Mineralization of Sialoliths Investigated by Ex Vivo and In Vivo X-ray Computed Tomography.

The fraction of organic matter present affects the fragmentation behavior of sialoliths; thus, pretherapeutic information on the degree of mineralization is relevant for a correct selection of lithotripsy procedures. This work proposes a methodology for in vivo characterization of salivary calculi in the pretherapeutic context. Sialoliths were characterized in detail by X-ray computed microtomography (μCT) in combination with atomic emission spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy.

Local Response of Sialoliths to Lithotripsy: Cues on Fragmentation Outcome.

Lithotripsy methods show relatively low efficiency in the fragmentation of sialoliths compared with the success rates achieved in the destruction of renal calculi. However, the information available on the mechanical behavior of sialoliths is limited and their apparently tougher response is not fully understood. This work evaluates the hardness and Young's modulus of sialoliths at different scales and analyzes specific damage patterns induced in these calcified structures by ultrasonic vibrations, pneumoballistic impacts, shock waves, and laser ablation.

A new image reconstruction algorithm for real-time monitoring of conductivity and permeability changes in Magnetic Induction Tomography.

A linear image reconstruction algorithm for solving the Magnetic Induction Tomography inverse problem is presented. It's an optimization process to determine a reconstruction matrix that does the best mapping between a set of training parameter vectors and their respective measurements dictated by a forward model. It allows the simultaneous 3D reconstructions of the electric conductivity, electric permittivity and magnetic permeability.

An alternating direction algorithm for total variation reconstruction of distributed parameters.

Augmented Lagrangian variational formulations and alternating optimization have been adopted to solve distributed parameter estimation problems. The alternating direction method of multipliers (ADMM) is one of such formulations/optimization methods. Very recently, the number of applications of the ADMM, or variants of it, to solve inverse problems in image and signal processing has increased at an exponential rate.