Hydrogeochemical transport modeling of the infiltration of tertiary treated wastewater in a dune area, Belgium

Managed artificial recharge (MAR) is a well-established practice for augmentation of depleted groundwater resources or for environmental benefit. At the St-Andr, MAR site in the Belgian dune area, groundwater resources are optimised through re-use of highly treated wastewater by means of infiltration ponds. The very high quality of the infiltration water sets this system apart from other MAR systems. The low total dissolved solid (TDS) content in the infiltration water (less than 50 mg/L) compared to the dune aquifer (500 mg/L) triggers a number of reactions, increasing the TDS through soil-aquifer passage. Multi-component reactive transport modelling was applied to analyse the geochemical processes that occur. Carbonate dissolution is the main process increasing the TDS of the infiltration water. Oxic aquifer conditions prevail between the infiltration ponds and the extraction wells. This is driven by the high flow velocities, leaving no time to consume O-2 between the ponds and extraction wells. Cation exchange is important when infiltration water is replaced by native dune water or when significant changes in infiltration-water quality occur. The seasonal variation of O-2 and temperature in the infiltration water are the main drivers for seasonal changes in the concentration of all major ions

Deep learning-based 2D/3D registration of an atlas to biplanar X-ray images

Purpose The registration of a 3D atlas image to 2D radiographs enables 3D pre-operative planning without the need to acquire costly and high-dose CT-scans. Recently, many deep-learning-based 2D/3D registration methods have been proposed which tackle the problem as a reconstruction by regressing the 3D image immediately from the radiographs, rather than registering an atlas image. Consequently, they are less constrained against unfeasible reconstructions and have no possibility to warp auxiliary data. Finally, they are, by construction, limited to orthogonal projections. Methods We propose a novel end-to-end trainable 2D/3D registration network that regresses a dense deformation field that warps an atlas image such that the forward projection of the warped atlas matches the input 2D radiographs. We effectively take the projection matrix into account in the regression problem by integrating a projective and inverse projective spatial transform layer into the network. Results Comprehensive experiments conducted on simulated DRRs from patient CT images demonstrate the efficacy of the network. Our network yields an average Dice score of 0.94 and an average symmetric surface distance of 0.84 mm on our test dataset. It has experimentally been determined that projection geometries with 80 degrees to 100 degrees projection angle difference result in the highest accuracy. Conclusion Our network is able to accurately reconstruct patient-specific CT-images from a pair of near-orthogonal calibrated radiographs by regressing a deformation field that warps an atlas image or any other auxiliary data. Our method is not constrained to orthogonal projections, increasing its applicability in medical practices. It remains a future task to extend the network for uncalibrated radiographs

Deep learning-based 2D/3D registration of an atlas to biplanar X-ray images

Purpose The registration of a 3D atlas image to 2D radiographs enables 3D pre-operative planning without the need to acquire costly and high-dose CT-scans. Recently, many deep-learning-based 2D/3D registration methods have been proposed which tackle the problem as a reconstruction by regressing the 3D image immediately from the radiographs, rather than registering an atlas image. Consequently, they are less constrained against unfeasible reconstructions and have no possibility to warp auxiliary data. Finally, they are, by construction, limited to orthogonal projections. Methods We propose a novel end-to-end trainable 2D/3D registration network that regresses a dense deformation field that warps an atlas image such that the forward projection of the warped atlas matches the input 2D radiographs. We effectively take the projection matrix into account in the regression problem by integrating a projective and inverse projective spatial transform layer into the network. Results Comprehensive experiments conducted on simulated DRRs from patient CT images demonstrate the efficacy of the network. Our network yields an average Dice score of 0.94 and an average symmetric surface distance of 0.84 mm on our test dataset. It has experimentally been determined that projection geometries with 80 degrees to 100 degrees projection angle difference result in the highest accuracy. Conclusion Our network is able to accurately reconstruct patient-specific CT-images from a pair of near-orthogonal calibrated radiographs by regressing a deformation field that warps an atlas image or any other auxiliary data. Our method is not constrained to orthogonal projections, increasing its applicability in medical practices. It remains a future task to extend the network for uncalibrated radiographs