No abstract

Cancer is a disease which requires a significant amount of careful medical attention. For minimally-invasive thermal ablation procedures, the monitoring of heat distribution is one of the biggest challenges. In this work, three approaches for volumetric heat map reconstruction (Delauney triangulation, minimum volume enclosing ellipsoids (MVEE) and splines) are presented based on uniformly distributed 2D MRI phase images rotated around the applicator’s main axis. We compare them with our previous temperature interpolation method with respect to accuracy, robustness and adaptability. All approaches are evaluated during MWA treatment on the same data sets consisting of 13 ex vivo bio protein phantoms, including six phantoms with simulated heat sink effects. Regarding accuracy, the DSC similarity results show a strong trend towards the MVEE ($$0.80\pm 0.03$$ 0.80 ± 0.03 ) and the splines ($$0.77\pm 0.04$$ 0.77 ± 0.04 ) method compared to the Delauney triangulation ($$0.75\pm 0.02$$ 0.75 ± 0.02 ) or the temperature interpolation ($$0.73\pm 0.07$$ 0.73 ± 0.07 ). Robustness is increased for all three approaches and the adaptability shows a significant trend towards the initial interpolation method and the splines. To overcome local inhomogeneities in the acquired data, the use of adaptive simulations should be considered in the future. In addition, the transfer to in vivo animal experiments should be considered to test for clinical applicability.

<p>We present a mechanistic, spatially and temporally explicit microscale model to investigate the interactions between a growing root, its exudates and the soil structure. Our model allows us to simultaneously simulate and study the dynamic rearrangement of soil particles, the input and turnover of organic matter, the root growth and decay, as well as the deposition, redistribution and decomposition of mucilage into the rhizosphere. The interactions between these components are realized within a cellular automaton framework. Mechanistic rules lead to the formation and break-up of soil structures. The most stable configuration is determined by the amount and attractivity of surface contacts between the particles. Alteration of surface types due to addition and decomposition of organic matter and the root growth induced movements of particles result in varying aggregation dynamics over time and space.</p><p>We illustrate the capability of our model by simulating the growth and shrinkage period of a fine root in a two-dimensional, horizontal cross section through the soil. We evaluate various scenarios to identify the impact of the root and further influencing factors that shape soil aggregation in the rhizosphere. More precisely, we address how the soil structure formation is influenced by soil texture and the amount of mucilage. We quantify the variations in local porosity due to the change in available pore space as influenced by the root growth. We further identify attractive properties of the soil surface induced by root exudation as key factors for the creation of stable soil structures.</p>

<p>We present a spatially and temporally explicit mechanistic model for soil aggregation at the microscale. This consists of a cellular automaton model for the dynamic rearrangement of solid building units whose size and shape are derived from dynamic image analysis of wet-sieved, water stable aggregates. This model is combined with a particulate organic matter (POM) turnover model or a model for a growing fine root which exudes and distributes mucilage into the soil. Along this line the mutually interacting soil and POM dynamics and the intertwined processes at the root-soil interface are captured and evaluated simultaneously at the biologically relevant scale. Our comprehensive modeling toolbox allows us to conduct various simulation scenarios and to discriminate and evaluate the underlying processes and different drivers such as texture. We quantify our results by evaluating the stability of the created structures or the dynamical change in local porosity around a growing root. Finally, the insights gained at the microscale are used to parametrize a CO2 transport model at the profile scale. Thereby, the microbially mediated CO2 source is taken into account as well as the driver soil texture, and changing ambient environmental conditions such as water saturation, oxygen concentration and POM content.</p>

Minimally-invasive thermal ablation procedures have become clinically accepted treatment options for tumors and metastases. Continuous and reliable monitoring of volumetric heat distribution promises to be an important condition for successful outcomes. In this work, an adaptive bioheat transfer simulation of 3D thermometry maps is presented. Pennes’ equation model is updated according to temperature maps generated by uniformly distributed 2D MR phase images rotated around the main axis of the applicator. The volumetric heat diffusion and the resulting shape of the ablation zone can be modelled accurately without introducing a specific heat source term. Filtering the temperature maps by extracting isotherms reduces artefacts and noise, compresses information of the measured data and adds physical a priori knowledge. The inverse heat transfer for estimating values of the simulated tissue and heating parameters is done by reducing the sum squared error between these isotherms and the 3D simulation. The approach is evaluated on data sets consisting of 13 ex vivo bio protein phantoms, including six perfusion phantoms with simulated heat sink effects. Results show an overall average Dice score of 0.89 ± 0.04 (SEM < 0.01). The optimization of the parameters takes 1.05 ± 0.26 s for each acquired image. Future steps should consider the local optimization of the simulation parameters instead of a global one to better detect heat sinks without a priori knowledge. In addition, the use of a proper Kalman filter might increase robustness and accuracy if combined with our method.