Potential of X-ray computed tomography for 3D anatomical analysis and microdensitometrical assessment in wood research with focus on wood modification

Studying structure and chemistry of wood and wood-based materials is the backbone of all wood research and many techniques are at hand to do so. A very valuable modality is X-ray computed tomography (CT), able to non-destructively probe the three-dimensional (3D) structure and composition. In this paper, we elaborate on the use of Nanowood, a flexible multi-resolution X-ray CT set-up developed at UGCT, the Ghent University Centre for X-ray Tomography. The technique has been used successfully in many different fields of wood science. It is illustrated how 3D structural and microdensitometrical data can be obtained using different scan set-ups and protocols. Its potential for the analysis of modified wood is exemplified, e.g. for the assessment of wood treated with hydrophobing agents, localisation of modification agents, pathway analysis related to functional tissues, dimensional changes due to thermal treatment, etc. Furthermore, monitoring of transient processes is a promising field of activity too

Simultaneous inversion of mantle properties and initial conditions using an adjoint of mantle convection

Through the assimilation of present-day mantle seismic structure, adjoint methods can be used to constrain the structure of the mantle at earlier times, i.e., mantle initial conditions. However, the application to geophysical problems is restricted through both the high computational expense from repeated iteration between forward and adjoint models and the need to know mantle properties (such as viscosity and the absolute magnitude of temperature or density) a priori. We propose that an optimal first guess to the initial condition can be obtained through a simple backward integration (SBI) of the governing equations, thus lessening the computational expense. Given a model with known mantle properties, we show that a solution based on an SBI-generated first guess has smaller residuals than arbitrary guesses. Mantle viscosity and the effective Rayleigh number are crucial for mantle convection models, neither of which is exactly known. We place additional constraints on these basic mantle properties when the convection-induced dynamic topography on Earth's surface is considered within an adjoint inverse method. Besides assimilating present-day seismic structure as a constraint, we use dynamic topography and its rate of change in an inverse method that allows simultaneous inversion of the absolute upper and lower mantle viscosities, scaling between seismic velocity and thermal anomalies, and initial condition. The theory is derived from the governing equations of mantle convection and validated by synthetic experiments for both one-layer viscosity and two-layer viscosity regionally bounded spherical shells. For the one-layer model, at any instant of time, the magnitude of dynamic topography is controlled by the temperature scaling while the rate of change of topography is controlled by the absolute value of viscosity. For the two-layer case, the rate of change of topography constrains upper mantle viscosity while the magnitude of dynamic topography determines the temperature scaling (lower mantle viscosity) when upper-mantle (lower-mantle) density anomaly dominates the flow field; this two-stage scheme minimizes the tradeoff between temperature and lower mantle viscosity. For both cases, we show that the theory can constrain mantle properties with errors arising through the adjoint recovery of the initial condition; for the two-layer model, this error is manifest as a tradeoff between the temperature scaling and lower mantle viscosity

Direct estimation of kinetic parametric images for dynamic PET.

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed

A schema for generic process tomography sensors

A schema is introduced that aims to facilitate the widespread exploitation of the science of process tomography (PT) that promises a unique multidimensional sensing opportunity. Although PT has been developed to an advanced state, applications have been laboratory or pilot-plant based, configured on an end-to-end basis, and limited typically to the formation of images that attempt to represent process contents. The schema facilitates the fusion of multidimensional internal process state data in terms of a model that yields directly usable process information, either for design model confirmation or for effective plant monitoring or control, here termed a reality visualization model (RVM). A generic view leads to a taxonomy of process types and their respective RVM. An illustrative example is included and a review of typical sensor system components is given

Direct EIT Reconstructions of Complex Admittivities on a Chest-Shaped Domain in 2-D

Electrical impedance tomography (EIT) is a medical imaging technique in which current is applied on electrodes on the surface of the body, the resulting voltage is measured, and an inverse problem is solved to recover the conductivity and/or permittivity in the interior. Images are then formed from the reconstructed conductivity and permittivity distributions. In the 2-D geometry, EIT is clinically useful for chest imaging. In this work, an implementation of a D-bar method for complex admittivities on a general 2-D domain is presented. In particular, reconstructions are computed on a chest-shaped domain for several realistic phantoms including a simulated pneumothorax, hyperinflation, and pleural effusion. The method demonstrates robustness in the presence of noise. Reconstructions from trigonometric and pairwise current injection patterns are included