Reducing avoidable hospital admissions is often viewed as a possible positive consequence of introducing integrated care (IC). The aim of this study was to investigate the impact of implementing IC in Norrtälje on the rate of admissions for ambulatory care sensitive conditions (ACSC).Using interrupted time series analyses we investigated the effect of implementing IC in Norrtälje municipality in the northern part of Stockholm county, Sweden. The time period included 48 time points, from year 2000 to year 2011 with measurements before and after introducing IC in Norrtälje in 2006. In order to control for other extraneous events that could affect the outcome measure, but not related to the introduction of IC, we included a control population from Stockholm municipality.After introducing IC in Norrtälje the rate of admissions for ACSC decreased. This decrease was greater in Norrtälje than in the matched control population, however the difference between the two areas was not statistically significant (p = 0.08).Introducing IC in Norrtälje may have had positive impact on admissions for ACSC for older people living in Norrtälje; however, the interpretation of the impact of IC on admissions for ACSC is complicated by intervening policy changes in health and social care during the study period. The health problems of older people are often characterized by multi-morbidity and frailty in combination with multiple functional  limitations 2. This rIntegrated care has become an international buzzword in the field of health policy and health management research as a way to tackle both financial and quality issues in a world with increasing costs and high demands on health and social care . Integra57In Sweden health care is decentralised and the responsibility of the 21 different regions. Social care on the other hand is the responsibility of municipalities. Both health- and social care have a public and private providers, however, both sectors are publicly funded and regulated. As every region is responsible for managing and prioritising resources for health care and every municipality for social care, care may vary between regions and municipalities .This division of responsibilities between the regions and municipalities are not conducive to good coordination between health and social care. Therefore, policies to improve care coordination, especially for older people with complex health problems and severe needs, have been high on the social policy agenda in recent years. Integrated care projects of different scales have been one attempt to manage these problems in several regions of Sweden . NeverthThe company TioHundra is owned by the “Municipal association of health and social care in Norrtälje”, which is a cooperation between Region Stockholm  and the municipality of Norrtälje . This organisation was founded in 2006 after some years of political debate about whether the local hospital, owned and financed by the Region Stockholm, should be shut down due to financial difficulties. Huge protests from the citizens and the local politicians in Norrtälje led to a new initiative to improve health care and social care through extended cooperation between providers and a shared responsibility for both health and social care in one organisation.Table 1). The goal of the Norrtälje model is to achieve vertical and horizontal cooperation in order to better respond to the health care needs of the population. Besides shared financial responsibility between the municipality and the region for both health care and social care, the model is characterised by having a focus on health promotion for the population, as well as integrating the health and social care organisation on the administrative level. From the start TioHundra have particularly focused on interventions for older adults with complex health and social care needs [TioHundra operates as one comprehensive health and social care organisation, owned, financed and managed jointly by the Norrtälje municipality and Region Stockholm, and was established with the objective to improve efficiency, quality and coordination in care provision while still controlling the costs. The process started in 2006 with the implementation of the macro structure and pooling of resources, but the implementation of full integration has been continuously improved along the way and is still ongoing  analysis . This is a quasi-experimental design with a series of periodic measurements before and after introducing IC in Norrtälje in 2006. The time period includes 48 time points, from year 2000 to year 2011.In order to control for other extraneous events that could affect the outcome measure, but not related to the introduction of IC in Norrtälje, we included a control population from Stockholm municipality, individually matched by year, age, sex and level of income. Introducing control groups in interrupted time series analyses has been shown to increase validity of ITSs to a level comparable with randomized control trials 25.Data on hospitalisation for ACSC was obtained from the Region Stockholm administrative database for analysis and follow-up of healthcare utilisation (VAL). This database contains information on all registered outpatient and inpatient care financed by Region Stockholm.Sociodemographic background characteristics were obtained from the ‘Longitudinal integration database for health insurance and labour market studies’ (LISA by Swedish acronym). The LISA database is a collection of individual level variables from different population registers linked individually through encrypted personal identity numbers. These data were individually linked to the VAL database by Statistics Sweden and used for matching purposes.The primary outcome variable is the quarterly rate of hospitalisation for ACSC. ACSC refers to hospitalizations for conditions that would have been possible to avoid by timely and effective use of primary care and includes both chronic diseases and acute diseases. The definition of ACSC may vary between different settings as well as the included conditions. In this study ACSC conditions were derived from the VAL register and coded using a categorisation adapted from that used by the British National Health Services . The ada27The population in Norrtälje was slightly older, had a smaller proportion of men and on average a lower income than the control group. These factors have all been shown to be correlated with hospitalization for ambulatory care sensitive conditions. 29. ThereWhen more than one individual in the comparison group matched the Norrtälje profile, then a random procedure was used to select one control individual. Age was used as a continuous variable. The income variable used was individualised disposable income (after taxes and transfers). Income was then categorised into income quintiles.In the subgroup analyses on low income, only the lowest income group was used.We used the generalised additive model with the thin plate spline as a fitting method in the interrupted time series analysis to find the function best fitted for the data. Where the non-linear changes over time could be approximated by straight lines, we performed interrupted time series analyses using ordinary least squares to estimate the trends before 2006 as well as the trend change after. The Durbin-Watson test and the partial autocorrelation function were used to assess autocorrelation.In order to assess whether changes in Norrtälje could be attributed to the introduction of IC we calculated quarterly rates of hospitalisation for ACSC in 2000–2011 for a matched population from Stockholm municipality. The relative difference between the two areas was then used to assess whether changes appeared over time between the two areas.To investigate whether integrated care would affect equity in health care utilisation among the older population we calculated the relative index of inequality (RII) for hospitalisation for ACSC. RII was calculated based on the average cumulative proportion in each income quintile.Data management was handled in SAS 9.4 and the interrupted time series analysis was performed in the statistical software R  using thTable 2).Norrtälje had 10,249 inhabitants in 2000 aged 65 years or above. In 2011 the number had increased to 13,893. In this period the mean age decreased by about 1 year from 75.7 to 74.7. The proportion of men increased from 45.3% to 47.5% and the proportion living alone decreased from 44.3% to 43.5% . The effective degrees of freedom for the smoothing term was 1.8 and the model showed a nonlinear change that could be approximated by 2 straight lines; one before and one after the intervention. We therefore fitted a linear model using ordinary least squares giving the possibility to calculate linear trends before the introduction of IC as well as trend change after. The linear model showed that the rate of hospitalisation for ambulatory care sensitive conditions did decrease significantly after introducing IC in Norrtälje . When comparing to the changes in a control population this decrease was larger in Norrtälje, however not statistically significantly different from that in the control population .Based on Akaike information criterion (AIC) and evaluation of the results from the partial autocorrelation function (PACF) we found that a generalised additive model with a thin plate spline(?) and 3 degrees of freedom was the best fitted model for describing the non-linear change in rate of hospitalisation for ACSC (see Table 3).For the age-group 80+ neither the pre-intervention trend nor the post-intervention trend was significant. The same was the case for the relative difference between Norrtälje and the control group. We did not find any significant changes in the relative index of inequality in Norrtälje in this time period . Adding a linear trend term from 2006 decreased the AIC and showed a significant decreasing post-intervention trend . The best fitted model was a GAM with two trend terms one post-intervention trend and one post-2010-trend (blue line). This model showed a steep decreasing post-intervention trend from 2006 and a decreasing, but much less steep decreasing trend from 2010 . The green line shows the fitted line from a linear model with two added linear trend changes in 2006 and 2010 (only 2006 was significant).The change in rate of hospitalisation for ACSC among the lowest income group was best fitted with a generalised additive model (GAM) with a thin plate spline and 4 degrees of freedom and could not be approximated by two straight lines to calculate the trend and trend change after the introduction of IC. The predicted line based on the GAM without fixed effects is shown in Two major changes to health and social care policy were implemented in the post intervention period. In 2009 a choice reform in social care was implemented, introducing free choice of provider in social care in the entire Stockholm region, coupled with the right to free establishment for private providers of social care. This led to multiple providers establishing in Norrtälje alongside Tiohundra, challenging the coordination between health and social care. In 2010 a similar reform was introduced in primary care giving private providers of primary care the right to establish in Norrtälje, In the rest of Region Stockholm the reform in primary care was implemented already in 2008. However, in Norrtälje, Tiohundra was still, by far, the main provider of both health and social care. Due to these policy reforms, we made sensitivity analyses introducing the possibility of additional trend changes in 2009 and 2010 for Norrtälje and 2008 and 2009 for the Stockholm control group.For most outcome measures, additional trends were not significant and did not add to the interpretation of the results. However, introducing an additional trend change in 2009 did make the relative changes between Norrtälje and Stockholm significant. To investigate this further, we therefore restricted the analyses to three years pre and three years post IC, in order to minimise the effect of additional policy changes.For the rate of ACSC in Norrtälje the coefficients were in the same direction as the overall analysis. The pre-intervention trend was not as steep (0.0464 vs 0.1018), however the post-intervention trend change was somewhat larger (–0.3856 vs –0.2441). None of the estimates were statistically significant in the model based on data from 2003–2008.When analysing the relative difference between Norrtälje and the matched control group, results were in the same direction. The pre-intervention trend in the matched control group was 0.0133 (p = 0.282) and the trend change after the intervention was –0.0415 with a p-value of 0.056. This indicates that in Norrtälje, the rate of hospitalisation for ACSC decreased more in comparison with the matched control group in the first years after IC was implemented. This corresponds to a 36% decrease in the relative difference in 2008 compared to the difference that would have been, had the pre-intervention trend continued.In this study we have examined the impact on rates of ACSC following the organisational change in health and social care in Norrtälje that started 2006 with the foundation of the company TioHundra. The goal was to achieve vertical and horizontal cooperation in order to better respond to the health care needs of the population in Norrtälje by pooling resources from the municipality of Norrtälje and Region Stockholm and integrating health and social care into one organisation. The results showed that the rate of hospitalisation for ACSC did drop among the older population living in Norrtälje after introducing IC in Norrtälje. The decrease in the rate of hospitalisation for ACSC was greater in Norrtälje than in a matched control group, however not significantly different from the decrease in the matched control group (p = 0.083). Looking at only the period immediately after the introduction of IC the decrease was more pronounced (p = 0.056). Other policy changes that were introduced after the implementation of IC in Norrtälje may have interfered with the decreasing trend in Norrtälje. It might have become more difficult to maintain good co-ordination and integration between multiple providers in the years after introducing free establishment for private providers of health and social care in Norrtälje.For the group aged 80+ years there was a tendency to an upward trend change, however neither the pre-intervention trend nor the post-intervention trend change was significant, indicating that the rate of hospitalisation for ACSC was stable in this age group. These results are surprising as TioHundra have had a specific focus on the frail and older population of Norrtälje and we would have expected this group to have benefitted more. At the same time studies and reports suggest that older people and people suffering from multimorbidity have benefitted less from the primary care reform and receive relatively less primary care compared to other patient groups 33, whichThe trends in the group with low income could not be modelled in a linear model with a pre-intervention trend and a post-intervention trend change. Instead, we used the GAM and found that a model with two post-intervention trends, one from 2006 and one from 2010, best fitted the data. The results of this model suggested a decrease in rate of hospitalisation for ACSC in 2006 and a decrease, but less steep, in 2010.Robust evaluations of comprehensive integrated care systems are extremely rare and in many cases programmes are evaluated using uncontrolled before and after comparisons .One strength of the approach in this study is the long time-series we were able to create using the unique registers in Sweden and the possibility to monitor changes in outcomes several years before and after introducing the integrated care model. On the other hand, a weakness of this longitudinal design is that it introduces a risk of other events influencing the outcome, such as the policy reforms in health and social care. Including a control group from an area close to Norrtälje, bound by the same rules and regulations, limits these threats to validity, as general changes in e.g. rules and regulations will also be detected in the control group. This makes it more reliable that the detected differences could be attributed to the different level of integration in the organizations.Nevertheless, some policy changes were not introduced at the same time in Norrtälje and the control area. The primary care reform, introducing a new reimbursement system, free choice of provider as well as free establishment for private providers were introduced in Region Stockholm, except Norrtälje, in 2008. In Norrtälje this reform was not introduced until 2010. It is unclear what impact the time difference in introducing the reform could have had on the results of this study. The reform increased the number of providers of primary care in many areas  as well In what we refer to as standard care, health care and social care are separated and financed by the regions and the municipality, respectively. There is no shared funding or responsibility between the two organisations and there are no financial incentives for e.g. social care to minimise hospital care or vice versa as these are two separate budgets and two different organisations responsible for each sector. Nevertheless, as many patients move between e.g., hospitals and social care, there are of course routines for how transitions between the two systems should work. Even in standard care there is a desire to make this transition as smooth and positive for the patient as possible and e.g., give the necessary information to the social care system, when discharging a patient from inpatient care. This could be a reason for the modest change we see in this study.We have hypothesised that living in an integrated care system with better possibility for communication between health care providers, as well as social care providers, potentially could lead to quicker attention to older people’s health care needs and thereby avoiding hospitalisation for conditions that could have been attended in primary care. In this study we have looked at a long range of conditions; however, previous studies have suggested that it might only by some of the ACSC that are related to quality and continuity in primary care . As NorrThe fact that the implementation of IC in Norrtälje started with a focus on the macro structure and pooling of resources, and that the process of getting to full integration has been a longer process, has made the evaluation difficult and might be one reason for the modest changes that we see in this study. Longer follow-up times introduce the risk of other factors influencing the outcome, as has been the case for this study. The assumption for the interrupted time series analysis is that only the intervention affects the post intervention trend change, and therefore it can be estimated with only one estimate. Nevertheless, if other factors are introduced after the introduction of the initial intervention, there needs to be more interruptions in the model to allow for these changes as well. That might be the reason for the fact that we find a steeper decrease in Norrtälje compared to the control group in the period immediately after introducing IC. Studying the effect close in time to the intervention, there is a higher chance of capturing the pure effect of the intervention.IC is a broad concept and integration can be achieved in different ways and on different levels. To the best of our knowledge this is the first study to investigate the effect of organisational integration between health and social care on the rate of hospitalisations for ACSC. The lack of possibility to compare to previous studies makes it difficult to understand whether these results can be generalised for other IC organisations or whether they are specific to the way health and social care is integrated in Norrtälje. Further research is needed to confirm our results.The one study we found with positive effects of IC on hospitalisations for ACSC focused on frail older people . In thisAfter introducing IC in Norrtälje the rate of hospitalisation for ACSC decreased among the older population living in Norrtälje. This decrease was greater than in a matched control population in an area with standard care, however the difference between the two areas was not statistically significant (p = 0.08). Analyses of changes in the relative index of inequality in hospitalisations for ACSC showed no significant changes over time, indicating that IC did not affect inequality in hospitalisation for ACSC. The interpretation of the impact of IC on hospitalisation for ACSC is complicated by intervening policy changes in health and social care during the study period.


In this study, we evaluate a coronal mass ejection (CME) arrival prediction tool that utilizes the wide‐angle observations made by STEREO's heliospheric imagers (HI). The unsurpassable advantage of these imagers is the possibility to observe the evolution and propagation of a CME from close to the Sun out to 1 AU and beyond. We believe that by exploiting this capability, instead of relying on coronagraph observations only, it is possible to improve today's CME arrival time predictions. The ELlipse Evolution model based on HI observations (ELEvoHI) assumes that the CME frontal shape within the ecliptic plane is an ellipse and allows the CME to adjust to the ambient solar wind speed; that is, it is drag based. ELEvoHI is used to perform ensemble simulations by varying the CME frontal shape within given boundary conditions that are consistent with the observations made by HI. In this work, we evaluate different setups of the model by performing hindcasts for 15 well‐defined isolated CMEs that occurred when STEREO was near L4/5, between the end of 2008 and the beginning of 2011. In this way, we find a mean absolute error of between  CME prediction tool ELEvoHI is ready to be used in real time, based on STEREO‐A/HI beacon dataDifferent model setups and inputs lead to large differences of the prediction accuraciesAccurate modeling of the ambient solar wind is of particular importance to improve CME predictions CMEs are impulsive outbursts of the solar corona, consisting of a magnetic flux rope that impounds coronal material and solar wind particles during its propagation through the interplanetary medium. Fast CMEs can reach speeds of up to 3,000 km sBesides the magnetic field, the arrival speed of the CME plays an important role as high impact speeds, including those of the shock front driven by the CME, can intensify a geomagnetic disturbance , a platform that is used by scientists and model developers to test their models in real time. It was found that the model with the best performance  achieved a mean absolute arrival time error of 13 hr with a standard deviation of Most prediction models rely on images from coronagraphs that observe the solar corona out to a maximum plane‐of‐sky distance of 30 Other instruments that enable CMEs to be observed in white light are the heliospheric imagers  noninteracting Earth‐directed CMEs within the time range extending from the end of 2008 until the beginning of 2011 (Table Parts of this study use coronagraph images provided by (1) the SOHO mission, with LASCO C2 and C3  was first presented by Rollett et al.  as a sinAmerstorfer et al.  introducELEvoHI is a combination of three main modules that derive parameters from observations to serve as input to the next module. Figure The starting point is the CME time‐elongation track, t et al. .γ=CDACMEρswmCME, is the parameter that results from least squares fitting of the time‐distance track within the DBM fitting routine implemented in ELEvoHI. In the next step, each ensemble member time‐distance track for the CME is fitted using an equation of motion based on the DBM given in  and propagation direction, The procedure of defining The parameters obtained by DBM fitting serve as input for the ELlipse Evolution model  enables the manual fitting of a croissant‐shaped CME body to simultaneous images from coronagraphs observing from different vantage points. In our study, we use images from STEREO/COR2 from both sides, as well as LASCO/C2 and/or C3 images. Several shape‐related CME parameters can be adjusted within a widget tool until the best match with the CME visible within the coronagraph images is achieved. For our purposes, GCS is run as a part of the so‐called EAGEL (Ecliptic cut Angles from GCS for ELEvoHI) tool, which is described below.The first potential method to provide Δϕ=2° and Δλ=5°. The parameter f, which is related to the curvature of the front, is not obtained from the ecliptic cut but is, instead, varied between 0.7  and 1 .Within EAGEL the download and preprocessing of the coronagraph data is included in such a way that a CME is clearly recognizable in the images. Based on these images, GCS fitting of a CME is performed. EAGEL then creates an ecliptic cut of the wire frame of the fitted CME and calculates a et al. , who cit4.1.2In the study predicting CME arrival times and speeds using ELEvoHI performed by Rollett et al. , the proTo test the effect of assuming a finite CME width, we use the direction of motion from FPF as well as from SSEF EAGEL direction and half‐width,(2)FPF direction and predefined angular half‐width from 30–50°, and(3)SSEF direction and predefined angular half‐width from 30–50°.For the ELEvoHI model setup test, as discussed in this section, we use the following inputs for the CME direction and angular half width: 4.24.2.1In its current version, ELEvoHI accepts only a constant (in space and time) background solar wind input. Rollett et al.  and Amer4.2.2In order to find a better method, we investigate whether the DBM fit is able to “decide” for itself which solar wind speed best fits the CME kinematics. To this end, we calculated the mean solar wind speed in OMNI data between the Years 2004 and 2018 to be 425 km sThis approach allows the model to select from a wide range of possible background solar wind speeds for itself. This is possible because the HI kinematics are not compatible with every possible solar wind speed. Depending on the CME speed and its evolution, that is, whether the CME is decelerating, accelerating, or propagating with a constant speed, only some candidate solar wind speeds will result in a converging DBM fit. Due to the wide range of ensemble members, each having different kinematics . More specifically, we use magnetic maps of the photospheric field from the Global Oscillation Network Group (GONG) of the National Solar Observatory (NSO) as input to magnetic models of the solar corona. Using the Potential Field Source Surface (PFSS) model in situ data from 1 AU,(2)speed range derived from statistics, and(3)modeled by WSA‐HUX model.To test the ELEvoHI model setup, we use the three previously discussed methods to provide the source for the ambient solar wind speed, that is, 4.3In the current version of ELEvoHI, the optimal DBM fit  is defined as the fit with the smallest mean residual to the time‐distance profile along the whole extent of the fitted curve. The ambient solar wind speed associated with the best DBM fit is then used for further modeling. Usually, the DBM fit is performed over a radial distance of around 30 to 100 (1)the smallest mean residual along the whole extent of the fit,(2)the smallest mean residual of the last three fitted points.For testing the ELEvoHI model setup, we use the two previously discussed methods for evaluating the DBM fit and choosing the most suitable background solar wind speed, that is,4.4In order to compare the results of the different ELEvoHI ensemble runs to a well‐established but simple prediction method that relies on HI data only, we use FPF  of t, and speed, v, for each of the 18 different model setups. Negative values correspond to an underestimated transit time; hence, the event was predicted to arrive earlier than it actually did. In the case of the arrival speed prediction, negative values correspond to an underestimated arrival speed. The results are ordered from smallest to largest MAE in arrival time, revealing that the six setups using the WSA‐HUX output as input for the ambient solar wind estimate lead to the most accurate predictions. The benchmark FPF technique leads to an MAE of 7.8 hr with an MSTD of 10.5 hr and an ME of 2.6 hr, which means that FPF has a tendency to overestimate the transit time. Considering the underlying geometry assumed by FPF, this is not surprising. FPF reduces the CME front to a single point and assumes that this point is tracked throughout the CME's propagation. Being conscious of the fact that CMEs can be extremely large‐scale structures, it is clear that this is an oversimplification. Additionally, our CME sample almost exclusively consists of slow CMEs for which the assumption of constant propagation speed is usually close to reality. The faster the CME and hence the larger its likely deceleration, the larger the error due to a constant speed assumption  is 11 hr and the MAE(t) is v) is 66 km sv) is 53 km sFigure Some of the events under study . The statistical solar wind approach is directly implemented within the ELEvoHI model. As shown above, this setup leads to an MAE in arrival time of 8.6 hr and an ME of 2.7 hr. However, if an ambient solar wind solution is available in real time , ELEvoHI can achieve an MAE of 6.2 hr with an ME of 0.1 hr—still without the necessity for additional coronagraph data or the need for manual fitting to these images. Of course, we always need to keep in mind that these values are derived from a predefined set of very well observed, and isolated, events and from HI science quality data that is currently not available in real time. However, HI beacon data are available in near real time and can serve as input to ELEvoHI since STEREO‐A/HI is already close to L5 and is observing the space between the Sun and Earth—hopefully until 2027, when it will be around L4. An additional possibility for having HI real‐time data available in the future might be provided by the Polarimeter to Unify the Corona and Heliosphere (PUNCH) mission. PUNCH will be launched in 2023 and will operate in low Earth orbit.For real‐time predictions, it is of the utmost importance to be able to include an estimate of the arrival probability with a CME prediction. Currently, ELEvoHI simply calculates this as the ratio of the number of ensemble members that are predicted to hit the target to the total ensemble size. This is going to be updated in the near future, to give predicted flank hits a lower weighting. In addition, we have noticed that for flank hits, the arrival time error tends to be larger than expected and the transit time is overestimated. This could be due to the elliptical shape of the front resulting in highly curved flanks. In the future, we will examine if we can find a suitable approach to deal with these strongly bent flanks to avoid such extreme delays when predicting a flank encounter.7In this work we studied 18 different combinations of inputs to run the HI‐based ensemble CME arrival prediction model, ELEvoHI, in order to ascertain the setup leading to the most accurate arrival time and speed predictions. As input for the ambient solar wind that influences the drag‐based propagation of the modeled CME we used (1) the WSA‐HUX background solar wind model, (2) an approach of simply providing a range of possible solar wind speeds  to lead to better predictions.To analyze the influence of the CME frontal shape/propagation direction on ELEvoHI predictions, we compared three different sources of The third aspect of the model setup that we tested is the model‐intrinsic procedure to determine the optimal DBM fit to the HI kinematics. This process defines the ambient solar wind speed, which is further used as a basis for the arrival prediction. Several DBM fits were performed over the provided range of solar wind speeds; the best DBM fit then determined the optimal ambient solar wind speed and drag parameter. We compared two ways of defining the best DBM fit, namely, the fit with the minimum value of the mean residual of (1) the whole fit or (2) the last three points of the fit. We found that both procedures lead to similar results, but with using the residuals of the whole fit leading to slightly better predictions.Based on this study, we are now able to operate ELEvoHI to gain the best possible arrival predictions. Our results emphasize the importance of an accurate ambient solar wind model, as the solar wind heavily influences the drag‐based evolution of the CME. In the future, an interesting advancement might be to include a range of values for the solar wind to contribute to the ensemble instead of deriving only a single value per shape/direction setup. Another logical next step would be to release ELEvoHI from its rigid elliptical shape and to allow deformation due to the influence of the ambient solar wind. In any case, with ELEvoHI, we are prepared for real‐time CME arrival predictions, once a new HI observer is delivering high‐quality data in real time.


Patients with medical implants often are deprived of magnetic resonance imaging examination because of safety risks. One specific risk is the enhancement of the radiofrequency fields around the medical implant potentially resulting in significant tissue heating and damage. The assessment of this enhancement is a computationally demanding task, with simulations taking hours or days to converge. Conventionally the source of the radiofrequency fields, patient anatomy, and the medical implant are simulated concurrently. To alleviate the computational burden, we reformulate a fast simulation method that views the medical implant as a small perturbation of the simulation domain without the medical implant and calculates the radiofrequency fields associated with this perturbation. Previously, this method required an extensive offline stage where the result is intractable for large simulation domains. Currently, this offline stage is no longer required and the method is completely online. The proposed method results in comparable radiofrequency fields but is orders of magnitude faster compared to standard simulation technique; the finite-difference time-domain, the finite-sums, and the finite element methods. This acceleration could enable patient-specific and potentially online radiofrequency safety assessment. This is in part because of the excellent soft-tissue contrast provided by this imaging modality. Based on the principle of nuclear magnetic resonance, MRI requires three types of magnetic fields: a permanent strong magnetic field (1.5 or 3 T), rapidly switching magnetic gradient fields for spatially encoding the signal, and radiofrequency (RF) fields generated by an RF coil or antenna to excite the atomic nuclei to create the signal. This multitude of magnetic fields poses a severe safety risk for patients with medical implants. Also, this patient category is rapidly growing. For example, from Fig. 5. Both MRI investigations and medical implants are more prevalent in the aging population; it is estimated that 50–75% of patients with PMs and ICDs will require an MRI within the lifetime of the device6.Magnetic resonance imaging (MRI) is one of the main medical imaging modalities and has become indispensable for diagnosis, treatment planning, and patient follow-up studies. The number of people requiring an MRI examination is steadily increasing every year as can be seen in Fig. 19. The safety concerns include displacement, malfunctioning, and heating of the medical implant all of which can harm the patient under examination12. This has occurred in a handful of instances with detrimental outcomes17. Furthermore, in the USA alone around 300 cases of adverse events involving MRI and medical implants are reported to the FDA annually18. When these patients are scanned, extra precautions have to be taken; an individual risk-benefit analysis, extra monitoring and experienced physicians are mandatory, and an informed consent by the patient is required. Since a lot of smaller hospitals either do not have the equipment or the experience these patients are often referred to another hospital or not scanned at all.Patients with medical implants can be excluded for an MRI examination because of the many safety concerns that exist when introducing metal inside the MRI environment19, and for active implanted medical devices like pacemakers or neurostimulators; ISO/TS 1097420. Depending on the results of the prescribed tests and simulations included in this technical specification, an implant can obtain a label stating it is MRI safe, MRI conditional, or MRI unsafe. When a medical implant is labeled, MRI conditional limitations are placed on the MRI sequence parameters (e.g. maximum RF power). These limitations decrease the image quality and/or increase the required scan time.To avoid depriving patients of the diagnostic capabilities of MRI, implant manufacturers have been developing increasingly more products that are MRI-compatible. To facilitate this, implant manufacturers, MRI vendors, and the scientific community have set up a technical specification for passive implanted medical devices like surgical plates or vascular stents; ASTM F218217.One particularly demanding and challenging safety aspect of this technical specification is the RF safety testing. The RF fields that are generated during an MRI examination can induce currents in metallic implants which are excellent conductors. The induced currents cause charge accumulation at the interfaces between the implant and the tissues surrounding the implant. The charge accumulation creates conservative electric fields in those tissues and thereby enhances the RF electric field. This enhancement creates locations where the temperature is drastically increased, potentially causing tissue burns inside the patient or even excessive brain damageThe potentially dangerous enhancement of the electric field can be calculated using for example finite difference time domain (FDTD) or finite element method (FEM) simulations. The electric field inside the patient results in power deposition because of the conductivity of the tissue. The amount of dissipated power is expressed by the local specific absorption rate (SAR), which is the power deposition divided by the mass density of the tissue.21 and RF coils27 should be simulated. Furthermore, the location of the medical implant inside the patient anatomy and the patient position within the RF coil are important factors for the SAR increase. A single simulation can take up to hours or even days to converge even with GPU acceleration29. Therefore, the RF safety assessment of implants is typically done using simplified scenarios because the full analysis of every possible combination of patient anatomy, implant location inside the patient, patient position inside the scanner, and RF coil  is computationally too demanding.To correctly assess the RF safety of a medical implant a wide variety of patient anatomiesThese simplified scenarios are overly conservative to ensure safety and therefore often result in medical implants being conditionally safe. However, the resulting scanning constraints are for the largest patient group overly conservative because these constraints are determined for the worst-case scenario. This entails the worst implant position inside the patient in the worst position in the scanner while having the worst-case patient anatomy. Therefore, if the patient-specific RF interaction of the implant could be characterized before the patient arrives for their MRI examination, these conservative scanning constraints could be relaxed for the vast majority of patients. Furthermore, when the characterization of the RF interaction can be done within the time span of a few minutes the RF safety assessment could be performed online at the scanner. In addition, subject-specific RF safety assessment could make the large group of patients that carry non-labeled or multiple implants eligible again for MRI examination. Currently, the simulation process for RF safety assessment is too slow to be performed before a patient is scheduled for an MRI examination.31. In this method, if the RF field distribution without the implant is known, the introduction of the implant into the setup can be considered a small perturbation of the system. We consider a perturbation small if it does not perturb the source of the RF fields (i.e. the RF coil). The RF field distributions arising from this small perturbation can be calculated on the small domain where the implant is present in comparison to the entire simulation domain including the RF coil and patient anatomy. This method requires an extensive offline precomputation stage dependent on the frequency, patient anatomy, and location of the implant. Afterward, the method can calculate the RF fields within the small domain where the problematic heating will occur in mere seconds compared to hours using standard simulation techniques.In previous work, a method has been described to accelerate the simulation process significantly20 which uses the transfer function, together with the RF field distribution without the implant present. The transfer function is a simplified model of the electromagnetic interaction of the implant with the electric RF field distribution along the implant. Furthermore, the transfer function as in the ISO/TS 10974 is only defined for elongated implant such as pacemaker leads. For three-dimensional implants using the Tier 3 approach is not feasible, whereas the proposed method is.The proposed method bears some resemblance to the Tier 3 approach in the ISO/TS 1097431.Unfortunately, the proposed method cannot be used practically. The result of the offline precomputation stage requires such an excessive amount of memory storage that the maximum calculable size of the implant is rather small. For example, when a human head model, with average dimensions of 15.2 cm by 18.6 cm by 11.2 cm (i.e. from top of the head down to the eyes), is discretized on a 1 mm isotropic resolution this would require around 650 TB of memory to store all the simulated field distributions in single precision complex float numbers. Furthermore, using an FDTD solver running on a single GPU would take almost 3 years to complete, at 10 secs per simulation31. The result is an ultrafast calculation method of RF field enhancement by medical implants in MRI. The presented method is fast enough to be used for patient specific and/or online RF safety assessment. This would improve image quality and/or reduce scan time for most implants with a conditional MRI safety label. Apart from the patient specific and online possibilities for RF safety assessment, the presented method also enables RF field calculations for the highest standard of RF safety assessment (Tier 4 in ISO/TS 10974) in a significantly accelerated fashion.In this work, a rigorous adaptation of the earlier method is presented by which the drawbacks of the previous method are overcomeAlthough the method has been developed for MRI implant safety assessment, it is suitable for any application area where electromagnetic simulations (EM) are being used. If a calculated EM field distribution is perturbed by a small change in the simulation domain, the presented method allows for an unprecedented calculation speed of the new field distribution, without the need for an extensive offline precomputation stage.First, the proposed calculation method is presented. In Fig. 31 that describes a perturbation of the RF fields as a result of a medical implant, given byf and N, where N is the number of edges inside the simulation domain, when the implant is and is not present respectively. I is the identity matrix of size M by M, where M is the number of edges the implant occupies inside the discretized simulation domain (i.e. the size of the implant). The electric properties and the location of the implant are defined by M by M) and M by N) respectively. The last matrix, Z, is the so-called library matrix that by definition contains the RF field distributions of a source, z, inside the library matrix corresponds to a source located at a single edge indicated by the column of the support matrix S according toN elements long. The original method prescribes computing this library matrix beforehand by use of numerical simulations which results in unmanageable memory requirements and precomputation times, as mentioned in the introduction. In addition, the resulting matrix Z is patient anatomy-, discretization-, and frequency-specific.In this work, we will start from the equation given inx for which we minimize represents the scattered current density as a result of the introduced implant. Equation . The generated electric field  is given byx. Here diag(x) creates a matrix of size M by M with x on the main diagonal. This results in Zx without explicitly having to calculate Z. Now the function that replaces Zx for the matrix-free minimization is given byZ. A schematic visualization of the proposed innovation to the prior method is shown in Fig. To tackle the issues of memory constraints and precomputation times, we propose an alternative procedure that circumvents the requirement of the library matrix entirely. This is achieved by solving the matrix inversion in Eq.  using a entclass1pt{minimaTo verify the correctness of the proposed method, we compare the resulting RF field distributions with those obtained from a traditional FDTD simulation. Four different medical implants are included, where we vary the patient anatomy, implant location, discretization, and the MRI field strength/frequency.The first implant geometry that we evaluate is a vascular stent placed in the carotid artery where the patient model is placed inside the MRI environment for a head scan at 3 T (128 MHz). The setup and the resulting maximum intensity projections (MIP) of the local SAR distribution can be observed in Fig. The next implant for which we test our method is an orthopedic implant placed to hold together the ulna after a fracture. Resulting SAR distributions for a scan at 1.5 T 64 MHz) are shown in Fig.  MHz are The third implant setup that we simulated is a double hip implant at 1.5 T 64 MHz) for which the setup and MIPs of the SAR distributions are shown in Fig. 4 MHz forWhile the presented method has been developed for the RF safety assessment of medical implants in combination with MRI, it is not limited to this field of research. In fact, Eq.  can be sM is the length of the solution, in our case, this is the number of edges for which the dielectric is changed (i.e. the size of the implant).For the investigated cases, the proposed method is orders of magnitude faster, as depicted in Fig. M/x-axis in the plot the FDTD method will be faster than our proposed method.As a result, in Fig. In this work, we have shown that it is possible to significantly accelerate the calculation of the RF field enhancement that is associated with a medical implant in MRI, without extensive precomputations or excessive storage requirements. This is achieved by using a matrix-free minimization scheme where we use a surrogate function based on the VIE method to replace the intractable so-called library matrix. The local SAR distributions that are obtained using the proposed method are in good agreement with those acquired with a traditionally used FDTD solver.The acceleration of the proposed method compared to FDTD is obtained by regarding the implant as a perturbation to the original system where the RF field distribution is known. As such, only the effect of the implant on the RF field distributions is calculated.S. Using this information the SAR near the implant can be calculated for multiple body models and positions of the implant to ensure the SAR increase is not underestimated. Moreover, as a result of the acquired acceleration, we believe that the proposed method is fast enough to enable RF safety assessment online at the MRI scanner. Finally, another application of the proposed method is the so-called Tier 4 RF safety assessment described in the ISO/TS 10794. The accelerated fashion in which the RF field distributions can be calculated will significantly impact the time required for a Tier 4 simulation study.Using the proposed method, it would be possible to perform patient-specific RF safety assessment for patients with MR conditional implants. The MR conditional label assumes a worst-case scenario which for most MRI examinations would be exceedingly conservative. From the proposed method patient-specific scanning restrictions can be derived to ensure safety while also remaining efficient with the MRI scan time. Furthermore, this method could also be used to assess the RF safety for patients with non-labeled implants. This would allow this patient group to be eligible for MRI examination again, given that the implant is MRI safe on all other aspects. We envision this by using a library of human models and obtaining the implant location from X-ray images acquired prior to the MRI exam and using that information to construct the support matrix M) are dispersed around the simulation domain (N). When the updates inside the domain are dispersed it could be more efficient to apply the proposed method multiple times and apply the update in steps. As an example, the double hip implant update could also be performed in two steps, first the left hip implant and afterward the right hip implant. This would however ignore the interaction that these implants may have on each other.The acceleration that is found in Fig. 35. While the VIE method has been adapted to closely resemble the FDTD method, by having the same Yee cell discretization and dielectric averaging, the method for solving the RF fields is still different and could explain the found underestimation. As for the distribution of the error, only 0.5% of the voxels inside the calculated domain had a larger underestimation than 5% with a maximum of 18% underestimation, this can also be observed in Fig. From Figs. 31 to no longer require the offline stage, and is now completely online. This entails that given an electromagnetic field without an implant present the effect of the implant on the electromagnetic field can be calculated. The proposed method is demonstrated for multiple implants where we show that the calculated RF fields are comparable to the RF fields obtained with FDTD simulations. Using the proposed method we obtain a speed up between 55 and 438 times. Therefore, the proposed method could be used for patient-specific RF safety assessment or an ISO/TS 10974 Tier 4 approach.In this work, we have reformulated the update method as described in36 and predominantly uses the existing software packages: CUDA.jl37 and IterativeSolvers.jl. The software has been developed into a new package that can be used by others, details are in the code availability section.The proposed method has been implemented in the Julia programming languageTo find the solution to Eq.  we use ag in Eq. .To benchmark our method we use the commercial FDTD package Sim4Life v5.0 . Furthermore, the body models of Ella and Duke from the virtual family  are used.For each comparison, we simulated a setup without and with the medical implant present. The simulation without the medical implant served as the input RF field distribution for our proposed method, while the simulation with the implant was used to compare the RF field distributions. The medical implants were modeled after standard implant geometries using XCoreModelling within the Sim4Life environment. The metal parts in all implants are modeled as titanium.In the first comparison, the stent was placed in the carotid artery of the Ella model. The stent is 22 mm long and has a diameter of 5 mm. The thickness of the wires that make up the stent is 0.5 mm. The birdcage head coil that is used has a 155 mm radius, a leg length of 168 mm, a leg width of 20 mm, and the endrings are 2.5 mm wide. The RF shield around the coil has a 190 mm radius and a length of 173 mm. The stent was discretized using a 0.25 mm isotropic resolution and the rest of the setup was automatically discretized by Sim4Life for a total of 15.5 million voxels inside the simulation domain.For the second comparison, the plate with screws is placed unto the ulna of the Ella model, with the screws going through the bone. The length of the plate is 100 mm long, 4 mm wide, and has a thickness of 2 mm. The screws that are placed inside have a diameter of 3 mm and a length outside of the plate of 9 mm. The birdcage body coil that is used has a radius of 352 mm and a length of 420 mm. The width of the legs is 40 mm while the endrings are 80 mm wide. The RF shield around the coil has a radius of 371.5 mm and a length of 700 mm. The implant is discretized on a 1 by 1 by In the third comparison, the two hip implants have a length of 200 mm, a thickness of 8 mm, a width of 30 mm, and the ball has a radius of 30 mm. The two hip implants are identical in size and are placed such that they replace the hip joint inside the body model of Duke. The birdcage body coil in these simulations is identical to the birdcage body coil used for the orthopedic implant placed unto the ulna of Ella. The simulations setup was discretized using 12 million voxels with the hip implants being discretized on a 3 mm isotropic grid.39. The lead is insulated with a plastic material that has zero conductivity and a relative permittivity of 3. One end of the implanted lead is attached to a casing that is 6 by 6 by The last implant that is investigated is a cochlear implant. The lead of the cochlear implant has 12 electrodes, a diameter of 1.5 mm, and is 31 mm longAll the FDTD simulations are terminated at a convergence level of −50 dB and run on an NVIDIA GTX Titan Black. The proposed method uses GPU acceleration for the solver of Eq.  running i is the imaginary unit, D is the discretized version of the curl operators, and q contains the source current density (f our equation becomesS we can define the significantly smaller matrix N by N to M by M.Z, we arrive at f is calculated from the unperturbed RF field distributions For the sake of completeness, here we provide the full details of the original method. A visual overview of the original method can be seen in Fig. mentclasspt{minimax to Eq. (H and f.When changing the conductivity and permittivity within the simulation domain to include the medical implant, the library matrix only requires the electric field responses of single-source current densities within the patient anatomy. For other applications, using the same formalism and equations as above the magnetic permeability can also be updated in case if the magnetic field responses are required. This could for example be used to calculate the magnetic field distortion around the implant. For this purpose, the magnetic field responses would need to be included within the library matrix after the current density distribution is obtained from the matrix inverse in Eq. . Similarx to Eq.  is obtai


In many situations, humans make decisions based on serially sampled information through the observation of visual stimuli. To quantify the critical information used by the observer in such dynamic decision making, we here applied a classification image (CI) analysis locked to the observer's reaction time (RT) in a simple detection task for a luminance target that gradually appeared in dynamic noise. We found that the response-locked CI shows a spatiotemporally biphasic weighting profile that peaked about 300 ms before the response, but this profile substantially varied depending on RT; positive weights dominated at short RTs and negative weights at long RTs. We show that these diverse results are explained by a simple perceptual decision mechanism that accumulates the output of the perceptual process as modelled by a spatiotemporal contrast detector. We discuss possible applications and the limitations of the response-locked CI analysis. In cognitive psychology, such dynamic information processing has been investigated mainly by measuring the reaction times and correct rates of observers. However, the reaction time alone is not powerful enough to reveal what kind of information in the stimuli led the observers to make a decision at that moment in time, unless data obtained under various conditions are compared.While humans and animals can immediately recognize objects and scenes at a glance8. This analysis has been applied not only to the responses of cortical neurons9, but also to the analysis of the behavioral responses of human observers11. The classification image (CI) method, one such technique, visualizes what information in the stimuli observers consider important for a given perceptual judgement14. In typical experiments, the observer's responses to a visual target embedded in white noise are collected, and the information in the stimulus that affected the observer's response is mapped out by analyzing the correlation between the noise and the response in each trial. The CI technique has been used to reveal the spatial distribution of information, or perceptive field, that determines the observer's judgments for a variety of visual tasks17.In visual neuroscience, reverse correlation analysis is widely applied to reveal the information in stimuli that determines the system responses20. Neri and Heeger18 analyzed the correlation between spatiotemporal noise and responses in each trial in a contrast detection task for luminance bars that slowly appear in dynamic noise. They found CI profiles with biphasic weights in time and space, similar to the spatiotemporal impulse response of the early visual system. Recently, similar psychophysical reverse correlation with dynamic stimuli has been applied to the judgement on the average of time-varying visual information to investigate the mechanisms of perceptual decision making28.With dynamic stimuli, the CI method can also yield spatiotemporal perceptive fields31. For example, Caspi et al.30 examined visual features that trigger saccadic eye movements by analyzing the noise at time points locked to the onset of the saccade while the observers views a multi-element display. Okazawa et al.31 adopted a reverse correlation analysis locked to button-press responses to stochastic motion to explore the properties of global-motion detectors and decision-making mechanisms.In the aforementioned studies, however, observers made decisions after the visual stimuli had been shown. Such a judgment, which is usually based on visual working memory, may be different from on-the-fly judgments that we make in the real world. To clarify when observers make decisions and what information observers rely on to make decisions during observation, one can analyze correlations at each time point of the stimulus locked to the reaction time of the observer during the presentation of the stimulus rather than the stimulus onset. This response-locked reverse correlation has been employed in several studies18 to measure responses and reaction times for target stimuli that emerge slowly in dynamic noise, and we then analyzed the correlation between the noise and response at each time point backward, locked to the observer's reaction time. This protocol allowed us to examine what signals and what point in the stimulus determined the observer's decision about the target and the observer’s reaction time. The results revealed spatiotemporally biphasic CIs similar to those reported by Neri and Heeger18. On the other hand, we also found that the profile of the CI substantially varied depending on the response time of the observer in a way that was unpredictable from the response properties of the early visual system. These apparently complicated results, however, were quantitatively described by a simple computational model incorporating a perceptual process approximated by a spatiotemporal filter and a decision process (drift–diffusion) that accumulates its output43.In the present study, we applied the response-locked CI analysis to the most basic visual task, luminance contrast detection. Specifically, we used stimuli similar to those used by Neri and HeegerFive naïves and two of the authors (average age: 22.8 years) with corrected-to-normal vision participated in the experiment. All experiments were conducted with permission from the Ethics Committee of the University of Tokyo. Observers gave written informed consent. The study followed the Declaration of Helsinki guidelines.2. All experiments were conducted in a dark room.Visual stimuli were displayed on a gamma-corrected LCD monitor (BENQ XL2735) controlled by a PC. The refresh rate was 60 Hz, and the pixel resolution was 0.04 deg/pixel at the viewing distance of 50 cm that we used. The mean luminance of the uniform background was 88.9 cd/mThe visual stimulus was square dynamic one-dimensional noise (4.8 × 4.8 deg) comprising 16 vertical bars with a width of 0.3 deg  from stimulus onset. α is the rate of increase, which was set at three levels: 0.05, 0.1, and 0.2. The contrast of each bar was clipped in the range of − 1 to + 1. The two fields, with and without the target signal, were transformed into luminance images using the relation L(t) = Lmean (1 + C(t)), where Lmean is the mean luminance of the uniform background (88.9 cd/m2).Here, C noise), the observer’s response , and the reaction time were recorded. Each session of the experiment comprised 150 trials for a single condition. For each observer, sessions were repeated until at least 1200 trials were conducted for each condition.In each trial, observers viewed the stimulus at a fixation point binocularly and indicated by pressing a button whether the target appeared in the left or right noise field as quickly as possible. If an observer’s response exceeded the deadline (8000 ms) or was an error, auditory feedback was given, and the data recorded in that trial were excluded from the analyses. The next trial started no less than 0.5 s after the observer’s response. The average error rates were 0.03, 0.02, and 0.02 for contrast increases (α) of 0.05, 0.1, and 0.2, respectively. In each trial, the contrast values of all individual bars  back from the reaction time to characterize the noise common to the time before the reaction. Figure 18, μ1 is the mean of the noise contrast in the region where the observer responded that the target was present and μ0 is the mean of the noise contrast in the region where the observer responded that the target was not present. Since the reaction time varied across trials, the number of trials used to calculate the mean at each time point was not constant and tended to decrease as time increased backward from the response. The results were calculated as follows.We conducted a reverse correlation analysis between the contrast of each bar and the observer's response  at each time (t) back from the reaction time, and the vertical axis represents the spatial position of each bar (x). In comparison with the grey background, the brighter points represent positive weights and the darker points represent negative weights. Individual panels show results for a contrast increase (α) of 0.05, 0.1, or 0.2. The lower panels show the mean of the weights of the two central bars (red) and the mean of the weights of the two adjacent bars (blue) in the CI. The vertical axis represents the weight and the horizontal axis represents the time from the reaction time. We refer to the plots as impact curves.The upper panels in Fig. 44, but found no clear CI profile. This discrepancy could be due to the fact that the target appeared gradually in the present study, whereas the target was abruptly flashed in the previous study.The above plots show characteristic temporal and spatial variations in the weights before the target detection response. At the center of the stimulus where the target appeared, a large positive weight was found about 300 ms before the response, and a negative weight was found about 500 ms before the response. For the spatial variation, we find that the weights around the target are reversed from the center. This means that the decrease in luminance in the bars adjacent to the target are also useful for detecting the target. It is also found that the absolute magnitude of the weights tends to decrease as the contrast increase rate increases. We also conducted the same analysis for contrast variance, as was done in a previous studyAs shown in Fig. Figure The present study examined the information utilization strategy adopted in dynamic decision making during stimulus observation in a simple contrast detection task. Applying the classification image method, we calculated the weights of the embedded noise at each time point retrospectively from the reaction time for the target. The resulting CIs indicate that observers responded by utilizing the biphasic luminance change and the central antagonistic spatial contrast before the response. In addition, we found that these spatiotemporal profiles of CIs varied significantly depending on the reaction time.The complex diversity of the results depending on the reaction time appears to be difficult to understand intuitively. This may seem to indicate that the observers were so flexible that they use different strategies for utilizing information depending on whether they could respond quickly to the target or not. However, we should first consider a simple explanation—that the results are a natural consequence of an interplay between the sensory system and the decision process. Therefore, we tested a simple model consisting of the early visual process (linear filtering model) and the perceptual decision process (drift–diffusion model). As a result, we found that this conservative model with a fixed set of parameters successfully duplicated the human data for all conditions and RT ranges.28). The model compares the spatially summarized outputs of the perceptual process, which is approximated using linear spatiotemporal filters, between the two regions. The decision process accumulates the differential signal between the two regions as sensory evidence over time and makes a decision when the evidence reaches a given boundary. The basic structure of the perceptual process follows that of a previous CI study (Neri and Hereger18), and the computation of the decision making follows traditional drift–diffusion model (DDM) for a two-alternative forced-choice task35. Figure Figure 18, the perceptual system is approximated as a space–time separable linear filter, Fst, as follows.Following previous studiesFs(x) is the spatial filter and Ft(t) is the temporal filter. The spatial filter Fs(x) is given as a DoG function, which has been widely used as a first-order approximation for contrast detectors in the visual system36 (Enroth-Cugell and Robson 1966). Note that t is the frame number (33 ms per unit) and x is the pixel (0.04 deg per unit).Here σc is the standard deviation for the central region and σs is the standard deviation for the adjacent region. The temporal filter Ft(t) is given as the following biphasic function38.Here n is the number of stages in the time integrator, t is the transient factor, and B is a parameter that defines the amplitude ratio of the positive and negative phases.Here Fst with the stimulus input I.The response of the perceptual system was obtained by convolving the above spatiotemporal filter ΔR(t) between the left and right responses at time t from the stimulus onset. Here, ΔR(t) is regarded as the sensory evidence at time t in the decision-making model.Decisions concerning whether the target presented in the left or right region were made by comparing the spatial sum of the absolute values of the responses in each region between the left and right. Thus, the model observer continually monitored the difference 40. This property is practically described as a leaky temporal integration, and it is potentially a product of the adaptive gain control of evidence signals42. According to these findings, the present modeling assumes that the cumulative evidence S(T) at time T is given by the following equation, which approximates the noisy leaky integration of ΔR(t).Decisions for targets are based on evidence accumulated over time. However, a number of decision-making studies suggest that sensory evidence decays with time; that is, the evidence weakens as it agesg is the time constant of evidence integration and εt is the internal noise following a normal distribution. The model observer makes a decision about whether the target is on the left or right when S(T) exceeds a certain decision boundary, that is, b or − b, respectively. The observer was assumed to execute a manual response after a constant motor delay of 250 ms from T.Here σc and σs, in pixels), number of biphasic temporal filter integration stages (n), time constant , and ratio of positive to negative phases (B). The decision-making process part has three parameters: the decision boundary (b), internal noise (εt), and time constant for evidence reduction .In this modeling, the perceptual process part has five parameters: the standard deviations of the spatial filter (n) was fixed to 5, for all model observers.We analyzed the CI and impact curves of the model observer using the image input data that were presented to each observer in the experiment. In the simulations, for all data in the condition of α = 0.05, the model parameters were optimized for each observer to minimize the squared error between the impact curve obtained for the model observer and that of the human observer. To achieve the steady fitting, only the number of integration steps of the biphasic temporal filter . For all values of the contrast increase (α), we find that the model successfully duplicated both the CI and the impact curve of the observers. For the three different reaction time groups (Fig. Figure 43.To investigate the importance of the functional processes assumed in the model in Fig. The present results support the idea that on-the-view behavioral responses to visual stimuli can be explained by a simple combination of the conventional perceptual model and the standard perceptual decision-making model. This finding may allow us to perform a response-locked reverse correlation analysis of human responses to sensory stimuli during observation, rather than after observation, to explore the characteristics and strategies of human information use in various cognitive tasks. In further investigations, a similar framework may be used to understand the mechanisms for attentional selection and for high-level visual cognition. The present computational model can be used as a baseline account in these investigations.31. Although one can partially overcome this limitation by making full use of various aspects of data, such as by dividing the data into different RT ranges as in the present study, it is difficult to distinguish between some properties such as the latency of the perceptual sensors and the motor delay in the decision process.It should be noted that psychophysical analysis cannot reliably separate the properties of decision making from the low-level perceptual process


To implement and validate a beam current transformer as a passive monitoring device on a pulsed electron beam medical linear accelerator (LINAC) for ultra‐high dose rate (UHDR) irradiations in the operational range of at least 3 Gy to improve dosimetric procedures currently in use for FLASH radiotherapy (FLASH‐RT) studies.Two beam current transformers (BCTs) were placed at the exit of a medical LINAC capable of UHDR irradiations. The BCTs were validated as monitoring devices by verifying beam parameters consistency between nominal values and measured values, determining the relationship between the charge measured and the absorbed dose, and checking the short‐ and long‐term stability of the charge‐absorbed dose ratio.The beam parameters measured by the BCTs coincide with the nominal values. The charge‐dose relationship was found to be linear and independent of pulse width and frequency. Short‐ and long‐term stabilities were measured to be within acceptable limits.The BCTs were implemented and validated on a pulsed electron beam medical LINAC, thus improving current dosimetric procedures and allowing for a more complete analysis of beam characteristics. BCTs were shown to be a valid method for beam monitoring for UHDR (and therefore FLASH) experiments. It delivers pulsed electron beams from 6 to 12 MeV . The device used in this study was modified to be able to deliver UHDR beams. Its possible configurations were one CONV mode at 9 MeV and two UHDR modes at 6 MeV and 9 MeV . In CONV mode, the pulse width (PW) and pulse repetition frequency (PRF) were fixed at 1.2 μs and 30 Hz respectively. In UHDR modes, the user could set the desired number of pulses between 1 and 200, the PW between 0.5 and 4 μs, and the PRF between 5 and 90 Hz. The commissioning of the modified Mobetron for FLASH‐RT preclinical biological experiments as well as FLASH‐RT clinical human protocols is described elsewhere.2.2The BCTs used on the Mobetron are AC current trasnsformers (ACCTs) from Bergoz , which are toroid sensors connected to their own power supply and external electronic system.The signal from the ACCT was acquired with a PXIe‐1071 oscilloscope with a NI PXIe‐5114 card  and then displayed and recorded with a LabVIEW 2014 VI software . After the acquisition, signal processing was done by applying a 20 MHz low‐pass filter, to remove random high‐frequency noise,In order to relate the ACCT signal to dose in a given condition, a charge‐dose relationship was measured in all modes. In the CONV mode, the dose was measured with an Advanced Markus ionization chamber from PTW  connected to an Unidos electrometer . For UHDR modes, EBT3 GafChromic films , which have been proven to be efficient in UHDR mode due to a dose range of 0.1‐20 Gy and a 2% uncertainty independent of dose rate,The Advanced Markus chamber was also used for the stability checks in all modes. The issue arising from chamber saturation2.3The comparison of the nominal beam parameters  with the respective parameters read by the ACCTs was done for all modes.The CONV mode on the Mobetron has a fixed PW of 1.2 μs and a PRF of 30 Hz. Therefore, in order to check the beam parameters of this mode, ten measurements for 180 pulses were done and the PW, PRF, and number of pulses read by the ACCT were averaged and compared to the nominal values.For each UHDR energy, one measurement of two pulses in each possible configuration of PW and PRF was done (10 PW × 8 PRF = 80 configurations per energy). The parameters read by the LabVIEW were recorded for each measurement and compared with the nominal parameters. To get the average PW read by the ACCT, the ten individual PW were averaged over each PRF. Likewise, to get the average PRF, the eight individual values are averaged over each PW. The correspondence of the number of pulses was obtained with ten measurements of 100 pulses with a PW of 4 μs and a PRF of 60 Hz for each energy. The number of pulses read by the ACCT was then averaged and compared to the nominal value.2.4The ACCT is of interest as a beam monitoring device if the charge read can be converted into dose. This conversion is created similarly to conventional methods, where the transmission chamber data is related to the absorbed dose to water in a reference setup.The reference setup chosen here was an open field at an SSD of 0.5 m and at 1 cm depth in solid water, which corresponds to 1–2 Gy per pulse for UHDR modes. For each measurement, one film was placed at this reference condition and the charge produced by the irradiation and read by the ACCT was recorded. The number of pulses (or the number of MU) given by the Mobetron was slowly increased in order to cover a wide range of charges/doses for UHDR modes (respectively CONV mode). The charge‐to‐dose relationship was compared for the full range of PW and PRF. Again, as the PW and PRF for 9 MeV CONV are fixed, only measurements for 1.2 μs and 30 Hz were done for this mode.2.5In order to check the short‐ and long‐term stabilities of the charge‐dose ratios of the ACCTs, the Advanced Markus chamber was placed at the reference condition. Two pulses were given for the UHDR modes  and 100 MU for the CONV mode. Five measurements of the ratio of the Markus chamber charge (corrected for temperature and pressure) over the ACCT charge were done per day. These measurements were repeated for 8 days over the course of 3 months. The short‐term stability was calculated as the average standard deviation of the five daily ratios. The long‐term stability was calculated as the standard deviation of the daily averages of the ratios.33.1The pulses as current in function of time recorded by the LabVIEW VI are presented in Figure 3.2Table Figure 3.3The charge‐dose relationship for the CONV mode is shown in Figure 3.4The results of the stability check of the charge‐dose ratio are displayed in Table 4The validity of the use of BCTs as a beam monitoring device on a clinical UHDR electron beam LINAC was checked by verifying beam parameter consistency, developing a charge‐absorbed dose relationship, and checking short‐ and long‐term stabilities.The beam parameters measured by the ACCTs coincide suitably with the nominal values. A slight deviation from the nominal value was observed as the PW tends to 4 μs. This is due to physical limitation of the Mobetron's UHDR modes, as large PW and high PRF push the limit of the modulator. The charge‐dose relationship was linear and independent of PW and PRF. Even for noisy and low‐amplitude signals acquired in the CONV mode, the charge‐dose relationship for the Mobetron is remarkably linear. The differences from the fit show the possibility of an accurate estimation of dose. The short‐ and long‐term stabilities are both acceptable. The short‐term stability measured with the film is within the uncertainties of the dose measured by the film. The actual dose delivered can be estimated in the short‐term with a precision that depends on the short‐term stability and the difference from the fit. Combining these two uncertainties, the dose in the expected operational range (> 3 Gy) can be estimated with an average precision of 0.56% for CONV and 2.41% and 2.62% for 6 MeV and 9 MeV in short‐term. All of these individual points, along with the non‐destructive aspect of the ACCTs, support their validation as a beam monitoring device for UHDR electron beam irradiations. The drawback of using ACCTs is their inability to give information on beam shape and spatial distribution, as the only thing that is measured is the total flux of electrons going through the ACCT. Thus, certain operating parameters that can be monitored with conventional ionization chambers and are necessary for clinical use, such as beam energy, flatness, beam size, and symmery,At the moment, the only other LINAC on which ACCTs were implemented and validated is the Oriatron eRT6.Future integration of an ACCT in the head of the Mobetron would allow for easier use and for a more clinical environment. The next step in improving the use of ACCTs as beam monitoring would be to implement a beam‐stopping electronic‐based mechanism for total beam monitoring and a way to resolve the issues related to the inability to measure the beam spatial distribution. One possible solution for the latter could be to implement screens.5The use of ACCTs as a beam monitoring for UHDR on a medical device has been demonstrated. Various beam characteristics can be measured in real time. The additional advantage of being able to calculate the absorbed dose by using a single relationship depending on the measured charge, regardless of PW or PRF used, supports the use of ACCTs for UHDR electron LINACs.The study was partly supported by IntraOp Medical corporation, by Fondation pour le soutien de la recherche et du développement de l'oncologie (FSRDO), a Synergia grant from the FNS CRS II5_186369, a grant from ISREC Foundation thank to Biltema donation, a grant from the EMPIR program of the European Union Horizon 2020 research and innovation program, and a NIH program project grant PO1CA244091. The authors would like to acknowledge Derek Descioli, Kenneth Brooks, Sebastian Adamczyk, James Nelson, and Marc Swallyee of IntraOp Medical corporation for their technical support.All authors have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data, have been involved in drafting the manuscript or revising it critically for important intellectual content, have given final approval of the version to be published, have participated sufficiently in the work to take public responsibility for appropriate portions of the content and have agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.6The study was partially funded by IntraOp Medical corporation.


Wing area, wing loading, and aspect ratio are key variables for studies of avian comparative ecology, despite the complexity of measuring wing characteristics in living and museum specimens. The systematic databases of feather photographs available on the Internet may offer an alternative way of obtaining such morphometric data. Here, we evaluate whether measurements of scanned feathers from web photograph databases may offer reliable estimates of avian morphometry.Published data on wing area were obtained for 317 bird species and feather measurements from web photograph databases for 225 of them. A variable termed “lift generation area,” a proxy for wing area, was calculated for each species on the basis of the mean length of the five distal secondary feathers and wingspan data from literature. The fit between this proposed variable and data extracted from the literature was examined by correlation, employing linear regression to explore the lack of fit among species.R2 > .98). Discrepancies observed between species were strongly negatively associated with the size of the original sample used to calculate wing area (p = .001) and, to a lesser extent, with bird size (p = .023), but not with aspect ratio. It was also found that the mean value of the mismatch between “lift generation area” and wing area (13.1%) among the study species as a whole was of similar magnitude to that found between sources of bibliographic wing area data for the 64 species for which two published estimates of this variable were available (15.3%).“Lift generation area” proved to be highly informative as a proxy for wing area for the study species as a whole . We conclude that measurements made from feather photograph databases are reliable for use in studies of avian comparative ecology.Wing area and aspect ratio are key variables for studies of avian comparative ecology, despite the complexity of measuring them both in living and in museum specimens. The systematic databases of feather photographs available on the Internet may offer an alternative way of obtaining such morphometric data. Published data on wing area were obtained for 317 bird species, and feather measurements from web photograph databases were found for 225 of them. A variable termed “lift generation area,” a proxy for wing area, was calculated for each species. “Lift generation area” proved to be highly informative as a proxy for wing area for the study species as a whole ( A sample size of 1 (in = 1) was used where the original sample size was not given but this was only included in calculating an average if in = 1 was the case in all sources for the species.Wing area data were extracted from the literature, with special emphasis on reviews of multiple bird taxa and, in all cases, on data including the interwing body area, in accordance with Pennycuick . The inib is wingspan, and 1LF to 5LF are the length of secondary feathers 1 to 5. The length of each of the 5 secondaries corresponds in turn to the average of measurements from several individuals. This is included in the Featherbase (b) data extracted from general compilations .The lift generation area for each species was then compared with published measurements of wing areas Figure  by PearsR2) in measured wing area explained by the lift generation area variable obtained from the databases. We also determined whether discrepancies between the values of lift generation area and wing area varied systematically in relation to bird size (mass), wing morphology as measured by aspect ratio , size of the smallest sample (minimum of in1 and in2), and species size (AvgWAi), given that measuring larger species may involve less error. These correlations were calculated with natural log‐transformed sample sizes and arcsin‐transformed percentages, although the text figures show untransformed data to aid comprehension. Data from sources where the sample size (in) was not given were excluded from this analysis.At the same time, with a view to establishing the intrinsic variability of traditional wing area measurements, the variation in wing areas between bibliographic sources was analyzed for those species for which two independent data sources were available. This was done by calculating the percentage difference between the published measurements (WAdif) as:p < .05.Statistical analyses used STATISTICA 8.0 , despite being drawn from various sources  of the former, enabling their lift generation areas to be calculated and comparisons to be made  or natural log‐transformed .The wing areas compiled here correspond to 317 species. Most are based on measurements of 1–6 individuals  and with bird size , but not with aspect ratio . The stepwise regression model included only the variables sample size  and bird mass  as explicative of the observed discrepancies.The simple regression model of lift generation area as a function of wing area was highly significant, the absolute value of its residuals being equivalent to 13.1 ± 11.0% of the predicted value. Discrepancies among species were highly variable (range 0.1%–86.7%), and they were negatively correlated with the number of samples used for the wing area estimate . The variability observed within published data increases slightly with bird size and is lower for larger samples , with total size of the original sample , or with the size of the smallest sample .The wing area analyses for the 64 species with two independent records in the literature showed that measurements of this variable on live birds or museum specimens have high intrinsic variability . There are also species in which the transition between the wing and the body is very gradual as a result of having very long proximal secondaries, which may result in difficulties in deciding the exact position of the wingbase, leading to high variation in estimating the interwing area below the body , although a large positive discrepancy has been found in others that also have similar proximal secondaries. . In fact, a complementary analysis carried out at the Order level showed that the explanatory power of lift generation area for wing area was high in all cases (R2 in the range .938–.999).The reliability of using lift generation area as a proxy for wing area varies between species, although the differences encountered are largely associated with measurement difficulties rather than with any systematic tendencies in the morphology of particular bird groups. The mean discrepancy between both variables is 13%, very similar to that found between independent measurements of wing areas of live birds or museum specimens . It is smaller in cases where multiple wing area measurements are available and also for the larger birds considered. A review of species for which larger discrepancies between the two variables were found revealed some exceptional cases, such as the Hawfinch Apus spp., Ardeaalba), and (b) average discrepancies across species of individual secondary feather lengths with respect to the s1–s5 average reach a minimum for s3 . However, the difference in length among s1–s5 secondaries for some species may reach up to 20%–30%, as it happens in some Galliformes with a small s1 feather . Since this study covers a limited number of species, it seems advisable to use the average of s1–s5 until we have a deeper knowledge of patterns of variability in secondaries among bird species. The use of s3 would be a conservative option in cases where it is not possible to measure several feathers, though the observed differences with the average were in the range between −3.3% and +5.8%.From all this, it emerges that the length of the distal secondaries is a good proxy for mean wing chord across species, although it may be less precise in certain cases. The decision to use the average of five secondaries was taken at the beginning of the study after a first review of scans from several species, and the analysis of collected data (not presented in Section 3) shows that: (a) secondary feather length decreases from s1 to s5 in most species, but there are several counterexamples ; Data curation ; Formal analysis ; Methodology ; Writing‐original draft ; Writing‐review & editing . Cristina Mata: Data curation ; Formal analysis ; Methodology ; Writing‐original draft ; Writing‐review & editing .Appendix S1Click here for additional data file.