Background: Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades. Challenges: The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results. Conclusions: In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.

This study sought to identify a reference tissue‐based quantification approach for improving the statistical power in detecting changes in brain glucose metabolism, amyloid, and tau deposition in Alzheimer's disease studies. A total of 794, 906, and 903 scans were included for ¹⁸F‐FDG, ¹⁸F‐florbetapir, and ¹⁸F‐flortaucipir, respectively. Positron emission tomography (PET) and T1‐weighted images of participants were collected from the Alzheimer's disease Neuroimaging Initiative database, followed by partial volume correction. The standardized uptake value ratios (SUVRs) calculated from the cerebellum gray matter, centrum semiovale, and pons were evaluated at both region of interest (ROI) and voxelwise levels. The statistical power of reference tissues in detecting longitudinal SUVR changes was assessed via paired t‐test. In cross‐sectional analysis, the impact of reference tissue‐based SUVR differences between cognitively normal and cognitively impaired groups was evaluated by effect sizes Cohen's d and two sample t‐test adjusted by age, sex, and education levels. The average ROI t values of pons were 86.62 and 38.40% higher than that of centrum semiovale and cerebellum gray matter in detecting glucose metabolism decreases, while the centrum semiovale reference tissue‐based SUVR provided higher t values for the detection of amyloid and tau deposition increases. The three reference tissues generated comparable d images for ¹⁸F‐FDG, ¹⁸F‐florbetapir, and ¹⁸F‐flortaucipir and comparable t maps for ¹⁸F‐florbetapir and ¹⁸F‐flortaucipir, but pons‐based t map showed superior performance in ¹⁸F‐FDG. In conclusion, the tracer‐specific reference tissue improved the detection of ¹⁸F‐FDG, ¹⁸F‐florbetapir, and ¹⁸F‐flortaucipir PET SUVR changes, which helps the early diagnosis, monitoring of disease progression, and therapeutic response in Alzheimer's disease.

Dynamic long-axial-field-of-view (LAFOV) PET imaging offers unprecedented opportunities for quantitative assessment of tracer kinetics across the entire body. This review discusses the core technical challenges posed by LAFOV datasets in parametric image generation and introduces methodological developments from a statistical perspective, including arterial input function strategies, classical and flexible kinetic models (compartment models, spectral analysis, adiabatic approximation to the tissue homogeneity, and non-parametric models), graphical techniques (Patlak, Logan, and their variants), and emerging directions such as direct parametric reconstruction, dimension reduction, and deep learning. These methodologies are further linked to dynamic clinical protocols designed to shorten scanning times, enable multi-tracer injections, support low-dose imaging, and open avenues for novel tracer and drug development. Finally, we summarize the most recent software packages, particularly those tailored for LAFOV PET parametric imaging. These advances indicate that reliable parametric imaging holds promise for broader clinical adoption, grounded in robust modeling, multi-center validation, and ongoing software advancements.

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is critical for the reliability and safety of energy storage systems in applications such as electric vehicles and aerospace. However, this task is hindered by noisy degradation measurements, non-stationary capacity regeneration, and complex nonlinear aging dynamics. To address these challenges, we propose an IDP–BO–ITE framework for RUL prediction that integrates intelligent data preprocessing , degradation-aware sequence modeling, and reliability-driven adaptive fusion. In the intelligent data preprocessing stage, variational mode decomposition, principal component analysis, and random forest are jointly employed to suppress noise and construct degradation-relevant features from raw capacity sequences. An improved transformer encoder is then developed to model temporal dependencies and nonlinear degradation dynamics, where a global average pooling mechanism is introduced to enhance cross-cycle feature aggregation and improve robustness under limited lifecycle data. A Bayesian optimization scheme is adopted to learn adaptive fusion weights and quantify the prediction uncertainty, leading to a more reliable multi-view integration with a better balance between noise suppression and information preservation. Experiments conducted on the widely used NASA dataset and the more recent XJTU dataset demonstrate that our method consistently outperforms representative approaches in terms of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). In addition, the proposed framework enables the construction of confidence intervals for long-term prognostics, supporting reliable RUL prediction with high accuracy, robustness and strong cross-dataset generalization, and thereby offering an effective and scalable solution for battery health management and predictive maintenance.