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While COVID-19 research has seen an explosion in the literature, the impact of pandemic-related societal and lifestyle disruptions on brain health among the uninfected remains underexplored. However, a global increase in the prevalence of fatigue, brain fog, depression and other "sickness behavior"-like symptoms implicates a possible dysregulation in neuroimmune mechanisms even among those never infected by the virus. We compared fifty-seven 'Pre-Pandemic' and fifteen 'Pandemic' datasets from individuals originally enrolled as control subjects for various completed, or ongoing, research studies available in our records, with a confirmed negative test for SARS-CoV-2 antibodies. We used a combination of multimodal molecular brain imaging (simultaneous positron emission tomography / magnetic resonance spectroscopy), behavioral measurements, imaging transcriptomics and serum testing to uncover links between pandemic-related stressors and neuroinflammation. Healthy individuals examined after the enforcement of 2020 lockdown/stay-at-home measures demonstrated elevated brain levels of two independent neuroinflammatory markers (the 18 kDa translocator protein, TSPO, and myoinositol) compared to pre-lockdown subjects. The serum levels of two inflammatory markers (interleukin-16 and monocyte chemoattractant protein-1) were also elevated, although these effects did not reach statistical significance after correcting for multiple comparisons. Subjects endorsing higher symptom burden showed higher TSPO signal in the hippocampus (mood alteration, mental fatigue), intraparietal sulcus and precuneus (physical fatigue), compared to those reporting little/no symptoms. Post-lockdown TSPO signal changes were spatially aligned with the constitutive expression of several genes involved in immune/neuroimmune functions. This work implicates neuroimmune activation as a possible mechanism underlying the non-virally-mediated symptoms experienced by many during the COVID-19 pandemic. Future studies will be needed to corroborate and further interpret these preliminary findings.

Emission-based attenuation correction (AC) meth-ods offer the possibility of overcoming quantification errors induced by conventional MR-based approaches in PET/MR imaging. However, the joint problem of determining AC and the activity of interest is strongly ill-posed in non-TOF PET. This can be improved by exploiting the extra information arising from low energy window photons, but the feasibility of this approach has only been studied with relatively simplistic analytic simulations so far. This manuscript aims to address some of the remaining challenges needed to handle realistic measurements; in particular, the detection efficiency (“normalisation”) estimation for each energy window is investigated. An energy-dependent detection efficiency model is proposed, accounting for the presence of unscattered events in the lower energy window due to detector scatter. Geometric calibration factors are estimated prior to the reconstruction for both scattered and unscattered events. Different reconstruction methods are also compared. Results show that geometric factors differ markedly between the energy windows and that our analytical model correspond in good approximation to Monte Carlo simulation; the multiple energy window reconstruction appears sensitive to input/model mismatch. Our method applies to Monte Carlo generated data but can be extended to measured data. This study is restricted to single scatter events.

We recently showed that patients with different chronic pain conditions (such as chronic low back pain, fibromyalgia, migraine and Gulf War illness) demonstrated elevated brain and/or spinal cord levels of the glial marker 18-kDa translocator protein (TSPO), which suggests that neuroinflammation might be a pervasive phenomenon observable across multiple aetiologically heterogeneous pain disorders. Interestingly, the spatial distribution of this neuroinflammatory signal appears to exhibit a degree of disease specificity (e.g. with respect to the involvement of the primary somatosensory cortex), suggesting that different pain conditions may exhibit distinct ‘neuroinflammatory signatures’. To explore this hypothesis further, we tested whether neuroinflammatory signal can characterize putative aetiological subtypes of chronic low back pain patients based on clinical presentation. Specifically, we explored neuroinflammation in patients whose chronic low back pain either did or did not radiate to the leg (i.e. ‘radicular’ versus ‘axial’ back pain). Fifty-four patients with chronic low back pain, 26 with axial back pain [43.7 ± 16.6 years old (mean ± SD)] and 28 with radicular back pain (48.3 ± 13.2 years old), underwent PET/MRI with 11C-PBR28, a second-generation radioligand for TSPO. 11C-PBR28 signal was quantified using standardized uptake values ratio (validated against volume of distribution ratio; n = 23). Functional MRI data were collected simultaneously to the 11C-PBR28 data (i) to functionally localize the primary somatosensory cortex back and leg subregions; and (ii) to perform functional connectivity analyses (in order to investigate possible neurophysiological correlations of the neuroinflammatory signal). PET and functional MRI measures were compared across groups, cross-correlated with one another and with the severity of ‘fibromyalgianess’ (i.e. the degree of pain centralization, or ‘nociplastic pain’). Furthermore, statistical mediation models were used to explore possible causal relationships between these three variables. For the primary somatosensory cortex representation of back/leg, 11C-PBR28 PET signal and functional connectivity to the thalamus were: (i) higher in radicular compared to axial back pain patients; (ii) positively correlated with each other; (iii) positively correlated with fibromyalgianess scores, across groups; and finally (iv) fibromyalgianess mediated the association between 11C-PBR28 PET signal and primary somatosensory cortex–thalamus connectivity across groups. Our findings support the existence of ‘neuroinflammatory signatures’ that are accompanied by neurophysiological changes and correlate with clinical presentation (in particular, with the degree of nociplastic pain) in chronic pain patients. These signatures may contribute to the subtyping of distinct pain syndromes and also provide information about interindividual variability in neuroimmune brain signals, within diagnostic groups, that could eventually serve as targets for mechanism-based precision medicine approaches.

Application of stochastic variance reduction algorithms to iterative PET reconstruction. We investigated the SAGA and SVRG algorithms for non-TOF PET image reconstruction. Both similated data and a patient data sets were used in the analysis. We found that the stochastic algorithms can improve convergence rate and eliminate behaviour, commonly know as limit cycle behaviour, from PET reconstruction within 5 epochs.

Positron emission tomography/computed tomography (PET/CT) lung imaging is highly sensitive to motion. Although several techniques exist to diminish motion artifacts, a few accounts for both tissue displacement and changes in density due to the compression and dilation of the lungs, which cause quantification errors. This article presents an experimental framework for joint activity image reconstruction and motion estimation in PET/CT, where the PET image and the motion are directly estimated from the raw data. Direct motion estimation methods for motion-compensated PET/CT are preferable as they require a single attenuation map only and result in optimal signal-to-noise ratio (SNR). Previous implementations, however, failed to address changes in density during respiration. We propose to account for such changes using the Jacobian determinant of the deformation fields. In a feasibility study, we demonstrate on a modified extended cardiac-torso (XCAT) phantom with breathing motion—where the lung density and activity vary—that our approach achieved better quantification in the lungs than conventional PET/CT joint activity image reconstruction and motion estimation that does not account for density changes. The proposed method resulted in lower bias and variance in the activity images, reduced mean relative activity error in the lung at the reference gate (−4.84% to −3.22%) and more realistic Jacobian determinant values.

Estimation of attenuation from positron emission tomography (PET) data only is of interest for hybrid PET-MR and systems where CT is not available or recommended. However, when using data from a single energy window, emission-based non-time-of-flight (TOF) PET attenuation correction (AC) methods suffer from “cross-talk” artifacts. Based on earlier work, this article explores the hypothesis that cross-talk can be reduced by using more than one energy window. We propose an algorithm for the simultaneous estimation of both activity and attenuation images, as well as, the scatter component of the measured data from a PET acquisition, using multiple energy windows. The model for the measurements is 3-D and accounts for the finite energy resolution of PET detectors; it is restricted to single scatter. The proposed energy-based simultaneous maximum likelihood reconstruction of activity and attenuation with photopeak scatter re-estimation algorithm is compared with simultaneous estimation from a single energy window simultaneous maximum likelihood reconstruction of activity and attenuation with photopeak scatter re-estimation. The evaluation is based on simulations using the characteristics of the Siemens mMR scanner. Phantoms of different complexity were investigated. In particular, a 3-D XCAT torso phantom was used to assess the inpainting of attenuation values within the lung region. Results show that the cross-talk present in non-TOF maximum likelihood reconstruction of activity and attenuation reconstructions is significantly reduced when using multiple energy windows and indicate that the proposed approach warrants further investigation.

SummaryBackgroundThe impact of COVID-19 on human health extends beyond the morbidity and death toll directly caused by the SARS-CoV-2 virus. In fact, accumulating evidence indicates a global increase in the incidence of fatigue, brain fog and depression, including among non-infected, since the pandemic onset. Motivated by previous evidence linking those symptoms to neuroimmune activation in other pathological contexts, we hypothesized that subjects examined after the enforcement of lockdown/stay-at-home measures would demonstrate increased neuroinflammation.MethodsWe performed simultaneous brain Positron Emission Tomography / Magnetic Resonance Imaging in healthy volunteers either before (n=57) or after (n=15) the 2020 Massachusetts lockdown, using [11C]PBR28, a radioligand for the glial marker 18 kDa translocator protein (TSPO). First, we compared [11C]PBR28 signal across pre- and post-lockdown cohorts. Then, we evaluated the link between neuroinflammatory signals and scores on a questionnaire assessing mental and physical impacts of the pandemic. Further, we investigated multivariate associations between the spatial pattern of [11C]PBR28 post-lockdown changes and constitutive brain gene expression in post-mortem brains (Allen Human Brain Atlas). Finally, in a subset (n=13 pre-lockdown; n=11 post-lockdown), we also used magnetic resonance spectroscopy to quantify brain (thalamic) levels of myoinositol (mIns), another neuroinflammatory marker.FindingsBoth [11C]PBR28 and mIns signals were overall stable pre-lockdown, but markedly elevated after lockdown, including within brain regions previously implicated in stress, depression and “sickness behaviors”. Moreover, amongst the post-lockdown cohort, subjects endorsing higher symptom burden showed higher [11C]PBR28 PET signal compared to those reporting little/no symptoms. Finally, the post-lockdown [11C]PBR28 signal changes were spatially aligned with the constitutive expression of several genes highly expressed in glial/immune cells and/or involved in neuroimmune signaling.InterpretationOur results suggest that pandemic-related stressors may have induced sterile neuroinflammation in healthy individuals that were not infected with SARS-CoV-2. This work highlights the possible impact of the COVID-19 pandemic-related lifestyle disruptions on human brain health.FundingR01-NS094306-01A1, R01-NS095937-01A1, R01-DA047088-01, The Landreth Family Foundation.

Several groups have considered the option of using multiple-energy window emission data to improve reconstruction accuracy for PET/MR imaging. However, the feasibility of this approach has only been studied with analytic simulations. To be able to handle realistic measurements a remaining challenge is related to the detection efficiencies (“normalisation”) for the different energy windows, which likely differ for scattered and unscattered events given the heterogeneity of photon energies, angles of incidence, and points of origination. In addition, the lower energy windows will potentially contain events that have been affected by scatter in the detector. This manuscript investigates the problem of estimating the normalisation factors for PET data acquired from multiple energy windows.

Penalised PET image reconstruction methods are often accelerated with the use of only a subset of the data at each update. It is known that many subset algorithms, such as Ordered Subset Expectation Maximisation, do not converge to a single solution but to a limit cycle, which can lead to variations between subsequent image estimates. A new class of stochastic variance reduction optimisation algorithms have been recently proposed for general optimisation problems. These methods aim to reduce the subset update variance by incorporating previous subset gradients into the update direction computation. This work applies three of these algorithms to iterative PET penalised reconstruction and exhibits superior performance to standard deterministic reconstruction methods after only a few epochs.

Statistical PET image reconstruction methods are often accelerated by the use of a subset of available projections at each iteration. It is known that many subset algorithms, such as ordered subset expectation maximisation, will not converge to a single solution but to a limit cycle. Reconstruction methods exist to relax the update step sizes of subset algorithms to obtain convergence, however, this introduces additional parameters that may result in extended reconstruction times. Another approach is to gradually decrease the number of subsets to reduce the effect of the limit cycle at later iterations, but the optimal iteration numbers for these reductions may be data dependent. We propose an automatic method to increase subset sizes so a reconstruction can take advantage of the acceleration provided by small subset sizes during early iterations, while at later iterations reducing the effects of the limit cycle behaviour providing estimates closer to the maximum a posteriori solution. At each iteration, two image updates are computed from a common estimate using two disjoint subsets. The divergence of the two update vectors is measured and, if too great, subset sizes are increased in future iterations. We show results for both sinogram and list mode data using various subset selection methodologies.

This study investigates the use of a synergistic edge-preserving prior for a maximum-likelihood reconstruction of PET activity and attenuation images. The effect of the prior was tested on different reconstruction methods: MLAA and our algorithm MLAA-EB-S. Simple simulations on cylindrical phantoms with an insert showed that the presence of a synergistic prior enforces cross-talk related edges when the images are reconstructed with non-TOF MLAA and increases the bias in both the reconstructed activity and attenuation images. However, benefits appear when the joint problem is less ill-posed, as in the case of our method MLAA-EB-S, which exploits scatter information to improve conditioning. Overall, MLAA-EB-S with synergistic prior achieved the lowest bias and variance in both the activity and the attenuation images amongst the compared methods. Therefore, the proposed synergistic prior could be a useful way for improving the performance of MLAA-EB-S without the need of relying on anatomical images.

Registration of medical images corresponding to different respiratory states is complicated, as respiration not only moves the lungs and nearby organs, but it also causes localised density and radiotracer activity concentration changes of up to 20% in the lung. Nevertheless, few registration methods incorporate mass-preserving constraints, which could lead to suboptimal estimation of the deformation. This is especially important in the case of diffuse lung diseases for which the deformation field could provide biomarkers for pulmonary mechanical properties. This preliminary work is aimed at evaluating the impact of lung expansion in CT and PET image registration. We use patient data for which gated CT and PET acquisition data are available. We compare results of mass-preserving registration with two novel priors on the Jacobian determinant of the deformation field. Best results were observed for edge-preserving regularisation, where the mean errors in the lungs are decreased (−17.0% for CT and −4.8% for regularised PET using average regularisation), compared to warped images with no mass preservation.

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single-valued population-based lung LAC, and better estimation is needed to improve quantification. Given the under-appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single-valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission-based schemes. Potential strategies for future developments are also presented.

This study explores the feasibility of incorporating energy information into a maximum-likelihood reconstruction of activity and attenuation (MLAA) framework. The attenuation and activity distributions were reconstructed from multiple energy window data, and a scatter function was added to the system model of the algorithm. The proposed energy-based method (MLAA-EB) was evaluated with simulated 3D phantom data, using the geometry and characteristics of a Siemens mMR PET-MR scanner. Results showed that the proposed algorithm is able to compensate for errors in the activity image caused by the incorrect assignment of attenuation values to the segmented MR. This is effective for small objects only, for large objects further solutions need to be found.

This study explores the advantages of using a different energy window for each individual annihilation photon when estimating attenuation from scattered PET data. Several studies have shown that scattered data can be used to reconstruct the density distribution of the object, although they are limited to simple phantoms and ideal energy measurements. We have incorporated a realistic photon detection probability model into our algorithm, as well as the dependence of attenuation on photon energy. In the proposed method each detector can assign a photon to either energy window. Preliminary results on point source simulations have shown that using a different window for each annihilation photon allows to derive spatial information on both attenuation and activity distribution, and to isolate subsets of scatter angles depending on the energy window pair used. XCAT simulations demonstrated that it is possible to yield an attenuation map from scattered PET data only and that MR information can increase reconstruction accuracy. This method can also compensate for errors typically introduced from the MRAC, such as truncation artifacts.