Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical parameter estimation is an ill-posed inverse problem, and thus it is sensitive to measurement and modeling errors. In this work, we propose a novel way to solve the inverse problem of quantitative photoacoustic tomography based on the perturbation Monte Carlo method. Monte Carlo method for light propagation is a stochastic approach for simulating photon trajectories in a medium with scattering particles. It is widely accepted as an accurate method to simulate light propagation in tissues. Furthermore, it is numerically robust and easy to implement. Perturbation Monte Carlo maintains this robustness and enables forming gradients for the solution of the inverse problem. We validate the method and apply it in the framework of Bayesian inverse problems. The simulations show that the perturbation Monte Carlo method can be used to estimate spatial distributions of both absorption and scattering parameters simultaneously. These estimates are qualitatively good and quantitatively accurate also in parameter scales that are realistic for biological tissues

A model-based iterative learning approach for diffuse optical tomography

Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a ‘model-based’ learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration

Nonlinear approach to difference imaging in diffuse optical tomography

Difference imaging aims at recovery of the change in the optical properties of a body based on measurements before and after the change. Conventionally, the image reconstruction is based on using difference of the measurements and a linear approximation of the observation model. One of the main benefits of the linearized difference reconstruction is that the approach has a good tolerance to modeling errors, which cancel out partially in the subtraction of the measurements. However, a drawback of the approach is that the difference images are usually only qualitative in nature and their spatial resolution can be weak because they rely on the global linearization of the nonlinear observation model. To overcome the limitations of the linear approach, we investigate a nonlinear approach for difference imaging where the images of the optical parameters before and after the change are reconstructed simultaneously based on the two datasets. We tested the feasibility of the method with simulations and experimental data from a phantom and studied how the approach tolerates modeling errors like domain truncation, optode coupling errors, and domain shape errors

Can a single molecule trap the electron?

We suggest that it might be possible to trap the electron in a cavity of a macrocycle molecule, in the same way this trapping occurs cooperatively, by several solvent molecules, in hydroxylic liquids. Such an encapsulated electron is a "molecular capacitor," in which the excess electron is largely decoupled from valence electrons in the trap. A specific design for such a trap that is based on calix[4]cyclohexanol is discussed in detail. It is shown theoretically, by ab initio and density functional theory (DFT) modeling, that one of the conformations of this molecule forms the optimum tetrahedral trap for the electron. The resulting "encapsulated electron" strikingly resembles the solvated electron in alcohols and water.Comment: 13 pages 2 tables 5 figures, submitted to Chem Phys Let

Geminate recombination of electrons generated by above-the-gap (12.4 eV) photoionization of liquid water

The picosecond geminate recombination kinetics for hydrated electrons generated by 200 nm two photon absorption (12.4 eV total energy) has been measured in both light and heavy water. The geminate kinetics are observed to be almost identical in both H2O and D2O. Kinetic analysis based upon the independent reaction time approximation indicates that the average separation between the electron and its geminate partners in D2O is 13% narrower than in H2O (2.1 nm vs. 2.4 nm). These observations suggest that, even at this high ionization energy, autoionization of water competes with direct ionization.Comment: 10 pages + 2 figures, submitted to Chem. Phys. Letter

Megacity pumping and preferential flow threaten groundwater quality

Many of the world’s megacities depend on groundwater from geologically complex aquifers that are over-exploited and threatened by contamination. Here, using the example of Dhaka, Bangladesh, we illustrate how interactions between aquifer heterogeneity and groundwater exploitation jeopardize groundwater resources regionally. Groundwater pumping in Dhaka has caused large-scale drawdown that extends into outlying areas where arsenic-contaminated shallow groundwater is pervasive and has potential to migrate downward. We evaluate the vulnerability of deep, low-arsenic groundwater with groundwater models that incorporate geostatistical simulations of aquifer heterogeneity. Simulations show that preferential flow through stratigraphy typical of fluvio-deltaic aquifers could contaminate deep (>150 m) groundwater within a decade, nearly a century faster than predicted through homogeneous models calibrated to the same data. The most critical fast flowpaths cannot be predicted by simplified models or identified by standard measurements. Such complex vulnerability beyond city limits could become a limiting factor for megacity groundwater supplies in aquifers worldwide.National Institute of Environmental Health Sciences. Superfund Research Program (Grant P42 ES010349)National Science Foundation (U.S.) (Grant EAR-115173

Vulnerability of low-arsenic aquifers to municipal pumping in Bangladesh

Sandy aquifers deposited >12,000 years ago, some as shallow as 30 m, have provided a reliable supply of low-arsenic (As) drinking water in rural Bangladesh. This study concerns the potential risk of contaminating these aquifers in areas surrounding the city of Dhaka where hydraulic heads in aquifers >150 m deep have dropped by 70 m in a few decades due to municipal pumping. Water levels measured continuously from 2012 to 2014 in 12 deep (>150 m), 3 intermediate (90-150 m) and 6 shallow (<90 m) community wells, 1 shallow private well, and 1 river piezometer show that the resulting drawdown cone extends 15-35 km east of Dhaka. Water levels in 4 low-As community wells within the 62-147 m depth range closest to Dhaka were inaccessible by suction for up to a third of the year. Lateral hydraulic gradients in the deep aquifer system ranged from 1.7 × 10-4 to 3.7 × 10-4 indicating flow towards Dhaka throughout 2012-2014. Vertical recharge on the edge of the drawdown cone was estimated at 0.21 ± 0.06 m/yr. The data suggest that continued municipal pumping in Dhaka could eventually contaminate some relatively shallow community wells

The Political Economy of Domestic Tax Reform in Bangladesh: Political Settlements, Informal Institutions and the Negotiation of Reform

This paper explains the persistence of a tax system characterised by low revenue collection and extensive informality in Bangladesh. It combines analysis of long-term formal and informal institutions and of micro-level incentives shaping negotiation of short-term reform. The system is unusually informal, discretionary, and corrupt, but remains resistant to change because it delivers low and predictable tax rates to business, extensive opportunities for corruption to the tax administration, and an important vehicle for fundraising by political leaders and rent distribution to their elite supporters. We then explore the dynamics of micro-level reform and external pressure within the constraints of this overarching political bargain