Validation and Evaluation of a Vendor-Provided Head Motion Correction Algorithm on the uMI Panorama PET/CT System.

Brain PET imaging often faces challenges from head motion (HM), which can introduce artifacts and reduce image resolution, crucial in clinical settings for accurate treatment planning, diagnosis, and monitoring. United Imaging Healthcare has developed NeuroFocus, an HM correction (HMC) algorithm for the uMI Panorama PET/CT system, using a data-driven, statistics-based approach. The HMC algorithm automatically detects HM using a centroid-of-distribution technique, requiring no parameter adjustments. This study aimed to validate NeuroFocus and assess the prevalence of HM in clinical short-duration 18F-FDG scans. Methods: The study involved 317 patients undergoing brain PET scans, divided into 2 groups: 15 for HMC validation and 302 for evaluation. Validation involved patients undergoing 2 consecutive 3-min single-bed-position brain 18F-FDG scans-one with instructions to remain still and another with instructions to move substantially. The evaluation examined 302 clinical single-bed-position brain scans for patients with various neurologic diagnoses. Motion was categorized as small or large on the basis of a 5% SUV change in the frontal lobe after HMC. Percentage differences in SUVmean were reported across 11 brain regions. Results: The validation group displayed a large negative difference (-10.1%), with variation of 5.2% between no-HM and HM scans. After HMC, this difference decreased dramatically (-0.8%), with less variation (3.2%), indicating effective HMC application. In the evaluation group, 38 of 302 patients experienced large HM, showing a 10.9% ± 8.9% SUV increase after HMC, whereas most exhibited minimal uptake changes (0.1% ± 1.3%). The HMC algorithm not only enhanced the image resolution and contrast but also aided in disease identification and reduced the need for repeat scans, potentially optimizing clinical workflows. Conclusion: The study confirmed the effectiveness of NeuroFocus in managing HM in short clinical 18F-FDG studies on the uMI Panorama PET/CT system. It found that approximately 12% of scans required HMC, establishing HMC as a reliable tool for clinical brain 18F-FDG studies.

Framing protocol optimization in oncological Patlak parametric imaging with uKinetics.

PURPOSE: Total-body PET imaging with ultra-high sensitivity makes high-temporal-resolution framing protocols possible for the first time, which allows to capture rapid tracer dynamic changes. However, whether protocols with higher number of temporal frames can justify the efficacy with substantially added computation burden for clinical application remains unclear. We have developed a kinetic modeling software package (uKinetics) with the advantage of practical, fast, and automatic workflow for dynamic total-body studies. The aim of this work is to verify the uKinetics with PMOD and to perform framing protocol optimization for the oncological Patlak parametric imaging. METHODS: Six different protocols with 100, 61, 48, 29, 19 and 12 temporal frames were applied to analyze 60-min dynamic 18F-FDG PET scans of 10 patients, respectively. Voxel-based Patlak analysis coupled with automatically extracted image-derived input function was applied to generate parametric images. Normal tissues and lesions were segmented manually or automatically to perform correlation analysis and Bland-Altman plots. Different protocols were compared with the protocol of 100 frames as reference. RESULTS: Minor differences were found between uKinetics and PMOD in the Patlak parametric imaging. Compared with the protocol with 100 frames, the relative difference of the input function and quantitative kinetic parameters remained low for protocols with at least 29 frames, but increased for the protocols with 19 and 12 frames. Significant difference of lesion Ki values was found between the protocols with 100 frames and 12 frames. CONCLUSION: uKinetics was proved providing equivalent oncological Patlak parametric imaging comparing to PMOD. Minor differences were found between protocols with 100 and 29 frames, which indicated that 29-frame protocol is sufficient and efficient for the oncological 18F-FDG Patlak applications, and the protocols with more frames are not needed. The protocol with 19 frames yielded acceptable results, while that with 12 frames is not recommended.

Socioeconomic Positions and Midlife Health Trajectories in a Changing Social Context: Evidence from China, 1991-2006.

Cumulative (dis)advantage theory posits that socioeconomic disparities in health may increase with age. This study examines individuals' midlife health trajectories, taking account of how their life courses are embedded within changing social contexts. Using the China Health and Nutrition Survey (1991-2006), it examines the health gap between Chinese rural peasants and urban nonpeasants in three adjacent time periods, during which a rapid process of social change increased the inequalities between rural and urban areas. Findings show that the health gap increases more rapidly in the more recent time periods, with higher levels of inequality, indicating that health inequalities between the two groups are contingent upon the social contexts in which individuals' lives unfold. To better understand the differences observed over these time periods, further analysis will examine the roles of two structural factors: income inequality and differential access to medical care.

Whole-Body Parametric Imaging of 18F-FDG PET Using uEXPLORER with Reduced Scanning Time.

Parametric imaging of the net influx rate (Ki ) in 18F-FDG PET has been shown to provide improved quantification and specificity for cancer detection compared with SUV imaging. Current methods of generating parametric images usually require a long dynamic scanning time. With the recently developed uEXPLORER scanner, a dramatic increase in sensitivity has reduced the noise in dynamic imaging, making it more robust to use a nonlinear estimation method and flexible protocols. In this work, we explored 2 new possible protocols besides the standard 60-min one for the possibility of reducing scanning time for Ki imaging. Methods: The gold standard protocol (protocol 1) was conventional dynamic scanning with a 60-min scanning time. The first proposed protocol (protocol 2) included 2 scanning periods: 0-4 min and 54-60 min after injection. The second proposed protocol (protocol 3) consisted of a single scanning period from 50 to 60 min after injection, with a second injection applied at 56 min. The 2 new protocols were simulated from the 60-min standard scans. A hybrid input function combining the population-based input function and the image-derived input function (IDIF) was used. The results were also compared with the IDIF acquired from protocol 1. A previously developed maximum-likelihood approach was used to estimate the Ki images. In total, 7 cancer patients imaged using the uEXPLORER scanner were enrolled in this study. Lesions were identified from the patient data, and the lesion Ki values were compared among the different protocols. Results: The acquired hybrid input function was comparable in shape to the IDIF for each patient. The average difference in area under the curve was about 3%, suggesting good quantitative accuracy. The visual difference between the Ki images generated using IDIF and those generated using the hybrid input function was also minimal. The acquired Ki images using different protocols were visually comparable. The average Ki difference in the lesions was 2.8% ± 2.1% for protocol 2 and 1% ± 2.2% for protocol 3. Conclusion: The results suggest that it is possible to acquire Ki images using the nonlinear estimation approach with a much-reduced scanning time. Between the 2 new protocols, the protocol with dual injection shows the greatest promise in terms of practicality.

Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format.

Conventional positron emission tomography (PET) image reconstruction is achieved by the statistical iterative method. Deep learning provides another opportunity for speeding up the image reconstruction process. However, conventional deep learning-based image reconstruction requires a fully connected network for learning the Radon transform. The use of fully connected networks greatly complicated the network and increased hardware cost. In this study, we proposed a novel deep learning-based image reconstruction method by utilizing the DIRECT data partitioning method. The U-net structure with only convolutional layers was used in our approach. Patch-based model training and testing were used to achieve 3D reconstructions within current hardware limitations. Time-of-flight (TOF)-histoimages were first generated from the listmode data to replace conventional sinograms. Different projection angles were used as different channels in the input. A total of 15 patient data were used in this study. For each patient, the dynamic whole-body scanning protocol was used to expand the training dataset and a total of 372 separate scans were included. The leave-one-patient-out validation method was used. Two separate studies were carried out. In the first study, the measured TOF-histoimages were directly used for model training and testing, to study the performance of the method in real-world applications. In the second study, TOF-histoimages were simulated from already reconstructed images to exclude the scatters, randoms, attenuation-activity mismatch effects. This study was used to evaluate the optimal performance when all other corrections are ideal. Volumes of interests were placed in the liver and lesion region to study image noise and lesion quantitations. The reconstructed images using the proposed deep learning method showed similar image quality when compared with the conventional expectation-maximization approach. A minimal difference was observed when the simulated TOF-histoimages were used as model input and testing, suggesting the deep learning model can indeed learn the reconstruction process. Some quantitative difference was observed when the measured TOF-histoimages were used. The two studies suggested that the major difference is caused by inaccurate corrections performed by the network itself, which indicated that physics-based corrections are still required for better quantitative performance. In conclusion, we have proposed a novel deep learning-based image reconstruction method for TOF PET. With the help of the DIRECT data partitioning method, no fully connected layers were used and 3D image reconstruction can be directly achieved within the limits of the current hardware.

Primary and secondary effects of social origins on educational attainment: New findings for England.

We aim to bring together two current strands of research into inequalities in individuals' educational attainment that are associated with their social origins: that concerned with the "primary" and "secondary" effects of social origins in creating inequalities, and that concerned with the relation between these inequalities and different components of social origins, taken to represent different forms of parental resources. Our main findings are the following. The secondary effects of social origins-their effects via the educational choices that young people make given their prior academic performance-are clearly operative across five key educational transitions within the English educational system. More specifically, we estimate that 35% of the total effect of social origins is secondary in the earliest transition that we consider, and from 15% to 20% in the subsequent four. Furthermore, mediation analyses reveal that secondary effects are most strongly associated with parental education and then, to a lesser degree with parental status, while little association exists with parental class and none at all with parental income. Primary effects are also at all transitions most strongly associated with parental education and status but in this case both parental class and parental income do retain some importance. We suggest an explanation for our empirical findings as resulting largely from the concern of highly educated, professional parents, and their children to avoid the occurrence of downward intergenerational mobility, especially in terms of education and status.

Total-Body Quantitative Parametric Imaging of Early Kinetics of 18F-FDG.

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.

Simplified protocol for whole-body Patlak parametric imaging with 18 F-FDG PET/CT: Feasibility and error analysis.

PURPOSE: Parametric imaging using the Patlak model has been shown to provide improved lesion detectability and specificity. The Patlak model requires both tissue time-activity curves (TACs) after equilibrium and knowledge of the input function from the start of injection. Therefore, the conventional dynamic scanning protocol typically starts from the radiotracer injection all the way to equilibrium. In this paper, we propose the use of hybrid population-based and model-based input function estimation and evaluate its use for whole-body Patlak analysis, in order to reduce the total scan time and simplify clinical Patlak parametric imaging protocols. Possible quantitative errors caused by the simplified scanning protocol were also analyzed both theoretically and with the use of clinical data. MATERIALS AND METHODS: Clinical data from 24 patients referred for tumor staging were included in this study. The patients underwent a whole-body dynamic PET study, 20 min after FDG injection (0.13 mCi/kg). The proposed whole-body scanning protocol includes 6 passes with 4-5 bed positions, depending on the size of the patient, with 2 min for each bed position. An input function from the literature was selected as the shape of the population-based input function. The descending aorta from the corresponding CT image was segmented and applied on the reconstructed dynamic PET images to acquire an image-based input function, which was later fitted using an exponential model. Due to the late scan time, only the later portion of the input function was available, which was used to scale the population-based input function. The hybrid input function was used to derive the whole-body Patlak images. Assuming a given error in the population-based input function, its influence on the final Patlak images were also derived theoretically and verified using the clinical data sets. Finally, the image quality of the reconstructed Patlak slope image was evaluated by an experienced radiologist in four different aspects: image artifacts, image noise, lesion sharpness, and lesion detectability. RESULTS: It was found that errors in the population-based input function only affect the absolute scale of the Patlak slope image. The induced error is proportional to the percentage area-under-curve (AUC) error in the input function. These findings were also confirmed by numerical analysis. The predicted global scale was in good agreement with results from both image-based Patlak and direct Patlak approach. The fractions of the AUC from the early portion population-based input function were also found to be around 18% of the total AUC of the input function, further limiting the propagation of quantitation error from population-based input function to the final Patlak slope image. The reconstructed Patlak images were also found by the radiologist to provide excellent confidence in lesion detection tasks. CONCLUSIONS: We have proposed a simplified whole-body scanning protocol that utilizes both population-based input function and model-based input function. The error from the population-based function was found to only affect the global scale and the overall quantitative impact can be predicted using our proposed formulas.

Do the most successful migrants emulate natives in well-being? The compound effect of geographical and social mobility.

A growing body of research has been focusing on the well-being consequences of migration, yet most of this has overlooked the fact that many migrants experience intragenerational social mobility alongside geographical mobility. Without accounting for the effect of social mobility in working life, the impact of geographical mobility on well-being cannot be clearly examined. This paper focuses on the most successful migrants, who have started from the bottom and have achieved upward social mobility in the course of their careers, and compares their well-being with that of native non-migrants who have experienced a similar intragenerational social mobility trajectory. The analysis is based on a recent national survey in China, which has a representative sample for both the overall population and migrants. Findings show that migrants, whether from an urban or rural origin, have better incomes but significantly lower levels of well-being than natives, even with a similar career advancement trajectory and the same destination class position. Further exploration shows that the well-being disadvantage of migrants is mainly due to institutional and sociocultural barriers, rather than to reward differentials in the labour market. This may have a wider implication for migrants across national borders.

'Falling from grace' and 'rising from rags': Intergenerational educational mobility and depressive symptoms.

With this study, we make a number of contributions to the ongoing debate on the implications of intergenerational mobility for individuals' health. First, instead of focusing on absolute intergenerational mobility in educational attainment, we analyse varying implications of relative intergenerational mobility for depressive symptoms by considering the distribution of educational credentials separately in the parental and offspring generations. Second, unlike conventional approaches, which predominantly emphasise that upward and downward mobility has a negative effect, we argue that upward mobility might improve individuals' mental well-being and that this effect may vary by gender. Third, we use statistical approach which was designed specifically to study the consequences of intergenerational mobility and does not conflate mobility effects with effects of the positions of origin and destination. Using the 2012-2014 waves of the European Social Survey and data for 52,773 individuals nested in 28 societies, we fit the diagonal reference models with both individuals' short- and long-range experiences of intergenerational educational mobility. The results indicate that upward and downward mobility is associated with, respectively, lower and higher levels of depressive symptoms, as measured with the Center for Epidemiological Studies Depression Scale, and that these effects are only observed among men.

Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy.

BACKGROUND: This study proposed an effective method based on the wavelet multi-scale α-entropy features of heart rate variability (HRV) for the recognition of paroxysmal atrial fibrillation (PAF). This new algorithm combines wavelet decomposition and non-linear analysis methods. The PAF signal, the signal distant from PAF, and the normal sinus signals can be identified and distinguished by extracting the characteristic parameters from HRV signals and analyzing their quantification indexes. The original ECG signals for QRS detection and HRV signal extraction are first processed. The features from the HRV signals are extracted as feature vectors using the wavelet multi-scale entropy. A support vector machine-based classifier is used for PAF prediction. RESULTS: The performance of the proposed method in predicting PAF episodes is evaluated with 100 signals from the MIT-BIT PAF prediction database. With regard to the dynamics and uncertainty of PAF signals, our proposed method obtains the values of 92.18, 94.88, and 89.48% for the evaluation criteria of correct rate, sensitivity, and specificity, respectively. CONCLUSIONS: Our proposed method presents better results than the existing studies based on time domain, frequency domain, and non-linear methods. Thus, our method shows considerable potential for clinical monitoring and treatment.

Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG.

BACKGROUND: Atrial fibrillation (AF) is a common type of arrhythmia disease, which has a high morbidity and can lead to some serious complications. The ability to detect and in turn prevent AF is extremely significant to the patient and clinician. OBJECTIVE: Using ECG to detect AF and develop a robust and effective algorithm is the primary objective of this study. METHODS: Some studies show that after AF occurs, the regulatory mechanism of vagus nerve and sympathetic nerve will change. Each R-R interval will be absolutely unequal. After studying the physiological mechanism of AF, we will calculate the Rényi entropy of the wavelet coefficients of heart rate variability (HRV) in order to measure the complexity of PAF signals, as well as extract the multi-scale features of paroxysmal atrial fibrillation (PAF). RESULTS: The data used in this study is obtained from MIT-BIH PAF Prediction Challenge Database and the correct rate in classifying PAF patients from normal persons is 92.48%. CONCLUSIONS: The results of this experiment proved that AF could be detected by using this method and, in turn, provide opinions for clinical diagnosis.

Green synthesis of silver nanoparticles using 4-acetamido-TEMPO-oxidized curdlan.

A facile, simple, and eco-friendly method using 4-acetamido-2,2,6,6-tetramethypiperidine-1-oxyl radical-oxidized curdlan (Oc) as both reducing and stabilizing agents was developed for the fabrication of silver nanoparticles (AgNPs) from silver nitrate (AgNO3). The structure, morphology, and particle size of the as-prepared AgNPs were investigated by ultraviolet-visible spectroscopy, transmission electron microscopy, energy dispersive X-ray spectrometry, X-ray diffraction, and dynamic laser light scattering. The well-dispersed AgNPs were sphere like with a mean diameter of 15 nm. Their formation was dependent on reaction duration, reaction temperature, Oc concentration, and AgNO3 concentration. Fourier transform-infrared and Raman spectra demonstrated that the as-prepared AgNPs can readily bind covalently with the carboxylate groups of Oc through the strong monodentate interaction in the reaction medium.