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. Correlative analysis of the same specimens was performed by in vivo and ex vivo helical computed tomography (HCT) and ex vivo µCT. The mineral matter in the sialoliths consisted essentially of apatite (89 vol%) and whitlockite (11 vol%) with average density of 1.8 g/cm3. In hydrated conditions, the mineral mass prevailed with 53 ± 13 wt%, whereas the organic matter, with a density of 1.2 g/cm3, occupied 65 ± 10% of the sialoliths' volume. A quantitative relation between sialoliths mineral density and X-ray attenuation is proposed for both HCT and µCT.

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 clear correlation between local mechanical properties and ultrastructure/chemistry has been established: sialoliths are composite materials consisting of hard and soft components of mineralized and organic nature, respectively. Ultrasonic and pneumoballistic reverberations damage preferentially highly mineralized regions, leaving relatively unaffected the surrounding organic matter. In contrast, shock waves leach the organic component and lead to erosion of the overall structure. Laser ablation destroys homogeneously the irradiated zones regardless of the mineralized/organic nature of the underlying ultrastructure; however, damage is less extensive than with mechanical methods. Overall, the present results show that composition and internal structure are key features behind sialoliths' comminution behavior and that the organic matter contributes to reduce the therapeutic efficiency of lithotripsy methods.

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. The results were compared with the ones obtained from a single-step regularized Gauss-Newton method and a reduction of 15% in the image error was verified. The behavior of the developed algorithm in a simulated clinical environment was also assessed using a realistic bio-impedance model of the human head, derived from a high resolution magnetic resonance image.

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. The reason for this interest is that ADMM decomposes a difficult optimization problem into a sequence of much simpler problems. In this paper, we use the ADMM to reconstruct piecewise-smooth distributed parameters of elliptical partial differential equations from noisy and linear (blurred) observations of the underlying field. The distributed parameters are estimated by solving an inverse problem with total variation (TV) regularization. The proposed instance of the ADMM solves, in each iteration, an l(2) and a decoupled l(2) - l(1) optimization problems. An operator splitting is used to simplify the treatment of the TV regularizer, avoiding its smooth approximation and yielding a simple yet effective ADMM reconstruction method compared with previously proposed approaches. The competitiveness of the proposed method, with respect to the state-of-the-art, is illustrated in simulated 1-D and 2-D elliptical equation problems, which are representative of many real applications.