimation of uncertainty over small regions is problematic, particularly given that in some cases - such as in awake animal imaging - studies are unique and non-repeatable. The Origin Ensembles algorithm is an approach to image reconstruction based on Bayesian inference which utilises Monte-Carlo driven sampling to determine the posterior distribution of the emission counts given the measured data. The algorithm explores the full posterior and so can be used to estimate measures of uncertainty in quantitative parameters drawn from the data. However, being inference based, Origin Ensembles is sensitive to the dimension of the parameter space over which estimates are made. In this investigation the dimension of the image space is reduced using a regional segmentation based on an initial ML-EM reconstruction of detected data. Origin Ensembles is used to reconstruct data directly to the regions of interest over which quantification is desired providing estimation of the full posterior distribution for each region. Origin Ensembles sampling allows the uncertainty in quantitative values to be determined both in terms of parameter variance and the covariance between recovered regional intensities. Direct regional reconstruction was employed in this study using simulated data and was shown to enhance the precision and accuracy of recovered values.

Motion corrected images from awake and freely moving animals often exhibit reduced resolution when compared to their stationary counterparts. This could be attributed to the combination of brief periods of fast animal motion and insufficient motion sampling speed. In this paper we hypothesise that we can measure the motion dependent point spread function of a given study and mitigate the motion blurring artifacts in the reconstructed images, in a similar way that a measured system response point spread function can improve resolution due to geometric effects (e.g. parallax errors). We investigated this hypothesis on a set of experimentally measured phantom data, which underwent a series of distinctively different motion patterns, ranging from slow to fast. Preliminary results showed that motion corrected images have reduced resolution compared to the stationary image and noticeable motion blurring artefacts, particularly for fast speed/acceleration settings. In addition, images deconvolved after reconstruction with the measured motion dependent PSF appear to be sharper compared to their unprocessed counterparts, yet without completely eliminating the motion blurring artefacts. Work is in progress to refine the methodology, by decomposing the geometric and motion components of the PSF, as well as including the deconvolution within the reconstruction algorithm.

Image space decomposition based on tetrahedral voxels are interesting candidates for use in emission tomography. Tetrahedral voxels provide many of the advantages of point clouds with irregular spacing, such as being intrinsically multi-resolution, yet they also serve as a volumetric partition of the image space and so are comparable to more standard cubic voxels. Additionally, non-rigid displacement fields can be applied to the tetrahedral mesh in a straight-forward manner. So far studies incorporating tetrahedral decomposition of the image space have concentrated on pre-calculated, node-based, system matrix elements which reduces the flexibility of the tetrahedral approach and the capacity to accurately define regions of interest. Here, a list-mode on-the-fly calculation of the system matrix elements is described using a tetrahedral decomposition of the image space and volumetric elements-voxels. The algorithm is demonstrated in the context of awake animal PET which may require both rigid and non-rigid motion compensation, as well as quantification within small regions of the brain. This approach allows accurate, event based, motion compensation including non-rigid deformations.