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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.

To comprehensively understand temperature and moisture changes in a continuous microwave rice grain drying system, this study proposes a simulation strategy that facilitates the continuous movement of rice grains. The study establishes a three‐dimensional model concerning multiple physical fields, including electromagnetism, heat, and mass transfer. It examines the effects of rice grain layer thickness and microwave intensity on the moisture and temperature of rice grains. The results indicate that the drying rate is negatively correlated with material thickness but positively correlated with temperature. The optimal thickness for the rice grain layer is 8 mm. Furthermore, both the microwave drying rate and temperature increase with microwave intensity. These findings contribute to a deeper understanding of the hydrothermal change mechanisms in rice grains during microwave drying. Practical applications: Microwave drying, as an emergent drying technology, has garnered widespread attention for its selective heating, efficient drying, energy savings, and environmental protection. Nevertheless, enhancing uniformity during the microwave heating process constitutes an important research topic in scientific research and technological applications. Numerical simulation methods can visually analyze electric field distribution, thereby optimizing microwave heating uniformity and improving drying effects. This article aims to accurately predict the continuous microwave drying time of paddy and maximize energy usage by optimizing drying thickness and microwave power parameters establishing a mathematical model for the process. The study not only provides a solid theoretical basis for microwave dryer design but also scientifically guides the continuous microwave drying of rice and other food crops. [ABSTRACT FROM AUTHOR]

Kupffer cells (KCs) are liver‐resident macrophages involved in hepatic inflammatory responses, including nonalcoholic fatty liver disease (NAFLD) development. However, the contribution of KC subsets to liver inflammation remains unclear. Here, using high‐dimensional single‐cell RNA sequencing, we characterized murine embryo‐derived KCs and identified two KC populations with different gene expression profiles: KC‐1 and KC‐2. KC‐1 expressed CD170, exhibiting immunoreactivity and immune‐regulatory abilities, while KC‐2 highly expressed lipid metabolism‐associated genes. In a high‐fat diet–induced NAFLD model, KC‐1 cells differentiated into pro‐inflammatory phenotypes and initiated more frequent communications with invariant natural killer T (iNKT) cells. In KC‐1, interleukin (IL)‐10 expression was unaffected by the high‐fat diet but impaired by iNKT cell ablation and upregulated by iNKT cell adoptive transfer in vivo. Moreover, in a cellular co‐culture system, primary hepatic iNKT cells promoted IL‐10 expression in RAW264.7 and primary KC‐1 cells. CD206 signal blocking in KC‐1 or CD206 knockdown in RAW264.7 cells significantly reduced IL‐10 expression. In conclusion, we identified two embryo‐derived KC subpopulations with distinct transcriptional profiles. The CD206‐mediated crosstalk between iNKT and KC‐1 cells maintains IL‐10 expression in KC‐1 cells, affecting hepatic immune balance. Therefore, KC‐based therapeutic strategies must consider cellular heterogeneity and the local immune microenvironment for enhanced specificity and efficiency. [ABSTRACT FROM AUTHOR]