Purpose

Time of flight (TOF) PET reconstruction is well known to statistically improve the image quality compared to non-TOF PET. Although TOF PET can improve the overall signal to noise ratio (SNR) of the image compared to non-TOF PET, the SNR disparity between separate regions in the reconstructed image using TOF data becomes higher than that using non-TOF data. Using the conventional ordered subset expectation maximization (OS-EM) method, the SNR in the low activity regions becomes significantly lower than in the high activity regions due to the different photon statistics of TOF bins. A uniform recovery across different SNR regions is preferred if it can yield an overall good image quality within small number of iterations in practice. To allow more uniform recovery of regions, a spatially variant update is necessary for different SNR regions.

Methods

This paper focuses on designing a spatially variant step size and proposes a TOF-PET reconstruction method that uses a nonuniform separable quadratic surrogates (NUSQS) algorithm, providing a straightforward control of spatially variant step size. To control the noise, a spatially invariant quadratic regularization is incorporated, which by itself does not theoretically affect the recovery uniformity. The Nesterov's momentum method with ordered subsets (OS) is also used to accelerate the reconstruction speed. To evaluate the proposed method, an XCAT simulation phantom and clinical data from a pancreas cancer patient with full (ground truth) and 6× downsampled counts were used, where a Poisson thinning process was employed for downsampling. We selected tumor and cold regions of interest (ROIs) and compared the proposed method with the TOF-based conventional OS-EM and OS-SQS algorithms with an early stopping criterion.

Results

In computer simulation, without regularization, hot regions of OS-EM and OS-NUSQS converged similarly, but cold region of OS-EM was noisier than OS-NUSQS after 24 iterations. With regularization, although the overall speeds of OS-EM and OS-NUSQS were similar, recovery ratios of hot and cold regions reconstructed by the OS-NUSQS were more uniform compared to those of the conventional OS-SQS and OS-EM. The OS-NUSQS with Nesterov's momentum converged faster than others while preserving the uniform recovery. In the clinical example, we demonstrated that the OS-NUSQS with Nesterov's momentum provides more uniform recovery ratios of hot and cold ROIs compared to the OS-SQS and OS-EM. Although the cost function of all methods is equivalent, the proposed method has higher structural similarity (SSIM) values of hot and cold regions compared to other methods after 24 iterations. Furthermore, our computing time using graphics processing unit was 80× shorter than the time using quad-core CPUs.

Conclusion

This paper proposes a TOF PET reconstruction method using the OS-NUSQS with Nesterov's momentum for uniform recovery of different SNR regions. In particular, the spatially nonuniform step size in the proposed method provides uniform recovery ratios of different SNR regions, and the Nesterov's momentum further accelerates overall convergence while preserving uniform recovery. The computer simulation and clinical example demonstrate that the proposed method converges uniformly across ROIs. In addition, tumor contrast and SSIM of the proposed method were higher than those of the conventional OS-EM and OS-SQS in early iterations.

Positron emission tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as magnetic resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior to other Dixon-based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure.

PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently, the deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this paper, we trained a deep residual convolutional neural network to improve PET image quality by using the existing inter-patient information. An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool. We formulate the objective function as a constrained optimization problem and solve it using the alternating direction method of multipliers algorithm. Both simulation data and hybrid real data are used to evaluate the proposed method. Quantification results show that our proposed iterative neural network method can outperform the neural network denoising and conventional penalized maximum likelihood methods.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Coronary artery disease (CAD) causes mortality and morbidity worldwide. We used near-infrared erythrocyte-derived transducers (NETs), a contrast agent, in combination with a photoacoustic imaging system to identify the locations of atherosclerotic lesions and occlusion due to myocardial-infarction (MI). NETs (≈90 nm diameter) were fabricated from hemoglobin-depleted mice erythrocyte-ghosts and doped with Indocyanine Green (ICG). Ten weeks old male C57BL/6 mice (n = 9) underwent left anterior descending (LAD) coronary artery ligation to mimic vulnerable atherosclerotic plaques and their rupture leading to MI. 150 µL of NETs (20 µM ICG,) was IV injected via tail vein 1-hour prior to photoacoustic (PA) and fluorescence in vivo imaging by exciting NETs at 800 nm and 650 nm, respectively. These results were verified with histochemical analysis. We observed ≈256-fold higher PA signal from the accumulated NETs in the coronary artery above the ligation. Fluorescence signals were detected in LAD coronary, thymus, and liver. Similar signals were observed when the chest was cut open. Atherosclerotic lesions exhibited inflammatory cells. Liver demonstrated normal portal tract, with no parenchymal necrosis, inflammation, fibrosis, or other pathologic changes, suggesting biocompatibility of NETs. Non-invasively detecting atherosclerotic plaques and stenosis using NETs may lay a groundwork for future clinical detection and improving CAD risk assessment.

INTRODUCTION: Understanding early neuropathological changes and their associations with cognition may aid dementia prevention. This study investigated associations of cerebral amyloid and tau positron emission tomography (PET) retention with cognition in a predominately middle-aged community-based cohort and examined factors that may modify these relationships. METHODS: 11C-Pittsburgh compound B amyloid and 18F-flortaucipir tau PET imaging were performed. Associations of amyloid and tau PET with cognition were evaluated using linear regression. Interactions with age, apolipoprotein E (APOE) ε4 status, and education were examined. RESULTS: Amyloid and tau PET were not associated with cognition in the overall sample (N = 423; mean: 57 ± 10 years; 50% female). However, younger age (< 55 years) and APOE ε4 were significant effect modifiers, worsening cognition in the presence of higher amyloid and tau. DISCUSSION: Higher levels of Aβ and tau may have a pernicious effect on cognition among APOE ε4 carriers and younger adults, suggesting a potential role for targeted early interventions. HIGHLIGHTS: Risk and resilience factors influenced cognitive vulnerability due to Aβ and tau. Higher fusiform tau associated with poorer visuospatial skills in younger adults. APOE ε4 interacted with Aβ and tau to worsen cognition across multiple domains.

Over several months, representatives from the U.S. Department of Energy (DOE) Office of Science and National Institutes of Health (NIH) had a number of meetings that lead to the conclusion that innovations in the Nations health care could be realized by more directed interactions between NIH and DOE. It became clear that the expertise amassed and instrumentation advances developed at the DOE physical science laboratories to enable cutting-edge research in particle physics could also feed innovation in medical healthcare. To meet their scientific mission, the DOE laboratories created advances in such technologies as particle beam generation, radioisotope production, high-energy particle detection and imaging, superconducting particle accelerators, superconducting magnets, cryogenics, high-speed electronics, artificial intelligence, and big data. To move forward, NIH and DOE initiated the process of convening a joint workshop which occurred on July 12th and 13th, 2021. This Special Report presents a summary of the findings of the collaborative workshop and introduces the goals of the next one.

Advances in molecular positron emission tomography (PET) have enabled anatomic tracking of brain pathology in longitudinal studies of normal aging and dementia, including assessment of the central model of Alzheimer's disease (AD) pathogenesis, according to which TAU pathology begins focally but expands catastrophically under the influence of amyloid-β (Aβ) pathology to mediate neurodegeneration and cognitive decline. Initial TAU deposition occurs many years before Aβ in a specific area of the medial temporal lobe. Building on recent work that enabled focus of molecular PET measurements on specific TAU-vulnerable convolutional temporal lobe anatomy, we applied an automated anatomic sampling method to quantify TAU PET signal in 443 adult participants from several observational studies of aging and AD, spanning a wide range of ages, Aβ burdens, and degrees of clinical impairment. We detected initial cortical emergence of tauopathy near the rhinal sulcus in clinically normal people and, in a subset with longitudinal 2-year follow-up data (n = 104), tracked Aβ-associated spread of TAU from this site first to nearby neocortex of the temporal lobe and then to extratemporal regions. Greater rate of TAU spread was associated with baseline measures of both global Aβ burden and medial temporal lobe TAU. These findings are consistent with clinicopathological correlation studies of Alzheimer's tauopathy and enable precise tracking of AD-related TAU progression for natural history studies and prevention therapeutic trials.

Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.