Optimal allocation of resources among general and species-specific tools for plant pest biosecurity surveillance.

This paper proposes a surveillance model for plant pests that can optimally allocate resources among survey tools with varying properties. While some survey tools are highly specific for the detection of a single pest species, others are more generalized. There is considerable variation in the cost and sensitivity of these tools, but there are no guidelines or frameworks for identifying which tools are most cost-effective when used in surveillance programs that target the detection of newly invaded populations. To address this gap, we applied our model to design a trapping surveillance program in New Zealand for bark- and wood-boring insects, some of the most serious forest pests worldwide. Our findings show that exclusively utilizing generalized traps (GTs) proves to be highly cost-effective across a wide range of scenarios, particularly when they are capable of capturing all pest species. Implementing surveillance programs that only employ specialized traps (ST) is cost-effective only when these traps can detect highly damaging pests. However, even in such cases, they significantly lag in cost-effectiveness compared to GT-only programs due to their restricted coverage. When both GTs and STs are used in an integrated surveillance program, the total expected cost (TEC) generally diminishes when compared to programs relying on a single type of trap. However, this relative reduction in TEC is only marginally larger than that achieved with GT-only programs, as long as highly damaging species can be detected by GTs. The proportion of STs among the optimal required traps fluctuates based on several factors, including the relative pricing of GTs and STs, pest arrival rates, potential damage, and, more prominently, the coverage capacity of GTs. Our analysis suggests that deploying GTs extensively across landscapes appears to be more cost-effective in areas with either very high or very low levels of relative risk density, potential damage, and arrival rate. Finally, STs are less likely to be required when the pests that are detected by those tools have a higher likelihood of successful eradication because delaying detection becomes less costly for these species.

Quantitative validation of Monte Carlo SPECT simulation: application to a Mediso AnyScan GATE simulation.

BACKGROUND: Monte Carlo (MC) simulations are used in nuclear medicine imaging as they provide unparalleled insight into processes that are not directly experimentally measurable, such as scatter and attenuation in an acquisition. Whilst MC is often used to provide a 'ground-truth', this is only the case if the simulation is fully validated against experimental data. This work presents a quantitative validation for a MC simulation of a single-photon emission computed tomography (SPECT) system. METHODS: An MC simulation model of the Mediso AnyScan SCP SPECT system installed at the UK National Physical Laboratory was developed in the GATE (Geant4 Application for Tomographic Emission) toolkit. Components of the detector head and two collimator configurations were modelled according to technical specifications and physical measurements. Experimental detection efficiency measurements were collected for a range of energies, permitting an energy-dependent intrinsic camera efficiency correction function to be determined and applied to the simulation on an event-by-event basis. Experimental data were collected in a range of geometries with [Formula: see text]Tc for comparison to simulation. The procedure was then repeated with [Formula: see text]Lu to determine how the validation extended to another isotope and set of collimators. RESULTS: The simulation's spatial resolution, sensitivity, energy spectra and the projection images were compared with experimental measurements. The simulation and experimental uncertainties were determined and propagated to all calculations, permitting the quantitative agreement between simulated and experimental SPECT acquisitions to be determined. Statistical agreement was seen in sinograms and projection images of both [Formula: see text]Tc and [Formula: see text]Lu data. Average simulated and experimental sensitivity ratios of ([Formula: see text]) were seen for emission and scatter windows of [Formula: see text]Tc, and ([Formula: see text]) and ([Formula: see text]) for the 113 and 208 keV emissions of [Formula: see text]Lu, respectively. CONCLUSIONS: MC simulations will always be an approximation of a physical system and the level of agreement should be assessed. A validation method is presented to quantify the level of agreement between a simulation model and a physical SPECT system.

Approaches for estimating benefits and costs of interventions in plant biosecurity across invasion phases.

Nonnative plant pests cause billions of dollars in damages. It is critical to prevent or reduce these losses by intervening at various stages of the invasion process, including pathway risk management (to prevent pest arrival), surveillance and eradication (to counter establishment), and management of established pests (to limit damages). Quantifying benefits and costs of these interventions is important to justify and prioritize investments and to inform biosecurity policy. However, approaches for these estimations differ in (1) the assumed relationship between supply, demand, and prices, and (2) the ability to assess different types of direct and indirect costs at invasion stages, for a given arrival or establishment probability. Here we review economic approaches available to estimate benefits and costs of biosecurity interventions to inform the appropriate selection of approaches. In doing so, we complement previous studies and reviews on estimates of damages from invasive species by considering the influence of economic and methodological assumptions. Cost accounting is suitable for rapid decisions, specific impacts, and simple methodological assumptions but fails to account for feedbacks, such as market adjustments, and may overestimate long-term economic impacts. Partial equilibrium models consider changes in consumer and producer surplus due to pest impacts or interventions and can account for feedbacks in affected sectors but require specialized economic models, comprehensive data sets, and estimates of commodity supply and demand curves. More intensive computable general equilibrium models can account for feedbacks across entire economies, including capital and labor, and linkages among these. The two major considerations in choosing an approach are (1) the goals of the analysis (e.g., consideration of a single pest or intervention with a limited range of impacts vs. multiple interventions, pests or sectors), and (2) the resources available for analysis such as knowledge, budget and time.

Automating the assessment of biofouling in images using expert agreement as a gold standard.

Biofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because it increases fuel costs and presents a biosecurity risk by providing a pathway for non-indigenous marine species to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply deep learning to automate the classification of images from in-water inspections to identify the presence and severity of fouling. We combined several datasets to obtain over 10,000 images collected from in-water surveys which were annotated by a group biofouling experts. We compared the annotations from three experts on a 120-sample subset of these images, and found that they showed 89% agreement (95% CI: 87-92%). Subsequent labelling of the whole dataset by one of these experts achieved similar levels of agreement with this group of experts, which we defined as performing at most 5% worse (p [Formula: see text] 0.009-0.054). Using these expert labels, we were able to train a deep learning model that also agreed similarly with the group of experts (p [Formula: see text] 0.001-0.014), demonstrating that automated analysis of biofouling in images is feasible and effective using this method.

OpenDose: Open-Access Resource for Nuclear Medicine Dosimetry.

Radiopharmaceutical dosimetry depends on the localization in space and time of radioactive sources and requires the estimation of the amount of energy emitted by the sources deposited within targets. In particular, when computing resources are not accessible, this task can be performed using precomputed tables of specific absorbed fractions (SAFs) or S values based on dosimetric models. The aim of the OpenDose collaboration is to generate and make freely available a range of dosimetric data and tools. Methods: OpenDose brings together resources and expertise from 18 international teams to produce and compare traceable dosimetric data using 6 of the most popular Monte Carlo codes in radiation transport (EGSnrc/EGS++, FLUKA, GATE, Geant4, MCNP/MCNPX, and PENELOPE). SAFs are uploaded, together with their associated statistical uncertainties, in a relational database. S values are then calculated from monoenergetic SAFs on the basis of the radioisotope decay data presented in International Commission on Radiological Protection Publication 107. Results: The OpenDose collaboration produced SAFs for all source region and target combinations of the 2 International Commission on Radiological Protection Publication 110 adult reference models. SAFs computed from the different Monte Carlo codes were in good agreement at all energies, with SDs below individual statistical uncertainties. Calculated S values were in good agreement with OLINDA/EXM 2.0 (commercial) and IDAC-Dose 2.1 (free) software. A dedicated website (www.opendose.org) has been developed to provide easy and open access to all data. Conclusion: The OpenDose website allows the display and downloading of SAFs and the corresponding S values for 1,252 radionuclides. The OpenDose collaboration, open to new research teams, will extend data production to other dosimetric models and implement new free features, such as online dosimetric tools and patient-specific absorbed dose calculation software, together with educational resources.

Improving molecular radiotherapy dosimetry using anthropomorphic calibration.

The optimised delivery of Molecular Radiotherapy requires individualised calculation of absorbed dose to both targeted lesions and neighbouring healthy tissue. To achieve this, accurate quantification of the activity distribution in the patient by external detection is vital. METHODS: This work extends specific anatomy-related calibration to true organ shapes. A set of patient-specific 3D printed organ inserts based on a diagnostic CT scan was produced, comprising the liver, spleen and both kidneys. The inserts were used to calculate patient-specific calibration factors for 177Lu. These calibration factors were compared with previously reported calibration factors for corresponding organ models based on the Cristy and Eckerman phantom series and for a comparably sized sphere. Monte Carlo calculations of the patient-specific radiation dose were performed for comparison with current clinical dosimetry methods for these data. RESULTS: Patient-specific calibration factors are shown to be dependent on the volume, shape and position of the organ containing activity with a corresponding impact on the calculation of the dose to the patient. The impact of organ morphology on calculated dose is reduced when the dominant contributor to dose is beta particles. This is due to the small range of beta particles in tissue. Overestimations of recovered activity and hence dose of up to 135% are observed. CONCLUSION: For accurate quantification to be performed calibration factors accounting for organ size, shape and position must be used. Such quantification is vital if accurate, patient-specific dosimetry is to be achieved.

The influence of triple energy window scatter correction on activity quantification for (1 7 7)Lu molecular radiotherapy.

Accurate activity quantification is the foundation for all methods of radiation dosimetry for molecular radiotherapy (MRT). The requirements for patient-specific dosimetry using single photon emission computed tomography (SPECT) are challenging, particularly with respect to scatter correction. In this paper data from phantom studies, combined with results from a fully validated Monte Carlo (MC) SPECT camera simulation, are used to investigate the influence of the triple energy window (TEW) scatter correction on SPECT activity quantification for [Formula: see text]Lu MRT. Results from phantom data show that; (1) activity quantification for the total counts in the SPECT field-of-view demonstrates a significant overestimation in total activity recovery when TEW scatter correction is applied at low activities ([Formula: see text]200 MBq). (2) Applying the TEW scatter correction to activity quantification within a volume-of-interest with no background activity provides minimal benefit. (3) In the case of activity distributions with background activity, an overestimation of recovered activity of up to 30% is observed when using the TEW scatter correction. Data from MC simulation were used to perform a full analysis of the composition of events in a clinically reconstructed volume of interest. This allowed, for the first time, the separation of the relative contributions of partial volume effects (PVE) and inaccuracies in TEW scatter compensation to the observed overestimation of activity recovery. It is shown, that even with perfect partial volume compensation, TEW scatter correction can overestimate activity recovery by up to 11%. MC data is used to demonstrate that even a localized and optimized isotope-specific TEW correction cannot reflect a patient specific activity distribution without prior knowledge of the complete activity distribution. This highlights the important role of MC simulation in SPECT activity quantification.