Principles for an Implementation of a Complete CT Reconstruction Tool Chain for Arbitrary Sized Data Sets and Its GPU Optimization.

This article describes the implementation of an efficient and fast in-house computed tomography (CT) reconstruction framework. The implementation principles of this cone-beam CT reconstruction tool chain are described here. The article mainly covers the core part of CT reconstruction, the filtered backprojection and its speed up on GPU hardware. Methods and implementations of tools for artifact reduction such as ring artifacts, beam hardening, algorithms for the center of rotation determination and tilted rotation axis correction are presented. The framework allows the reconstruction of CT images of arbitrary data size. Strategies on data splitting and GPU kernel optimization techniques applied for the backprojection process are illustrated by a few examples.

Spherical Agglomerates of Lactose Reduce Segregation in Powder Blends and Improve Uniformity of Tablet Content at High Drug Loads.

We report here on improved uniformity of blends of micronised active pharmaceutical ingredients (APIs) using addition of spherical agglomerates of lactose and enhanced blend flow to improve tablet content uniformity with higher API loads. Micromeritic properties and intra-particle porosity (using nano-computed X-ray tomography) of recently introduced spherical agglomerates of lactose and two standard lactose grades for the direct compression processes were compared. Powder blends of the individual lactose types and different micronised API drug loads were prepared and subjected to specific conditions that can induce API segregation. Tablet content uniformity during direct compression was related to the lactose material attributes. The distinctive micromeritic properties of the lactose types showed that spherical agglomerates of lactose had high intra-particle porosity and increased specific surface area. The stability of binary blends after intense sieving was governed by the intra-particle porosity and surface roughness of the lactose particles, which determined the retention of the model substance. Greater intra-particle porosity, powder specific surface area, and particle size of the spherical agglomerates provided greater adhesion of micronised particles, compared to granulated and spray-dried lactose. Thus the spherical agglomerates provided enhanced final blend flow and uniformity of tablet content at higher drug loads.

Preparation and Machine-Learning Methods of Nacre-like Composites from the Self-Assembly of Magnetic Colloids Exposed to Rotating Magnetic Fields.

Composite materials designed by nature, such as nacre, can display unique mechanical properties and have therefore been often mimicked by scientists. In this work, we prepared composite materials mimicking the nacre structure in two steps. First, we synthesized a silica gel skeleton with a layered structure using a bottom-up approach by modifying a sol-gel synthesis. Magnetic colloids were added to the sol solution, and a rotating magnetic field was applied during the sol-gel transition. When exposed to a rotating magnetic field, magnetic colloids organize in layers parallel to the plane of rotation of the field and template the growing silica phase, resulting in a layered anisotropic silica network mimicking the nacre's inorganic phase. Heat treatment has been applied to further harden the silica monoliths. The final nacre-inspired composite is created by filling the porous structure with a monomer, leading to a soft elastomer upon polymerization. Compression tests of the platelet-structured composite show that the mechanical properties of the nacre-like composite material far exceed those of nonstructured composite materials with an identical chemical composition. Increased toughness and a nearly 10-fold increase in Young's modulus were achieved. The natural brittleness and low elastic deformation of silica monoliths could be overcome by mimicking the natural architecture of nacre. Pattern recognition obtained with a classification of machine learning algorithms was applied to achieve a better understanding of the physical and chemical parameters that have the highest impact on the mechanical properties of the monoliths. Multivariate statistical analysis was performed to show that the structural control and the heat treatment have a very strong influence on the mechanical properties of the monoliths.

Predicting the macroscopic response of electrospun membranes based on microstructure and single fibre properties.

In the present paper, the three-dimensional structure and macroscopic mechanical response of electrospun poly(L-lactide) membranes is predicted based only on the geometry and elasto-plastic mechanical properties of single fibres supplemented by measurements of membrane weight and volume, and the resulting computational models are used to study the non-affine micro-kinematics of electrospun networks. To this end, statistical parameters describing the in-plane fibre morphology are extracted from scanning electron micrographs of the membranes, and computational network models are generated by matching the porosity of the real mats. The virtual networks are compared against computed tomography scans in terms of structure, and against uniaxial tension tests with respect to their macroscopic mechanical response. The obtained virtual network structure agrees well with the fibre disposition in real networks, and the rigorous prediction of the mechanical response of two membranes with mean diameters of 1.10µm and 0.70µm captures the experimental behaviour qualitatively. Favourable quantitative agreement, however, is obtained only after lowering the Young's moduli, yield stresses and hardening slopes determined in single fibre tests, and after reducing the density of inter-fibre bonds in the model of the membrane with thinner fibres. The simulations thus demonstrate the validity and merits of the approach to study the multi-scale mechanics of electrospun networks, but also point to potential discrepancies between the properties of electrospun fibres within a network and those produced for single fibre characterisation, and highlight the existing uncertainty on the density and quality of bonds between fibres in electrospun networks.

Bone mineral density, mechanical properties, and trabecular orientation of cancellous bone within humeral heads affected by advanced shoulder arthropathy.

The mechanical properties of cancellous bone in the humeral head are increasingly interesting due to the increased popularity of stemless prosthetic fixation in the cancellous bone of the metaphysis. Age or pathology-related systemic osteoporosis, inactivity, or pathology of the shoulder joint may influence the primary bonding of implants that rely on good cancellous bone quality. We assessed the bone mineral density (BMD) and anisotropy using micro-computed tomography (micro-CT) (0.04 mm voxel size) and correlated the results with indentation load/displacement response. Resected parts of humeral heads (from patients undergoing total shoulder replacement, n = 18) were used as probes. The region of interest was defined as 2 mm medial from the resection plane, presuming that it mirrored the bone quality lateral to the resection plane. The indentation tests were performed with a large probe (diameter 10 mm) in a single destructive loading procedure. The BMD and trabecular orientation were determined by micro-CT. Our results showed a correlation between the BMD and the slope of the load/displacement curve. Furthermore, the trabeculae were predominantly oriented orthogonal to the joint surface. In conclusion, the predominant factor determining the bone quality and mechanical resistance to pressure appears to be the BMD, while trabecular orientation could not be related to load/displacement response. Statement of clinical significance: Bone quality predominately determines the mechanical properties of cancellous bone. This might be crucial when prosthetic implants need to be anchored in metaphyseal bone. Therefore, clinical decision-making processes should also include local BMD measurements.