Seth I. Dillard

From medical images to flow computations without user‐generated meshes

Biomedical flow computations in patient-specific geometries require integrating image acquisition and processing with fluid flow solvers. Typically, image-based modeling processes involve several steps, such as image segmentation, surface mesh generation, volumetric flow mesh generation, and finally, computational simulation. These steps are performed separately, often using separate pieces of software, and each step requires considerable expertise and investment of time on the part of the user. In this paper, an alternative framework is presented in which the entire image-based modeling process is performed on a Cartesian domain where the image is embedded within the domain as an implicit surface. Thus, the framework circumvents the need for generating surface meshes to fit complex geometries and subsequent creation of body-fitted flow meshes. Cartesian mesh pruning, local mesh refinement, and massive parallelization provide computational efficiency; the image-to-computation techniques adopted are chosen to be suitable for distributed memory architectures. The complete framework is demonstrated with flow calculations computed in two 3D image reconstructions of geometrically dissimilar intracranial aneurysms. The flow calculations are performed on multiprocessor computer architectures and are compared against calculations performed with a standard multistep route.

Techniques to derive geometries for image-based Eulerian computations

PURPOSE The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted. DESIGN/METHODOLOGY/APPROACH Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures. FINDINGS While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics. ORIGINALITY/VALUE It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

Image Based Modeling of Biotransport Through Complex Moving Geometries

One of the major challenges to realistically modeling biotransport phenomena in computational fluid dynamics simulations lies with the difficulty of accurately describing the tortuous geometries and motions exhibited by organs and organ systems. Descriptions must be created in a manner that is amenable to definition within some operative computational domain, while at the same time capturing the essence of what is desired to be understood.Copyright

Accuracy of detecting enlargement of aneurysms using different MRI modalities and measurement protocols

In BriefTo reliably assess the individual and agreement rates of accurately detecting intracranial aneurysm enlargement, the authors performed this study using flow phantom models and generally used MRI modalities. The results of this study suggest that the detection rate of at least 1 increase in any aneurysm dimension did not depend on the choice of MRI modality or different measurement protocols.

Image Based Flow Computations Without User Generated Meshes

Medical image processing has emerged as a powerful way to simulate fluid flows through realistic models of complex patient-specific geometries without relying upon simplifying geometric approximations. However, image-based flow modeling processes traditionally involve several steps (e.g. image segmentation, surface mesh generation, volumetric flow mesh generation, and finally computational simulation) that must often be performed using separate pieces of software. This work presents an alternative methodology in which the entire image-based flow modeling process takes place on a Cartesian domain with the image embedded as an implicit surface, circumventing the need for complex surface meshes and body-fitted flow meshes. The complete framework is demonstrated with flow calculations performed in a computed tomography (CT) image reconstruction of an intracranial aneurysm (ICA). Flow calculations are compared against calculations performed following a standard multi-step route using the Vascular Modeling Toolkit (VMTK) [1, 2] and Fluent™ (Ansys, Inc., Lebanon, NH).Copyright

Image-Based Modeling of Complex Boundaries for CFD Simulation

One outstanding challenge to understanding the behaviors of organisms and organ systems found in nature using CFD simulations lies in modeling the complex geometries and motions they generally exhibit. A route to overcoming this difficulty lies in employing imagery as a basis from which to begin creating such models. To this end we have developed a framework based on image segmentation techniques, followed by a level set based immersed boundary method that operates on a fixed Cartesian mesh. Level set motion is imposed using image morphing techniques, with optical flow used to set boundary conditions. With this approach, still images or movie files obtained from various imaging modalities can be automatically segmented so that flow solutions can be directly connected to image-derived geometries and boundary evolutions. The 2-dimensional methodology applied to a video sequence of an American eel swimming in a water tunnel apparatus is demonstrated presently, with current efforts to extend to 3-dimensions discussed briefly.

Image-Based Computational Modeling of Complex Organisms and Biological Structures

One outstanding challenge to understanding the behaviors of organisms and organ systems found in nature using CFD simulations lies in modeling the complex geometries and motions they generally exhibit. A route to overcoming this difficulty lies in employing imagery as a basis from which to begin creating such models. To this end we seek to develop a framework based on image segmentation techniques, followed by a level set based immersed boundary method that operates on a fixed Cartesian mesh. With this approach, still images or movie files obtained from various imaging modalities can be automatically segmented so that flow solutions can be directly connected to image-derived geometries and boundary evolutions. Preliminary examples of this methodology in two dimensions as applied to video files of an in vitro duodenal segment experiment and an eel swimming in a water tunnel apparatus are demonstrated presently.