A parametric level-set method for partially discrete tomography

This paper introduces a parametric level-set method for tomographic reconstruction of partially discrete images. Such images consist of a continuously varying background and an anomaly with a constant (known) grey-value. We represent the geometry of the anomaly using a level-set function, which we represent using radial basis functions. We pose the reconstruction problem as a bi-level optimization problem in terms of the background and coefficients for the level-set function. To constrain the background reconstruction we impose smoothness through Tikhonov regularization. The bi-level optimization problem is solved in an alternating fashion; in each iteration we first reconstruct the background and consequently update the level-set function. We test our method on numerical phantoms and show that we can successfully reconstruct the geometry of the anomaly, even from limited data. On these phantoms, our method outperforms Total Variation reconstruction, DART and P-DART.Comment: Paper submitted to 20th International Conference on Discrete Geometry for Computer Imager

A Finite Element Test Bed for Diffraction Tomography

Finite element analysis methods have been successfully applied to the study of ultrasonic wave propagation in elastic solids [1–4]. As a natural part of such numerical solutions. displacements are predicted for every node of the spatial discretization describing the solids geometry and at every instant of time in the temporal discretization used to define the pulse propagation through the material. All of the data constitute a solution to the forward problem and can be used to visualize wavefront propagation and interactions with defects, thus predicting displacement signals at any point in or on the solid

Photon CT Scanning of Advanced Ceramic Materials

Advanced ceramic materials (e. g. Si3N4, ZrO2, SiC, A12O3) are being developed for high temperature applications in advanced heat engines and high temperature heat recovery systems [1]. Although fracture toughness has been a constant problem, advanced ceramics are now being developed with fracture toughnesses close to those of metals [2]. Small size flaws (10–200 μm), small non-uniformities in density distributions (0.1–2%) present as long-range density gradients, and porous regions which can be seen as localized areas of slightly lower density, are critical in most ceramics. The need to detect these small flaws is causing a significant effort to be devoted towards nondestructive evaluation. Detection of “defects” such as those noted in engineering ceramics has presented problems for conventional non-destructive evaluation methods [3]

A Multi-Channel DART algorithm

Tomography deals with the reconstruction of objects from their projections, acquired along a range of angles. Discrete tomography is concerned with objects that consist of a small number of materials, which makes it possible to compute accurate reconstructions from highly limited projection data. For cases where the allowed intensity values in the reconstruction are known a priori, the discrete algebraic reconstruction technique (DART) has shown to yield accurate reconstructions from few projections. However, a key limitation is that the benefit of DART diminishes as the number of different materials increases. Many tomographic imaging techniques can simultaneously record tomographic data at multiple channels, each corresponding to a different weighting of the materials in the object. Whenever projection data from more than one channel is available, this additional information can potentially be exploited by the reconstruction algorithm. In this paper we present Multi-Channel DART (MC-DART), which deals effectively with multi-channel data. This class of algorithms is a generalization of DART to multiple channels and combines the information for each separate channel-reconstruction in a multi-channel segmentation step. We demonstrate that in a range of simulation experiments, MC-DART is capable of producing more accurate reconstructions compared to single-channel DART

Quantitative imaging of coronary blood flow

Positron emission tomography (PET) is a nuclear medicine imaging modality based on the administration of a positron-emitting radiotracer, the imaging of the distribution and kinetics of the tracer, and the interpretation of the physiological events and their meaning with respect to health and disease. PET imaging was introduced in the 1970s and numerous advances in radiotracers and detection systems have enabled this modality to address a wide variety of clinical tasks, such as the detection of cancer, staging of Alzheimer's disease, and assessment of coronary artery disease (CAD). This review provides a description of the logic and the logistics of the processes required for PET imaging and a discussion of its use in guiding the treatment of CAD. Finally, we outline prospects and limitations of nanoparticles as agents for PET imaging

Characterization of heterogeneity and spatial distribution of phases in complex solid dispersions by thermal analysis by structural characterization and X-ray micro computed tomography

Purpose: This study investigated the effect of drug-excipient miscibility on the heterogeneity and spatial distribution of phase separation in pharmaceutical solid dispersions at a micron-scale using two novel and complementary characterization techniques, thermal analysis by structural characterization (TASC) and X-ray micro-computed tomography (XCT) in conjunction with conventional characterization methods. Method: Complex dispersions containing felodipine, TPGS, PEG and PEO were prepared using hot melt extrusion-injection moulding. The phase separation behavior of the samples was characterized using TASC and XCT in conjunction with conventional thermal, microscopic and spectroscopic techniques. The in vitro drug release study was performed to demonstrate the impact of phase separation on dissolution of the dispersions. Results: The conventional characterization results indicated the phase separating nature of the carrier materials in the patches and the presence of crystalline drug in the patches with the highest drug loading (30% w/w). TASC and XCT where used to provide insight into the spatial configuration of the separate phases. TASC enabled assessment of the increased heterogeneity of the dispersions with increasing the drug loading. XCT allowed the visualization of the accumulation of phase separated (crystalline) drug clusters at the interface of air pockets in the patches with highest drug loading which led to poor dissolution performance. Semi-quantitative assessment of the phase separated drug clusters in the patches were attempted using XCT. Conclusion: TASC and XμCT can provide unique information regarding the phase separation behavior of solid dispersions which can be closely associated with important product quality indicators such as heterogeneity and microstructure

Atomic super-resolution tomography

We consider the problem of reconstructing a nanocrystal at atomic resolution from electron microscopy images taken at a few tilt angles. A popular reconstruction approach called discrete tomography confines the atom locations to a coarse spatial grid, which is inspired by the physical a priori knowledge that atoms in a crystalline solid tend to form regular lattices. Although this constraint has proven to be powerful for solving this very under-determined inverse problem in many cases, its key limitation is that, in practice, defects may occur that cause atoms to deviate from regular lattice positions. Here we propose a grid-free discrete tomography algorithm that allows for continuous deviations of the atom locations similar to super-resolution approaches for microscopy. The new formulation allows us to define atomic interaction potentials explicitly, which results in a both meaningful and powerful incorporation of the available physical a priori knowledge about the crystal's properties. In computational experiments, we compare the proposed grid-free method to established grid-based approaches and show that our approach can indeed recover the atom positions more accurately for common lattice defects

Non-Invasive Microstructure and Morphology Investigation of the Mouse Lung: Qualitative Description and Quantitative Measurement

BACKGROUND: Early detection of lung cancer is known to improve the chances of successful treatment. However, lungs are soft tissues with complex three-dimensional configuration. Conventional X-ray imaging is based purely on absorption resulting in very low contrast when imaging soft tissues without contrast agents. It is difficult to obtain adequate information of lung lesions from conventional X-ray imaging. METHODS: In this study, a recently emerged imaging technique, in-line X-ray phase contrast imaging (IL-XPCI) was used. This powerful technique enabled high-resolution investigations of soft tissues without contrast agents. We applied IL-XPCI to observe the lungs in an intact mouse for the purpose of defining quantitatively the micro-structures in lung. FINDINGS: The three-dimensional model of the lung was successfully established, which provided an excellent view of lung airways. We highlighted the use of IL-XPCI in the visualization and assessment of alveoli which had rarely been studied in three dimensions (3D). The precise view of individual alveolus was achieved. The morphological parameters, such as diameter and alveolar surface area were measured. These parameters were of great importance in the diagnosis of diseases related to alveolus and alveolar scar. CONCLUSION: Our results indicated that IL-XPCI had the ability to represent complex anatomical structures in lung. This offered a new perspective on the diagnosis of respiratory disease and may guide future work in the study of respiratory mechanism on the alveoli level

Reduced projection angles for binary tomography with particle aggregation

This paper extends particle aggregate reconstruction technique (PART), a reconstruction algorithm for binary tomography based on the movement of particles. PART supposes that pixel values are particles, and that particles diffuse through the image, staying together in regions of uniform pixel value known as aggregates. In this work, a variation of this algorithm is proposed and a focus is placed on reducing the number of projections and whether this impacts the reconstruction of images. The algorithm is tested on three phantoms of varying sizes and numbers of forward projections and compared to filtered back projection, a random search algorithm and to SART, a standard algebraic reconstruction method. It is shown that the proposed algorithm outperforms the aforementioned algorithms on small numbers of projections. This potentially makes the algorithm attractive in scenarios where collecting less projection data are inevitable