Evaluation of geometric deviations in rapid prototyped phantoms created from medical imaging data (on the example of computed tomography)

Aim of study. To evaluate the geometric deviations associated with creation of physical objects from computed tomography data using computer-aided design and additive manufacturing. Materials and methods. The source object was created using the FreeCAD application; Blender and Meshmixer software was used for polygon meshes correction and transformation. 3D printing was carried out on an Ender-3 printer with copper-impregnated polylactide plastic BFCopper. Scanning was performed using a 128-slice tomograph Philips Ingenuity CT. A series of tomographic images were processed in 3DSlicer software, used to create virtual models by semiautomatic segmentation with threshold values ​​of 500 HU, 0 HU, -500 HU, -750 HU and manual segmentation. Reproduced and reference polygon meshes were compared using Iterative Closest Point algorithm in CloudCompare software. Results. Reproduced models volume exceeded the volume of respective reference models by 1-27%. The average point cloud linear deviation values of reproduced models from the reference ones were 0.03-0.41 mm. A significant correlation between integral sums of linear deviations and changes in the volume of reproduced models was shown using Spearman's rank correlation coefficient ( = 0.83; temp = 5.27, significance level p = 0.05). Conclusion. The geometry of the reproduced object changes inevitably, while the linear deviations depend more on the chosen segmentation method rather than on the overall size of the model or its structures. Manual segmentation method can lead to greater linear deviations, though it allows to save all the necessary structures.

EUCLIDIAN DISTANCE TRANSFORMATION, MAIN AXIS ROTATION AND NOISY DILITATION SUPPORTED CROSS-SECTION CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS

Polygon meshes and particularly triangulated meshes can be used to describe the shape of different types of geometry such as bicycles, bridges, or runways. In engineering, such polygon meshes can be supplied as finite element meshes, resulting from topology optimization or from laser scanning. Especially from topology optimization, frame-like polygon meshes with slender parts are typical and often have to be converted into a CAD (Computer-Aided Design) format, e.g., for further geometrical detailing or performing additional shape optimization. Especially for such frame-like geometries, CAD designs are constructed as beams with cross-sections and beam-lines, whereby the cross-section is extruded along the beam-lines or beam skeleton. One major task in the recognition of beams is the classification of the cross-section type such as I, U, or T, which is addressed in this article. Therefore, a dataset consisting of different cross-sections represented as binary images is created. Noisy dilatation, the distance transformation, and main axis rotation are applied to these images to increase the robustness and reduce the necessary amount of samples. The resulting images are applied to a convolutional neuronal network.

SUBDIVISION SURFACE MID-SURFACE RECONSTRUCTION OF TOPOLOGY OPTIMIZATION RESULTS AND THIN-WALLED SHAPES USING SURFACE SKELETONS

Polygon meshes and particularly triangulated meshes can be used to describe the shape of different types of geometry such as bicycles, bridges, or runways. In engineering, such polygon meshes can occur as finite element meshes, resulting from topology optimization or laser scanning. This article presents an automated parameterization of polygon meshes into a parametric representation using subdivision surfaces, especially in topology optimization. Therefore, we perform surface skeletonization on a volumetric grid supported by the Euclidian distance transformation and topology preserving and shape-preserving criterion. Based on that surface skeleton, an automated conversation into a Subdivision Surface Control grid is established. The final mid-surface-like parametrization is quite flexible and can be changed by variating the control gird or the local thickness.

Musiré: multimodal simulation and reconstruction framework for the radiological imaging sciences

A software-based workflow is proposed for managing the execution of simulation and image reconstruction for SPECT, PET, CBCT, MRI, BLI and FMI packages in single and multimodal biomedical imaging applications. The workflow is composed of a Bash script, the purpose of which is to provide an interface to the user, and to organize data flow between dedicated programs for simulation and reconstruction. The currently incorporated simulation programs comprise GATE for Monte Carlo simulation of SPECT, PET and CBCT, SpinScenario for simulating MRI, and Lipros for Monte Carlo simulation of BLI and FMI. Currently incorporated image reconstruction programs include CASToR for SPECT and PET as well as RTK for CBCT. MetaImage (mhd) standard is used for voxelized phantom and image data format. Meshlab project (mlp) containers incorporating polygon meshes and point clouds defined by the Stanford triangle format (ply) are employed to represent anatomical structures for optical simulation, and to represent tumour cell inserts. A number of auxiliary programs have been developed for data transformation and adaptive parameter assignment. The software workflow uses fully automatic distribution to, and consolidation from, any number of Linux workstations and CPU cores. Example data are presented for clinical SPECT, PET and MRI systems using the Mida head phantom and for preclinical X-ray, PET and BLI systems employing the Digimouse phantom. The presented method unifies and simplifies multimodal simulation setup and image reconstruction management and might be of value for synergistic image research. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

Generalized Light Portals

Light portals are useful for accelerating the convergence of Monte Carlo path tracing when rendering interiors. However, they are generally limited to flat polygonal shapes. In this paper, we introduce a new concept that allows existing polygon meshes with arbitrary shaders in a scene to be used as generalized light portals. We also present an efficient sampling method that takes into account the pixel values of the environment map and ray guiding two-dimensional textures that are typically opacity or transparency maps. This novel sampling strategy can be combined with other sampling techniques by using multiple importance sampling.

Evo-Devo Strategies for Generative Architecture: Colour-Based Patterns in Polygon Meshes

Parametric design in architecture is often pigeonholed by its own definition and computational complexity. This article explores the generative capacity to integrate patterns and flows analogous to evolutionary developmental biology (Evo-Devo) strategies to develop emergent proto-architecture. Through the use of coloured patterns (genotype) and the modification of polygonal meshes (phenotype), a methodological proposal is achieved that is flexible to changes and personalization, computationally efficient, and includes a wide range of typologies. Both the process and the result are oriented towards computational lightness for a future and better integration of the workflow in genetic algorithms. Flow-based programming is used to replicate genetic properties such as multifunctionality, repeatability and interchangeability. The results reinforce the biological strategies against other more computationally abstract ones and successfully execute the parallels of universal mechanisms in Evo-Devo that are present in life.