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Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune systems capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. 18F-arabinosyl guanine ([18F]F-AraG) is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by noninvasive quantification of immune cell activity within the tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of [18F]F-AraG as a potential quantitative biomarker for immune response evaluation. Methods: The study consisted of 90-min total-body dynamic scans of 4 healthy subjects and 1 non-small cell lung cancer patient who was scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection was used to analyze tracer kinetics in various organs. Additionally, 7 subregions of the primary lung tumor and 4 mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess the reliability of kinetic parameter estimation. Correlations of the SUVmean, the tissue-to-blood SUV ratio (SUVR), and the Logan plot slope (K Logan) with the total volume of distribution (V T) were calculated to identify potential surrogates for kinetic modeling. Results: Strong correlations were observed between K Logan and SUVR with V T, suggesting that they can be used as promising surrogates for V T, especially in organs with a low blood-volume fraction. Moreover, practical identifiability analysis suggested that dynamic [18F]F-AraG PET scans could potentially be shortened to 60 min, while maintaining quantification accuracy for all organs of interest. The study suggests that although [18F]F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response after therapy. Although SUVmean showed variable changes in different subregions of the tumor after therapy, the SUVR, K Logan, and V T showed consistent increasing trends in all analyzed subregions of the tumor with high practical identifiability. Conclusion: Our findings highlight the promise of [18F]F-AraG dynamic imaging as a noninvasive biomarker for quantifying the immune response to immunotherapy in cancer patients. Promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.

Brown adipose tissue (BAT) in rodents appears to be an important tissue for the clearance of plasma branched-chain amino acids (BCAAs) contributing to improved metabolic health. However, the role of human BAT in plasma BCAA clearance is poorly understood. Here, we evaluate patients with prostate cancer who underwent positron emission tomography-computed tomography imaging after an injection of 18F-fluciclovine (L-leucine analog). Supraclavicular adipose tissue (AT; primary location of human BAT) has a higher net uptake rate for 18F-fluciclovine compared to subcutaneous abdominal and upper chest AT. Supraclavicular AT 18F-fluciclovine net uptake rate is lower in patients with obesity and type 2 diabetes. Finally, the expression of genes involved in BCAA catabolism is higher in the supraclavicular AT of healthy people with high BAT volume compared to those with low BAT volume. These findings support the notion that BAT can potentially function as a metabolic sink for plasma BCAA clearance in people.

Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation.MethodsThe conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored.ResultsThe estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method.ConclusionThe proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.

Dual-energy computed tomography (DECT) enables material decomposition for tissues and produces additional information for PET/CT imaging to potentially improve the characterization of diseases. PET-enabled DECT (PDECT) allows the generation of PET and DECT images simultaneously with a conventional PET/CT scanner without the need for a second x-ray CT scan. In PDECT, high-energy γ-ray CT (GCT) images at 511 keV are obtained from time-of-flight (TOF) PET data and are combined with the existing x-ray CT images to form DECT imaging. We have developed a kernel-based maximum-likelihood attenuation and activity (MLAA) method that uses x-ray CT images as a priori information for noise suppression. However, our previous studies focused on GCT image reconstruction at the PET image resolution which is coarser than the image resolution of the x-ray CT. In this work, we explored the feasibility of generating super-resolution GCT images at the corresponding CT resolution. The study was conducted using both phantom and patient scans acquired with the uEXPLORER total-body PET/CT system. GCT images at the PET resolution with a pixel size of 4.0 mm × 4.0 mm and at the CT resolution with a pixel size of 1.2 mm × 1.2 mm were reconstructed using both the standard MLAA and kernel MLAA methods. The results indicated that the GCT images at the CT resolution had sharper edges and revealed more structural details compared to the images reconstructed at the PET resolution. Furthermore, images from the kernel MLAA method showed substantially improved image quality compared to those obtained with the standard MLAA method.

BACKGROUND: The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome. MAIN BODY: This paper summarizes the limitations of IDIF methods for quantitative PET imaging and discusses some of the advances that may make IDIF extraction more reliable. The introduction of automated pipelines (both commercial and open-source) for clinical PET scanners is discussed as a way to improve the reliability of IDIF approaches and their utility for quantitative purposes. Survey data gathered from the PET community are then presented to understand whether the fields opinion of the usefulness and validity of IDIF is improving. Finally, as the introduction of next-generation PET scanners with long axial fields of view, ultra-high sensitivity, and improved spatial and temporal resolution, has also brought IDIF methods back into the spotlight, a discussion of the possibilities offered by these state-of-the-art scanners-inclusion of large vessels, less partial volume in small vessels, better description of the full IDIF kinetics, whole-body modeling of radiometabolite production-is included, providing a pathway for future use of IDIF. CONCLUSION: Improvements in PET scanner technology and software for automated IDIF extraction may allow to solve some of the major limitations associated with IDIF, such as partial volume effects and poor temporal sampling, with the exciting potential for accurate estimation of single kinetic rates. Nevertheless, until individualized radiometabolite correction can be performed effectively, IDIF approaches remain confined at best to a few tracers.

Tissue perfusion can be affected by physiology or disease. With the advent of total-body PET, quantitative measurement of perfusion across the entire body is possible. [11C]-butanol is a perfusion tracer with a superior extraction fraction compared with [15O]-water and [13N]-ammonia. To develop the methodology for total-body perfusion imaging, a pilot study using [11C]-butanol on the uEXPLORER total-body PET/CT scanner was conducted. Methods: Eight participants (6 healthy volunteers and 2 patients with peripheral vascular disease [PVD]) were injected with a bolus of [11C]-butanol and underwent 30-min dynamic acquisitions. Three healthy volunteers underwent repeat studies at rest (baseline) to assess test-retest reproducibility; 1 volunteer underwent paired rest and cold pressor test (CPT) studies. Changes in perfusion were measured in the paired rest-CPT study. For PVD patients, local changes in perfusion were investigated and correlated with patient medical history. Regional and parametric kinetic analysis methods were developed using a 1-tissue compartment model and leading-edge delay correction. Results: Estimated baseline perfusion values ranged from 0.02 to 1.95 mL·min-1·cm-3 across organs. Test-retest analysis showed that repeat baseline perfusion measurements were highly correlated (slope, 0.99; Pearson r = 0.96, P < 0.001). For the CPT subject, the largest regional increases were in skeletal muscle (psoas, 142%) and the myocardium (64%). One of the PVD patients showed increased collateral vessel growth in the calf because of a peripheral stenosis. Comorbidities including myocardial infarction, hypothyroidism, and renal failure were correlated with variations in organ-specific perfusion. Conclusion: This pilot study demonstrates the ability to obtain reproducible measurements of total-body perfusion using [11C]-butanol. The methods are sensitive to local perturbations in flow because of physiologic stressors and disease.

Conventional whole-body static 18F-FDG PET imaging provides a semiquantitative evaluation of overall glucose metabolism without insight into the specific transport and metabolic steps. Here we demonstrate the ability of total-body multiparametric 18F-FDG PET to quantitatively evaluate glucose metabolism using macroparametric quantification and assess specific glucose delivery and phosphorylation processes using microparametric quantification for studying recovery from coronavirus disease 2019 (COVID-19). Methods: The study included 13 healthy subjects and 12 recovering COVID-19 subjects within 8 wk of confirmed diagnosis. Each subject had a 1-h dynamic 18F-FDG scan on the uEXPLORER total-body PET/CT system. Semiquantitative SUV and the SUV ratio relative to blood (SUVR) were calculated for different organs to measure glucose utilization. Tracer kinetic modeling was performed to quantify the microparametric blood-to-tissue 18F-FDG delivery rate [Formula: see text] and the phosphorylation rate k 3, as well as the macroparametric 18F-FDG net influx rate ([Formula: see text]). Statistical tests were performed to examine differences between healthy subjects and recovering COVID-19 subjects. The effect of COVID-19 vaccination was also investigated. Results: We detected no significant difference in lung SUV but significantly higher lung SUVR and [Formula: see text] in COVID-19 recovery, indicating improved sensitivity of kinetic quantification for detecting the difference in glucose metabolism. A significant difference was also observed in the lungs with the phosphorylation rate k 3 but not with [Formula: see text], which suggests that glucose phosphorylation, rather than glucose delivery, drives the observed difference of glucose metabolism. Meanwhile, there was no or little difference in bone marrow 18F-FDG metabolism measured with SUV, SUVR, and [Formula: see text] but a significantly higher bone marrow [Formula: see text] in the COVID-19 group, suggesting a difference in glucose delivery. Vaccinated COVID-19 subjects had a lower lung [Formula: see text] and a higher spleen [Formula: see text] than unvaccinated COVID-19 subjects. Conclusion: Higher lung glucose metabolism and bone marrow glucose delivery were observed with total-body multiparametric 18F-FDG PET in recovering COVID-19 subjects than in healthy subjects, implying continued inflammation during recovery. Vaccination demonstrated potential protection effects. Total-body multiparametric PET of 18F-FDG can provide a more sensitive tool and more insights than conventional whole-body static 18F-FDG imaging to evaluate metabolic changes in systemic diseases such as COVID-19.

In order to meet the power source requirements of C-band linear accelerators for industrial applications, a C-band high power klystron operating at a frequency of 5 712 MHz was developed. For this klystron, the development objective of the output power is no less than 3.5 MW and the efficiency is no less than 35%. During the entire design process, the output power was optimized to over 4 MW and the power efficiency over 40% to compensate for the disadvantage effects caused by manufacturing and testing errors in the development processes. Firstly, the electron optics system with a Pierce gun and a combination of five forward and one reverse focusing coils was designed. The Pierce gun with a perveance of 2 μP was adopted and the beam voltage and current were chosen as 120 kV and 83.3 A. By iteratively calculating the structure of the electron gun with EGUN code and optimizing the magnetic field between the electron gun and the beam-wave interaction section, the final electron beam pass rate reaches 100% and the fluctuation of the beam envelop is only 4.8%. Secondly, the efficiency optimization and PIC simulation of the beam-wave interaction section were carried out using the one dimensional AJDISK code, 1.5 dimensional KlyC code and three dimensional CST code. The scheme of six resonant cavities was ultimately determined and the basic cavity parameters were optimized based on the principles of stagger tuning. The simulation results of different codes all indicate that the efficiency can reach above 46.5%, and the output power can all reach above 4.650 MW. And the influence of frequency errors and output external quality factor on efficiency was also analyzed to guide the manufacturing process. After that, the mechanical structure of cavities was designed and all cavities were manufactured with oxygen free copper. The cold test and tuning of all cavities were carried out and the frequency error is mostly within the range of ±5 MHz, which basically does not affect the output power. Finally, the electron gun, the beam-wave interaction section and the collector were welded together. And the high power test was conducted immediately after the first high power C-band klystron protype was completed. When the electron gun operates at the voltage of 115 kV and current of 79.6 A, the klystron peak output power reaches 3.52 MW, the gain reaches 45.5 dB and the efficiency reaches about 38.5%. The high power test results show that this C-band high power klystron achieves the expected development goals.

Tracer kinetic modeling in dynamic PET has the potential to improve the diagnosis, prognosis, and research of lung diseases. The advent of total-body PET systems with much greater detection sensitivity enables high-temporal-resolution (HTR) dynamic PET imaging of the lungs. However, existing models may become insufficient for modeling the HTR data. In this paper, we investigate the necessity of additional corrections to the input function for HTR lung kinetic modeling. Methods: Dynamic scans with HTR frames of as short as 1 s were performed on 13 healthy subjects with a bolus injection of about [Formula: see text] of 18F-FDG using the uEXPLORER total-body PET/CT system. Three kinetic models with and without time-delay and dispersion corrections were compared for the quality of lung time-activity curve fitting using the Akaike information criterion. The impact on quantification of 18F-FDG delivery rate [Formula: see text], net influx rate [Formula: see text] and fractional blood volume [Formula: see text] was assessed. Parameter identifiability analysis was also performed to evaluate the reliability of kinetic quantification with respect to noise. Correlation of kinetic parameters with age was investigated. Results: HTR dynamic imaging clearly revealed the rapid change in tracer concentration in the lungs and blood supply (i.e., the right ventricle). The uncorrected input function led to poor time-activity curve fitting and biased quantification in HTR kinetic modeling. The fitting was improved by time-delay and dispersion corrections. The proposed model resulted in an approximately 85% decrease in [Formula: see text], an approximately 75% increase in [Formula: see text], and a more reasonable [Formula: see text] (∼0.14) than the uncorrected model (∼0.04). The identifiability analysis showed that the proposed models had good quantification stability for [Formula: see text], [Formula: see text], and [Formula: see text] The [Formula: see text] estimated by the proposed model with simultaneous time-delay and dispersion corrections correlated inversely with age, as would be expected. Conclusion: Corrections to the input function are important for accurate lung kinetic analysis of HTR dynamic PET data. The modeling of both delay and dispersion can improve model fitting and significantly impact quantification of [Formula: see text], [Formula: see text], and [Formula: see text].

BackgroundEpidemiological studies suggest that maternal immune activation (MIA) is a significant risk factor for future neurodevelopmental disorders, including schizophrenia (SZ), in offspring. Consistent with findings in SZ research and work in rodent systems, preliminary cross-sectional findings in nonhuman primates suggest that MIA is associated with dopaminergic hyperfunction in young adult offspring.MethodsIn this unique prospective longitudinal study, we used [18F]fluoro-l-m-tyrosine positron emission tomography to examine the developmental time course of striatal presynaptic dopamine synthesis in male rhesus monkeys born to dams (n = 13) injected with a modified form of the inflammatory viral mimic, polyinosinic:polycytidylic acid [poly(I:C)], in the late first trimester. Striatal (caudate, putamen, and nucleus accumbens) dopamine from these animals was compared with that of control offspring born to dams that received saline (n = 10) or no injection (n = 4). Dopamine was measured at 15, 26, 38, and 48 months of age. Prior work with this cohort found decreased prefrontal gray matter volume in MIA offspring versus controls between 6 and 45 months of age. Based on theories of the etiology and development of SZ-related pathology, we hypothesized that there would be a delayed (relative to the gray matter decrease) increase in striatal fluoro-l-m-tyrosine signal in the MIA group versus controls.Results[18F]fluoro-l-m-tyrosine signal showed developmental increases in both groups in the caudate and putamen. Group comparisons revealed significantly greater caudate dopaminergic signal in the MIA group at 26 months.ConclusionsThese findings are highly relevant to the known pathophysiology of SZ and highlight the translational relevance of the MIA model in understanding mechanisms by which MIA during pregnancy increases risk for later illness in offspring.

Image reconstruction of low-count positron emission tomography (PET) data is challenging. Kernel methods address the challenge by incorporating image prior information in the forward model of iterative PET image reconstruction. The kernelized expectation-maximization (KEM) algorithm has been developed and demonstrated to be effective and easy to implement. A common approach for a further improvement of the kernel method would be adding an explicit regularization, which however leads to a complex optimization problem. In this paper, we propose an implicit regularization for the kernel method by using a deep coefficient prior, which represents the kernel coefficient image in the PET forward model using a convolutional neural-network. To solve the maximum-likelihood neural network-based reconstruction problem, we apply the principle of optimization transfer to derive a neural KEM algorithm. Each iteration of the algorithm consists of two separate steps: a KEM step for image update from the projection data and a deep-learning step in the image domain for updating the kernel coefficient image using the neural network. This optimization algorithm is guaranteed to monotonically increase the data likelihood. The results from computer simulations and real patient data have demonstrated that the neural KEM can outperform existing KEM and deep image prior methods.

Image reconstruction for positron emission tomography (PET) is challenging because of the ill-conditioned tomographic problem and low counting statistics. Kernel methods address this challenge by using kernel representation to incorporate image prior information in the forward model of iterative PET image reconstruction. Existing kernel methods construct the kernels commonly using an empirical process, which may lead to unsatisfactory performance. In this paper, we describe the equivalence between the kernel representation and a trainable neural network model. A deep kernel method is then proposed by exploiting a deep neural network to enable automated learning of an improved kernel model and is directly applicable to single subjects in dynamic PET. The training process utilizes available image prior data to form a set of robust kernels in an optimized way rather than empirically. The results from computer simulations and a real patient dataset demonstrate that the proposed deep kernel method can outperform the existing kernel method and neural network method for dynamic PET image reconstruction.

Quantitative dynamic PET with compartmental modeling has the potential to enable multiparametric imaging and more accurate quantification than static PET imaging. Conventional methods for parametric imaging commonly use a single kinetic model for all image voxels and neglect the heterogeneity of physiologic models, which can work well for single-organ parametric imaging but may significantly compromise total-body parametric imaging on a scanner with a long axial field of view. In this paper, we evaluate the necessity of voxelwise compartmental modeling strategies, including time delay correction (TDC) and model selection, for total-body multiparametric imaging. Methods: Ten subjects (5 patients with metastatic cancer and 5 healthy volunteers) were scanned on a total-body PET/CT system after injection of 370 MBq of 18F-FDG. Dynamic data were acquired for 60 min. Total-body parametric imaging was performed using 2 approaches. One was the conventional method that uses a single irreversible 2-tissue-compartment model with and without TDC. The second approach selects the best kinetic model from 3 candidate models for individual voxels. The differences between the 2 approaches were evaluated for parametric imaging of microkinetic parameters and the 18F-FDG net influx rate, KiResults: TDC had a nonnegligible effect on kinetic quantification of various organs and lesions. The effect was larger in lesions with a higher blood volume. Parametric imaging of Ki with the standard 2-tissue-compartment model introduced vascular-region artifacts, which were overcome by the voxelwise model selection strategy. Conclusion: The time delay and appropriate kinetic model vary in different organs and lesions. Modeling of the time delay of the blood input function and model selection improved total-body multiparametric imaging.

Quantitative kinetic modeling requires an input function. A noninvasive image-derived input function (IDIF) can be obtained from dynamic PET images. However, a robust IDIF location (e.g., aorta) may be far from a tissue of interest, particularly in total-body PET, introducing a time delay between the IDIF and the tissue. The standard practice of joint estimation (JE) of delay, along with model fitting, is computationally expensive. To improve the efficiency of delay correction for total-body PET parametric imaging, this study investigated the use of pulse timing methods to estimate and correct for delay. Methods: Simulation studies were performed with a range of delay values, frame lengths, and noise levels to test the tolerance of 2 pulse timing methods-leading edge (LE) and constant fraction discrimination and their thresholds. The methods were then applied to data from 21 subjects (14 healthy volunteers, 7 cancer patients) who underwent a 60-min dynamic total-body 18F-FDG PET acquisition. Region-of-interest kinetic analysis was performed and parametric images were generated to compare LE and JE methods of delay correction, as well as no delay correction. Results: Simulations demonstrated that a 10% LE threshold resulted in biases and SDs at tolerable levels for all noise levels tested, with 2-s frames. Pooled region-of-interest-based results (n = 154) showed strong agreement between LE (10% threshold) and JE methods in estimating delay (Pearson r = 0.96, P < 0.001) and the kinetic parameters vb (r = 0.96, P < 0.001), Ki (r = 1.00, P < 0.001), and K1 (r = 0.97, P < 0.001). When tissues with minimal delay were excluded from pooled analyses, there were reductions in vb (69.4%) and K1 (4.8%) when delay correction was not performed. Similar results were obtained for parametric images; additionally, lesion Ki contrast was improved overall with LE and JE delay correction compared with no delay correction and Patlak analysis. Conclusion: This study demonstrated the importance of delay correction in total-body PET. LE delay correction can be an efficient surrogate for JE, requiring a fraction of the computational time and allowing for rapid delay correction across more than 106 voxels in total-body PET datasets.

Background: Nonalcoholic steatohepatitis (NASH) is a severe form of fatty liver disease that has been shown to be associated with chronic kidney disease (CKD). Mechanism for the association of NASH with CKD remains unclear. In this study, we examined the association between NASH severity and kidney glucose uptake and the liver-secreted signaling molecule fibroblast growth factor 21 (FGF21). Methods: Kinetic parameters for kidney glucose transport rate (K1) and standardized uptake value (SUV) were determined using dynamic positron emission tomography after injection of 18F-fluorodeoxyglucose. Liver biopsies were scored for NASH activity (inflammation and ballooning), fibrosis, and steatosis FGF21 was measured from fasting serum samples. Patients were categorized by liver biopsy and multivariate analyses were performed to evaluate the associations. Results: Of 41 NASH patients 73% were females, 71% white, 51% with steatosis ≥2, 39% with NASH activity ≥4 and fibrosis ≥3. With severe NASH activity, kidney SUV significantly increased even when adjusted for underlying insulin-resistant (IR) state. Kidney K1 decreased significantly in higher liver activity in unadjusted models but not when adjusted for IR. FGF21 decreased with severe liver activity in adjusted models (P

Nonalcoholic steatohepatitis (NASH) is a severe form of nonalcoholic fatty liver disease characterized by lobular inflammation and hepatocyte injury and is a key determinant of clinical outcome.1 Liver biopsy remains the gold standard for diagnosis but is limited by risks of the procedure and interobserver variability. Although magnetic resonance imaging (MRI)-based technology may provide novel means to identify NASH,2 there remains a significant need for other modalities to diagnose NASH noninvasively. Glucose transport, an integral tissue process altered in NASH,3 is measurable with 18F-fluorodeoxyglucose positron emission tomography (FDG PET). Because unenhanced computed tomography (CT) scan can detect hepatic steatosis quite reliably,4 and PET combines unenhanced CT for attenuation correction, we hypothesized that measurement of the combination of glucose transport by PET and steatosis by CT could yield a reliable radiologic correlate of NASH.

The uEXPLORER total-body PET/CT system provides a very high level of detection sensitivity and simultaneous coverage of the entire body for dynamic imaging for quantification of tracer kinetics. This article describes the fundamentals and potential benefits of total-body kinetic modeling and parametric imaging focusing on the noninvasive derivation of blood input function, multiparametric imaging, and high-temporal resolution kinetic modeling. Along with its attractive properties, total-body kinetic modeling also brings significant challenges, such as the large scale of total-body dynamic PET data, the need for organ and tissue appropriate input functions and kinetic models, and total-body motion correction. These challenges, and the opportunities using deep learning, are discussed.

Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PET-enabled dual-energy CT combines a low-energy X-ray CT image with a high-energy γ-ray CT (GCT) image reconstructed from time-of-flight PET emission data to enable dual-energy CT material decomposition on a PET/CT scanner. The maximum-likelihood attenuation and activity (MLAA) algorithm has been used for GCT reconstruction but suffers from noise. Kernel MLAA exploits an X-ray CT image prior through the kernel framework to guide GCT reconstruction and has demonstrated substantial improvements in noise suppression. However, similar to other kernel methods for image reconstruction, the existing kernel MLAA uses image intensity-based features to construct the kernel representation, which is not always robust and may lead to suboptimal reconstruction with artefacts. In this paper, we propose a modified kernel method by using an autoencoder convolutional neural network (CNN) to extract an intrinsic feature set from the X-ray CT image prior. A computer simulation study was conducted to compare the autoencoder CNN-derived feature representation with raw image patches for evaluation of kernel MLAA for GCT image reconstruction and dual-energy multi-material decomposition. The results show that the autoencoder kernel MLAA method can achieve a significant image quality improvement for GCT and material decomposition as compared to the existing kernel MLAA algorithm. A weakness of the proposed method is its potential over-smoothness in a bone region, indicating the importance of further optimization in future work. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

With the widespread use of radioactive sources in various fields, incidents of loss of radioactive sources occur from time to time aiming at the problem of searching for a single radioactive sources, a particle filtering method based on the angle of arrival was proposed to improve the computational efficiency. The particle set was established to describe the position and intensity of the radioactive source, the angle of arrival was introduced into the particle update process, and the updated particles were selected according to the relative angle of the particles. Next, the following steps include: complete normalization, resampling and adaptive update. Establishing an observation vector set, whenever a new environmental observation data is measured, the optimal estimated position of the radioactive source reflected by all the current observation vectors can be obtained, and the search path can be guided closer to the position of the radioactive source, and when the particle filter meets the convergence conditions, the particles are weighted to obtain the final estimated position of the radioactive source. Simulation experiments show that this method can be applied to source seeking on mobile platform, its efficiency is higher than the existing particle filter method, and its detection area also significantly increases.

Parametric imaging has been shown to provide better quantitation physiologically than SUV imaging in PET. With the increased sensitivity from a recently developed total-body PET scanner, whole-body scans with higher temporal resolution become possible for dynamic analysis and parametric imaging. In this paper, we focus on deriving the parameter k1 using compartmental modeling and on developing a method to acquire whole-body 18F-FDG PET parametric images using only the first 90 s of the postinjection scan data with the total-body PET system. Methods: Dynamic projections were acquired with a time interval of 1 s for the first 30 s and a time interval of 2 s for the following minute. Image-derived input functions were acquired from the reconstructed dynamic sequences in the ascending aorta. A 1-tissue-compartment model with 4 parameters (k1, k2, blood fraction, and delay time) was used. A maximum-likelihood-based estimation method was developed with the 1-tissue-compartment model solution. The accuracy of the acquired parameters was compared with the ones estimated using a 2-tissue-compartment irreversible model with 1-h-long data. Results: All 4 parametric images were successfully calculated using data from 2 volunteers. By comparing the time-activity curves acquired from the volumes of interest, we showed that the parameters estimated using our method were able to predict the time-activity curves of the early dynamics of 18F-FDG in different organs. The delay-time effects for different organs were also clearly visible in the reconstructed delay-time image with delay variations of as large as 40 s. The estimated parameters using both 90-s data and 1-h data agreed well for k1 and blood fraction, whereas a large difference in k2 was found between the 90-s and 1-h data, suggesting k2 cannot be reliably estimated from the 90-s scan. Conclusion: We have shown that with total-body PET and the increased sensitivity, it is possible to estimate parametric images based on the very early dynamics after 18F-FDG injection. The estimated k1 might potentially be used clinically as an indicator for identifying abnormalities.

Due to the excellent physical properties and advantages in the field of tumor therapy, lutetium 177 has attracted extensive attention in the field of internal radiation therapy in nuclear medicine. The carrier free radionuclide lutetium 177 can be used for targeted treatment of neuroendocrine tumor and prostate tumor. The carrier free radionuclide lutetium 177 can be prepared by irradiating high abundance ytterbium 176 isotope in reactor. The experimental study result found that the abundance of ytterbium 176 isotope directly affects the activity of lutetium 177. In order to meet the requirement of high abundance of ytterbium 176 isotope, in view of the characteristics of high primary separation coefficient and good universality of electromagnetic method, the key process parameters of electromagnetic separation and preparation of high abundance ytterbium 176 isotopes were studied. Ultra dry YbCl3 with purity of 99.99% was selected as the separation raw material according to the requirements of electromagnetic isotope separation on raw materials. The trajectory of ytterbium ion beam in magnetic field was simulated, and the effects of magnetic induction intensity and beam opening angle on the dispersion of ytterbium isotope separation were given. The results are the same as the theoretical analysis. The design of nonuniform field will increase the dispersion between isotopes, reduce the sputtering contamination between isotopes, and then improve the abundance of isotopes. The emission angle of ytterbium ion beam is 11°, the seven isotopes of ytterbium can be separated completely, and the dispersion of ytterbium 176 and ytterbium 174 is large. The effects of ion source parameter on the beam intensity and focusing ability of ytterbium isotopes were obtained by ytterbium isotope electromagnetic separation experiment. The main basis for judging whether the discharge parameters of the ion source are appropriate is the current intensity of the ytterbium 176 ion beam and the focusing ability Q of the ion beam. In this experiment, the ratio of the current intensity of the ytterbium 176 to the panel current intensity was taken as the embodiment of the focusing ability Q. This experiment was carried out by controlling the variable method, and the optimal combination of separation parameters was given. Finally, ytterbium 176 isotope sample with high abundance was prepared by electromagnetic separation. After separation, the ytterbium isotope in the receiver is chemically purified to obtain ytterbium 176 isotope, which is detected by mass spectrometer. The abundance of the sample reaches 9816%. The high abundance ytterbium 176 isotope prepared in this study is chemically treated to form Yb2O3 compound, which is used as the target of nuclear reactor irradiation to supply the demander.

Total-body PET image reconstruction follows a similar procedure to the image reconstruction process for standard whole-body PET scanners. One unique aspect of total-body imaging is simultaneous coverage of the entire human body, which makes it convenient to perform total-body dynamic PET scans. Therefore, four-dimensional dynamic PET reconstruction and parametric imaging are of great interest in total-body imaging. This article covers some basics of PET image reconstruction and then focuses on three- and four-dimensional PET reconstruction for total-body imaging. Methods for image formation from raw measurements in total-body PET are described. Challenges and opportunities in total-body PET image reconstruction are discussed.

Positron emission tomography (PET) is actively used in a diverse range of applications in oncology, cardiology, and neurology. The use of PET in the clinical setting focuses on static (single time frame) imaging at a specific time-point post radiotracer injection and is typically considered as semi-quantitative; e.g. standardized uptake value (SUV) measures. In contrast, dynamic PET imaging requires increased acquisition times but has the advantage that it measures the full spatiotemporal distribution of a radiotracer and, in combination with tracer kinetic modeling, enables the generation of multiparametric images that more directly quantify underlying biological parameters of interest, such as blood flow, glucose metabolism, and receptor binding. Parametric images have the potential for improved detection and for more accurate and earlier therapeutic response assessment. Parametric imaging with dynamic PET has witnessed extensive research in the past four decades. In this paper, we provide an overview of past and present activities and discuss emerging opportunities in the field of parametric imaging for the future.