Region fine-grained attention network for accurate bone age assessment.

Bone age assessment plays a vital role in monitoring the growth and development of adolescents. However, it is still challenging to obtain precise bone age from hand radiography due to these problems: 1) Hand bone varies greatly and is always masked by the background; 2) the hand bone radiographs with successive ages offer high similarity. To solve such issues, a region fine-grained attention network (RFGA-Net) was proposed for bone age assessment, where the region aware attention (RAA) module was developed to distinguish the skeletal regions from the background by modeling global spatial dependency; then the fine-grained feature attention (FFA) module was devised to identify similar bone radiographs by recognizing critical fine-grained feature regions. The experimental results demonstrate that the proposed RFGA-Net shows the best performance on the Radiological Society of North America (RSNA) pediatric bone dataset, achieving the mean absolute error (MAE) of 3.34 and the root mean square error (RMSE) of 4.02, respectively.

Investigation of dissolved organic matter's influence on the toxicity of cadmium to the cyanobacterium Microcystis aeruginosa by biochemical and molecular assays.

Rapid economic development has increased the accumulation of dissolved organic matter (DOM) and heavy metals in aquatic environments. In addition, Microcystis aeruginosa can cause the outbreak of cyanobacteria bloom and can produce microcystin, which poses a threat to human water safety. Therefore, this study analyzed the biochemical and molecular assays of DOM (0, 1, 3, 5, 8, 10 mg C L-1) extracted from four different sources on the toxicity of cadmium (Cd) to M. aeruginosa. The results showed that the addition of different concentrations of DOM from sediment, biochar, and humic acid alleviated the toxicity of Cd to M. aeruginosa. But the addition of rice hulls DOM at high concentrations (8 and 10 mg L-1) significantly reduced the normal growth and metabolic activities of M. aeruginosa. DOM from four different sources promoted the expression level of microcystin-related gene mcyA and the production of microcystin-leucine-arginine (MC-LR), and mcyA was positively correlated with MC-LR. DOM from biochar, sediment, and humic acid were able to bind Cd through complexation. The results will help to understand the toxic effects of heavy metals on toxic-producing cyanobacteria in the presence of DOM, and provide certain reference for the evaluation of water environmental health.

Bone age assessment from articular surface and epiphysis using deep neural networks.

Bone age assessment is of great significance to genetic diagnosis and endocrine diseases. Traditional bone age diagnosis mainly relies on experienced radiologists to examine the regions of interest in hand radiography, but it is time-consuming and may even lead to a vast error between the diagnosis result and the reference. The existing computer-aided methods predict bone age based on general regions of interest but do not explore specific regions of interest in hand radiography. This paper aims to solve such problems by performing bone age prediction on the articular surface and epiphysis from hand radiography using deep convolutional neural networks. The articular surface and epiphysis datasets are established from the Radiological Society of North America (RSNA) pediatric bone age challenge, where the specific feature regions of the articular surface and epiphysis are manually segmented from hand radiography. Five convolutional neural networks, i.e., ResNet50, SENet, DenseNet-121, EfficientNet-b4, and CSPNet, are employed to improve the accuracy and efficiency of bone age diagnosis in clinical applications. Experiments show that the best-performing model can yield a mean absolute error (MAE) of 7.34 months on the proposed articular surface and epiphysis datasets, which is more accurate and fast than the radiologists. The project is available at https://github.com/YameiDeng/BAANet/, and the annotated dataset is also published at https://doi.org/10.5281/zenodo.7947923.

Research on the long tail mechanism of digital finance alleviating the relative poverty of rural households.

Digital finance provides a long-tail mechanism for alleviating relative poverty caused by unequal opportunities and rights. According to the inference of an improved Cobb-Douglas production function and Ramsey-Cass-Koopmans two-stage household consumption model, the long-tail mechanism for digital finance to alleviate the relative poverty of farmers includes productive investment mechanism, credit mechanism, financial asset allocation and entrepreneurial mechanism. An empirical analysis of 11,519 rural households across China based on CHFS2019 data shows that digital finance can significantly and steadily alleviate relative poverty by improving credit availability and promoting household entrepreneurship, while its effect on increasing productive investment opportunities and optimizing financial asset allocation is less certain. Therefore, it is necessary to continue to improve the "blood making" long tail mechanism of digital finance for farmers' credit and innovation and entrepreneurship, and at the same time guide the digital finance to empower the development of rural industries to increase farmers' productive investment opportunities, cultivate endogenous growth momentum, and improve the wealth allocation function of rural digital financial market.

Cross-engine transformation-based fast dose calculation for MRI-Linac online treatment planning.

PURPOSE: To propose a novel magnetic field dose calculation method based on transformation from pencil beam (PB) to Monte Carlo (MC) distribution for MRI-Linac online treatment planning. METHODS: The novel magnetic field dose calculation algorithm was established by a PB dose engine and a magnetic field with tissue inhomogeneity influence correction network. The correction network was constructed with a Res-UNet framework, including residual modules and an encoding-decoding path, by inputting three-dimensional PB dose and patient electron density map, and outputting transformed dose distribution. The influences of magnetic fields and tissue heterogeneity were considered and corrected simultaneously in the correction model. A total of 110 clinically treated static beam IMRT plans were collected, including plans for brain, head-and-neck, lung, and rectum cases. A total of 90 cases were used and enhanced to train and validate the model, and the other 20 cases were for test. By comparing the proposed pipeline-generated dose distribution with original input PB dose and corresponding MC dose, the feasibility and effectiveness of the method was evaluated. RESULTS: Results on both beam dose and plan dose accuracy comparisons on all investigated four tumor sites show great consistency between the cross-dose-engine transformation generations and the MC results, with averaged plan mean absolute error of 0.90% ± 0.13% for the voxel-wise dose difference and 98.33% ± 1.07% gamma passing rate at the 2%/2 mm criteria. The whole PB calculation and transformation process can be completed within second. CONCLUSIONS: We have successfully developed a fast novel magnetic field dose calculation pipeline based on transformation from PB distribution to MC distribution for MRI-Linac online treatment planning.

TransDose: a transformer-based UNet model for fast and accurate dose calculation for MR-LINACs.

Objective. To present a transformer-based UNet model (TransDose) for fast and accurate dose calculation for magnetic resonance-linear accelerators (MR-LINACs).Approach. A 2D fluence map from each beam was first projected into a 3D fluence volume and then fed into the TransDose model together with patient density volume and output predicted beam dose. The proposed TransDose model combined a 3D residual UNet with a transformer encoder, where convolutional layers extracted the volumetric spatial features, and the transformer encoder processed the long-range dependencies in a global space. Ninety-eight cases with four tumor sites (brain, nasopharynx, lung, and rectum) treated with fixed-beam intensity-modulated radiotherapy were included in the dataset; 78 cases were used for model training and validation; and 20 cases were used for testing. The ground-truth beam doses were calculated with Monte Carlo (MC) simulations within 1% statistical uncertainty and magnetic field strengthB = 1.5 T in the superior and inferior direction. Beam angles from the training and validation datasets were rotated 2-5 times, and doses were recalculated to augment the datasets.Results. The dose-volume histograms and indices between the predicted and MC doses showed good consistency. The average 3Dγ-passing rates (3%/2 mm, for dose regions above 10% of maximum dose) were 99.13 ± 0.89% (brain), 98.31 ± 1.92% (nasopharynx), 98.74 ± 0.70% (lung), and 99.28 ± 0.25% (rectum). The average dose calculation time, which included the fluence projection and model prediction, was less than 310 ms for each beam.Significance. We successfully developed a transformer-based UNet dose calculation model-TransDose in magnetic fields. Its accuracy and efficiency indicated its potential for use in online adaptive plan optimization for MR-LINACs.

Development of a GPU-superposition Monte Carlo code for fast dose calculation in magnetic fields.

Objective.To develop and validate a graphics processing unit (GPU) based superposition Monte Carlo (SMC) code for efficient and accurate dose calculation in magnetic fields.Approach.A series of mono-energy photons ranging from 25 keV to 7.7 MeV were simulated with EGSnrc in a water phantom to generate particle tracks database. SMC physics was extended with charged particle transport in magnetic fields and subsequently programmed on GPU as gSMC. Optimized simulation scheme was designed by combining variance reduction techniques to relieve the thread divergence issue in general GPU-MC codes and improve the calculation efficiency. The gSMC code's dose calculation accuracy and efficiency were assessed through both phantoms and patient cases.Main results.gSMC accurately calculated the dose in various phantoms for bothB = 0 T andB = 1.5 T, and it matched EGSnrc well with a root mean square error of less than 1.0% for the entire depth dose region. Patient cases validation also showed a high dose agreement with EGSnrc with 3D gamma passing rate (2%/2 mm) large than 97% for all tested tumor sites. Combined with photon splitting and particle track repeating techniques, gSMC resolved the thread divergence issue and showed an efficiency gain of 186-304 relative to EGSnrc with 10 CPU threads.Significance.A GPU-superposition Monte Carlo code called gSMC was developed and validated for dose calculation in magnetic fields. The developed code's high calculation accuracy and efficiency make it suitable for dose calculation tasks in online adaptive radiotherapy with MR-LINAC.

Extension and validation of a GPU-Monte Carlo dose engine gDPM for 1.5 T MR-LINAC online independent dose verification.

PURPOSE: To extend and validate the accuracy and efficiency of a graphics processing unit (GPU)-Monte Carlo dose engine for Elekta Unity 1.5 T Magnetic Resonance-Linear Accelerator (MR-LINAC) online independent dose verification. METHODS: Electron/positron propagation physics in a uniform magnetic field was implemented in a previously developed GPU-Monte Carlo dose engine-gDPM. The dose calculation accuracy in the magnetic field was first evaluated in heterogeneous phantom with EGSnrc. The dose engine was then commissioned to a Unity machine with a virtual two photon-source model and compared with the Monaco treatment planning system. Fifteen patient plans from five tumor sites were included for the quantification of online dose verification accuracy and efficiency. RESULTS: The extended gDPM accurately calculated the dose in a 1.5 T external magnetic field and was well matched with EGSnrc. The relative dose difference along central beam axis was less than 0.5% for the homogeneous region in water-lung phantom. The maximum difference was found at the build-up regions and heterogeneous interfaces, reaching 1.9% and 2.4% for 2 and 6 MeV mono-energy photon beams, respectively. The root mean square errors for depth-dose fall-off region were less than 0.2% for all field sizes and presented a good match between gDPM and Monaco GPUMCD. For in-field profiles, the dose differences were within 1% for cross-plane and in-plane directions for all calculated depths except dmax. For penumbra regions, the distance-to-agreements between two dose profiles were less than 0.1 cm. For patient plan verification, the maximum relative average dose difference was 1.3%. The gamma passing rates with criteria 3% (2 mm) for dose regions above 20% were between 93% and 98%. gDPM can complete the dose calculation for less than 40 s with 5 × 108 photons on a single NVIDIA GTX-1080Ti GPU and achieve a statistical uncertainty of 0.5%-1.1% for all evaluated cases. CONCLUSIONS: A GPU-Monte Carlo package-gDPM was extended and validated for Elekta Unity online plan verification. Its calculation accuracy and efficiency make it suitable for online independent dose verification for MR-LINAC.

Measuring Success of Patients' Continuous Use of Mobile Health Services for Self-management of Chronic Conditions: Model Development and Validation.

BACKGROUND: Mobile health services are gradually being introduced to support patients' self-management of chronic conditions. The success of these services is contingent upon patients' continuous use of them. OBJECTIVE: This study aims to develop a model to measure the success of patients' continuous use of mobile health services for the self-management of chronic conditions. METHODS: The proposed model was derived from the information systems continuance model and the information systems success model. This model contains 7 theoretical constructs: information quality, system quality, service quality, perceived usefulness, user satisfaction, perceived health status, and continuous use intention. A web-based questionnaire survey instrument was developed to test the model. The survey was conducted to collect data from 129 patients who used a mobile health app for hypertension management from 2017 to 2019. The questionnaire items were derived from validated instruments and were measured using a 5-point Likert scale. The partial least squares modelling method was used to test the theoretical model. RESULTS: The model accounted for 58.5% of the variance in perceived usefulness (R2=0.585), 52.3% of the variance in user satisfaction (R2=0.523), and 41.4% of the variance in patients' intention to make continuous use of mobile health services (R2=0.414). The continuous use intention was significantly influenced by their perceived health status (ß=.195, P=.03), perceived usefulness (ß=.307, P=.004), and user satisfaction (ß=.254, P=.04) with the mobile health service. Information quality (ß=.235, P=.005), system quality (ß=.192, P=.02), and service quality (ß=.494, P<.001) had a significantly positive influence on perceived usefulness but not on user satisfaction. Perceived usefulness had a significantly positive influence on user satisfaction (ß=.664, P<.001). In a result opposite to the original hypothesis, perceived health status did not negatively influence patients' intention to continue using the mobile health service but showed a significantly positive correlation. CONCLUSIONS: This study developed a theoretical model to predict and explain patients' continuous use of mobile health services for self-management of chronic conditions and empirically tested the model. Perceived usefulness, user satisfaction, and health status contributed to patients' intention to make continuous use of mobile health services for self-managing their chronic conditions.

A Comprehensive 6A Framework for Improving Patient Self-Management of Hypertension Using mHealth Services: Qualitative Thematic Analysis.

BACKGROUND: Hypertension affects over 15% of the world's population and is a significant global public health and socioeconomic challenge. Mobile health (mHealth) services have been increasingly introduced to support hypertensive patients to improve their self-management behaviors, such as adherence to pharmacotherapy and lifestyle modifications. OBJECTIVE: This study aims to explore patients' perceptions of mHealth services and the mechanisms by which the services support them to self-manage their hypertension. METHODS: A semistructured, in-depth interview study was conducted with 22 outpatients of the General Hospital of Ningxia Medical University from March to May 2019. In 2015, the hospital introduced an mHealth service to support community-dwelling outpatients with self-management of hypertension. Content analysis was conducted by following a grounded theory approach for inductive thematic extraction. Constant comparison and categorization classified the first-level codes with similar meanings into higher-level themes. RESULTS: The patient-perceived mechanisms by which the mHealth service supported their self-management of hypertension were summarized as 6A: access, assessment, assistance, awareness, ability, and activation. With the portability of mobile phones and digitization of information, the mHealth service provided outpatients with easy access to assess their vital signs and self-management behaviors. The assessment results gave the patients real-time awareness of their health conditions and self-management performance, which activated their self-management behaviors. The mHealth service also gave outpatients access to assistance, which included health education and self-management reminders. Both types of assistance could also be activated by abnormal assessment results, that is, uncontrolled or deteriorating blood pressure values, discomfort symptoms, or not using the service for a long period. With its scalable use to handle any possible information and services, the mHealth service provided outpatients with educational materials to learn at their own pace. This led to an improvement in self-management awareness and ability, again activating their self-management behaviors. The patients would like to see further improvements in the service to provide more useful, personalized information and reliable services. CONCLUSIONS: The mHealth service extended the traditional hypertension care model beyond the hospital and clinician's office. It provided outpatients with easy access to otherwise inaccessible hypertension management services. This led to process improvement for outpatients to access health assessment and health care assistance and improved their awareness and self-management ability, which activated their hypertension self-management behaviors. Future studies can apply the 6A framework to guide the design, implementation, and evaluation of mHealth services for outpatients to self-manage chronic conditions.

Association of Three Simple Insulin Resistance Indexes with Prehypertension in Normoglycemic Subjects.

Background: Insulin resistance (IR) is the common pathophysiology of prehypertension and prediabetes. Recognition of IR in one of the two disease states is critical for carrying out preventive strategies of another disease state. This study aimed to explore which simple IR indexes were significantly associated with prehypertension in subjects with normoglycemia. Methods: A total of 108,370 adults without elevated fasting plasma glucose and hypertension were included in this study. The three simple IR indexes [triglycerides to high-density lipoprotein cholesterol ratio, the product of fasting triglycerides and glucose, and metabolic score for IR (METS-IR)] were calculated. Partial correlation was used to analyze the correlation between the three indicators and blood pressure (BP) levels, and logistic regression analysis was used to explore their association with prehypertension. Results: Among the three indicators, only METS-IR had positive correlations with systolic and diastolic blood pressure levels. Furthermore, METS-IR was also significantly associated with prehypertension, irrespective of the categorization of waist circumference (WC). The odds ratios of the highest quartile were 2.223 (95% confidence interval [CI]: 2.044-2.417) in all subjects, 2.022 (95% CI: 1.501-2.725) in elevated WC subgroup, and 1.815 (95% CI: 1.620-2.034) in normal WC subgroup. Conclusions: METS-IR was associated with prehypertension in normoglycemic Chinese subjects, which bypasses the impact of WC and might be valuable for the management of prehypertension and the prevention of prediabetes in different ethnic groups.

A new approach to integrate GPU-based Monte Carlo simulation into inverse treatment plan optimization for proton therapy.

Monte Carlo (MC)-based spot dose calculation is highly desired for inverse treatment planning in proton therapy because of its accuracy. Recent studies on biological optimization have also indicated the use of MC methods to compute relevant quantities of interest, e.g. linear energy transfer. Although GPU-based MC engines have been developed to address inverse optimization problems, their efficiency still needs to be improved. Also, the use of a large number of GPUs in MC calculation is not favorable for clinical applications. The previously proposed adaptive particle sampling (APS) method can improve the efficiency of MC-based inverse optimization by using the computationally expensive MC simulation more effectively. This method is more efficient than the conventional approach that performs spot dose calculation and optimization in two sequential steps. In this paper, we propose a computational library to perform MC-based spot dose calculation on GPU with the APS scheme. The implemented APS method performs a non-uniform sampling of the particles from pencil beam spots during the optimization process, favoring those from the high intensity spots. The library also conducts two computationally intensive matrix-vector operations frequently used when solving an optimization problem. This library design allows a streamlined integration of the MC-based spot dose calculation into an existing proton therapy inverse planning process. We tested the developed library in a typical inverse optimization system with four patient cases. The library achieved the targeted functions by supporting inverse planning in various proton therapy schemes, e.g. single field uniform dose, 3D intensity modulated proton therapy, and distal edge tracking. The efficiency was 41.6 ± 15.3% higher than the use of a GPU-based MC package in a conventional calculation scheme. The total computation time ranged between 2 and 50 min on a single GPU card depending on the problem size.

An Automated Treatment Plan Quality Control Tool for Intensity-Modulated Radiation Therapy Using a Voxel-Weighting Factor-Based Re-Optimization Algorithm.

Intensity-modulated radiation therapy (IMRT) currently plays an important role in radiotherapy, but its treatment plan quality can vary significantly among institutions and planners. Treatment plan quality control (QC) is a necessary component for individual clinics to ensure that patients receive treatments with high therapeutic gain ratios. The voxel-weighting factor-based plan re-optimization mechanism has been proved able to explore a larger Pareto surface (solution domain) and therefore increase the possibility of finding an optimal treatment plan. In this study, we incorporated additional modules into an in-house developed voxel weighting factor-based re-optimization algorithm, which was enhanced as a highly automated and accurate IMRT plan QC tool (TPS-QC tool). After importing an under-assessment plan, the TPS-QC tool was able to generate a QC report within 2 minutes. This QC report contains the plan quality determination as well as information supporting the determination. Finally, the IMRT plan quality can be controlled by approving quality-passed plans and replacing quality-failed plans using the TPS-QC tool. The feasibility and accuracy of the proposed TPS-QC tool were evaluated using 25 clinically approved cervical cancer patient IMRT plans and 5 manually created poor-quality IMRT plans. The results showed high consistency between the QC report quality determinations and the actual plan quality. In the 25 clinically approved cases that the TPS-QC tool identified as passed, a greater difference could be observed for dosimetric endpoints for organs at risk (OAR) than for planning target volume (PTV), implying that better dose sparing could be achieved in OAR than in PTV. In addition, the dose-volume histogram (DVH) curves of the TPS-QC tool re-optimized plans satisfied the dosimetric criteria more frequently than did the under-assessment plans. In addition, the criteria for unsatisfied dosimetric endpoints in the 5 poor-quality plans could typically be satisfied when the TPS-QC tool generated re-optimized plans without sacrificing other dosimetric endpoints. In addition to its feasibility and accuracy, the proposed TPS-QC tool is also user-friendly and easy to operate, both of which are necessary characteristics for clinical use.

A new Monte Carlo-based treatment plan optimization approach for intensity modulated radiation therapy.

Intensity-modulated radiation treatment (IMRT) plan optimization needs beamlet dose distributions. Pencil-beam or superposition/convolution type algorithms are typically used because of their high computational speed. However, inaccurate beamlet dose distributions may mislead the optimization process and hinder the resulting plan quality. To solve this problem, the Monte Carlo (MC) simulation method has been used to compute all beamlet doses prior to the optimization step. The conventional approach samples the same number of particles from each beamlet. Yet this is not the optimal use of MC in this problem. In fact, there are beamlets that have very small intensities after solving the plan optimization problem. For those beamlets, it may be possible to use fewer particles in dose calculations to increase efficiency. Based on this idea, we have developed a new MC-based IMRT plan optimization framework that iteratively performs MC dose calculation and plan optimization. At each dose calculation step, the particle numbers for beamlets were adjusted based on the beamlet intensities obtained through solving the plan optimization problem in the last iteration step. We modified a GPU-based MC dose engine to allow simultaneous computations of a large number of beamlet doses. To test the accuracy of our modified dose engine, we compared the dose from a broad beam and the summed beamlet doses in this beam in an inhomogeneous phantom. Agreement within 1% for the maximum difference and 0.55% for the average difference was observed. We then validated the proposed MC-based optimization schemes in one lung IMRT case. It was found that the conventional scheme required 10(6) particles from each beamlet to achieve an optimization result that was 3% difference in fluence map and 1% difference in dose from the ground truth. In contrast, the proposed scheme achieved the same level of accuracy with on average 1.2 × 10(5) particles per beamlet. Correspondingly, the computation time including both MC dose calculations and plan optimizations was reduced by a factor of 4.4, from 494 to 113 s, using only one GPU card.

Navigated DENSE strain imaging for post-radiofrequency ablation lesion assessment in the swine left atria.

AIMS: Prior work has demonstrated that magnetic resonance imaging (MRI) strain can separate necrotic/stunned myocardium from healthy myocardium in the left ventricle (LV). We surmised that high-resolution MRI strain, using navigator-echo-triggered DENSE, could differentiate radiofrequency ablated tissue around the pulmonary vein (PV) from tissue that had not been damaged by radiofrequency energy, similarly to navigated 3D myocardial delayed enhancement (3D-MDE). METHODS AND RESULTS: A respiratory-navigated 2D-DENSE sequence was developed, providing strain encoding in two spatial directions with 1.2 × 1.0 × 4 mm(3) resolution. It was tested in the LV of infarcted sheep. In four swine, incomplete circumferential lesions were created around the right superior pulmonary vein (RSPV) using ablation catheters, recorded with electro-anatomic mapping, and imaged 1 h later using atrial-diastolic DENSE and 3D-MDE at the left atrium/RSPV junction. DENSE detected ablation gaps (regions with >12% strain) in similar positions to 3D-MDE (2D cross-correlation 0.89 ± 0.05). Low-strain (<8%) areas were, on average, 33% larger than equivalent MDE regions, so they include both injured and necrotic regions. Optimal DENSE orientation was perpendicular to the PV trunk, with high shear strain in adjacent viable tissue appearing as a sensitive marker of ablation lesions. CONCLUSIONS: Magnetic resonance imaging strain may be a non-contrast alternative to 3D-MDE in intra-procedural monitoring of atrial ablation lesions.

[A field assessment on the risk factors of musculoskeletal disorders].

OBJECTIVE: To investigate the usability of quick exposure check (Quick Exposure Check, QEC) for the field assessment of occupational musculoskeletal disorder risk factors. METHOD: In the shipyard and automobile manufacturing plants, QEC was used to observe the operations among workers with different jobs and to assess the work loads of workers. On the basis of results, the reliability of QEC was evaluated, and the correlation between QEC scores and morbidities of musculoskeletal disorders in workers was analyzed. RESULTS: The inter-observer reliability (ICC) was in the range from 0.737 to 1.000, and intra-observer reliability (Spearman coefficient) was from 0.605 to 1.000. The order of exposure levels to risk factors of workers engaged in different jobs (QEC scores) in the shipyard factory was plumbers > assemblers > welders; The order of exposure levels to risk factors of workers engaged in different jobs (QEC scores) in the automobile factory was welders > punching workers > machinists > casters > assemblers. In different body parts, the exposure level at back and neck parts was the highest and the exposure level at the shoulder and wrist parts was the second. The regression analysis between QEC scores of body parts and the morbidities of musculoskeletal disorders showed that there was a good correlation between exposure levels and morbidities, the coefficients (r(2)) at the shoulder, wrist, and back (static work) were 0.670, 0.740 and 0.958, respectively (P < 0.05). CONCLUSION: The QEC method is suitable and reliable as demonstrated by the field assessment on the exposure to risk factors in shipyard and automobile workers, and its results is correlated closely to the disease prevalence.

Functional assessment of the kidney from magnetic resonance and computed tomography renography: impulse retention approach to a multicompartment model.

A three-compartment model is proposed for analyzing magnetic resonance renography (MRR) and computed tomography renography (CTR) data to derive clinically useful parameters such as glomerular filtration rate (GFR) and renal plasma flow (RPF). The model fits the convolution of the measured input and the predefined impulse retention functions to the measured tissue curves. A MRR study of 10 patients showed that relative root mean square errors by the model were significantly lower than errors for a previously reported three-compartmental model (11.6% +/- 4.9 vs 15.5% +/- 4.1; P < 0.001). GFR estimates correlated well with reference values by (99m)Tc-DTPA scintigraphy (correlation coefficient r = 0.82), and for RPF, r = 0.80. Parameter-sensitivity analysis and Monte Carlo simulation indicated that model parameters could be reliably identified. When the model was applied to CTR in five pigs, expected increases in RPF and GFR due to acetylcholine were detected with greater consistency than with the previous model. These results support the reliability and validity of the new model in computing GFR, RPF, and renal mean transit times from MR and CT data.

Renal function measurements from MR renography and a simplified multicompartmental model.

The purpose of this study was to determine the accuracy and sources of error in estimating single-kidney glomerular filtration rate (GFR) derived from low-dose gadolinium-enhanced T1-weighted MR renography. To analyze imaging data, MR signal intensity curves were converted to concentration vs. time curves, and a three-compartment, six-parameter model of the vascular-nephron system was used to analyze measured aortic, cortical, and medullary enhancement curves. Reliability of the parameter estimates was evaluated by sensitivity analysis and by Monte Carlo analyses of model solutions to which random noise had been added. The dominant sensitivity of the medullary enhancement curve to GFR 1-4 min after tracer injection was supported by a low coefficient of variation in model-fit GFR values (4%) when measured data were subjected to 5% noise. These analyses also showed the minimal effects of bolus dispersion in the aorta on parameter reliability. Single-kidney GFR from MR renography analyzed by the three-compartment model (4.0-71.4 ml/min) agreed well with reference measurements from (99m)Tc-DTPA clearance and scintigraphy (r = 0.84, P < 0.001). Bland-Altman analysis showed an average difference of 11.9 ml/min (95% confidence interval = 5.8-17.9 ml/min) between model and reference values. We conclude that a nephron-based multicompartmental model can be used to derive clinically useful estimates of single-kidney GFR from low-dose MR renography.

Assessment of Adipose Tissue from Whole Body 3T MRI Scans.

Accurate assessment of total body and the distribution of regional adipose tissue is a critical problem emerging in the United States. Using manual segmentation of MRI scanned images is a problem due to the high variation between manual delineations. Manual segmentation also requires highly trained experts with knowledge of anatomy. In this study, we used a specific water saturation sequence and histogram based segmentation method that provides robust delineation results for adipose tissue from whole body MRI scans. Both phantom and human subject studies were performed. Compared with a standard clinical T1-weighted acquisition, our method appears to give superior quantitative results.