Turbulence investigation in a laboratory model of the ascending aorta

This study aims to investigate turbulence inside a model of the human ascending aorta as a function of the main flow control parameters. For this purpose, we performed a two-dimensional in vitro investigation of the pulsatile flow inside a laboratory model of a healthy aorta by varying both the Reynolds and Womersley numbers. Our findings indicate that the velocity fluctuations become significant particularly during the deceleration phase of the flow, reach the maximum near the systolic peak and then decay during the rest of the diastole phase. Higher levels of turbulence were recovered for increasing Stroke Volumes, in particular maxima of Turbulent Kinetic Energy occurred in the bulk region while higher values of Reynolds shear stresses were found in correspondence of the sinus of Valsalva

Turbulence investigation in a laboratory model of the ascending aorta

This study aims to investigate turbulence inside a model of the human ascending aorta as a function of the main flow control parameters. For this purpose, we performed a two-dimensional in vitro investigation of the pulsatile flow inside a laboratory model of a healthy aorta by varying both the Reynolds and Womersley numbers. Our findings indicate that the velocity fluctuations become significant particularly during the deceleration phase of the flow, reach the maximum near the systolic peak and then decay during the rest of the diastole phase. Higher levels of turbulence were recovered for increasing Stroke Volumes, in particular maxima of Turbulent Kinetic Energy occurred in the bulk region while higher values of Reynolds shear stresses were found in correspondence of the sinus of Valsalva

Design and validation of Segment - freely available software for cardiovascular image analysis

<p>Abstract</p> <p>Background</p> <p>Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format.</p> <p>Results</p> <p>Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page <url>http://segment.heiberg.se</url>.</p> <p>Conclusions</p> <p>Segment is a well-validated comprehensive software package for cardiovascular image analysis. It is freely available for research purposes provided that relevant original research publications related to the software are cited.</p

Implicit deformable models for biomedical image segmentation.

In this thesis, new methods for the efficient segmentation of images are presented. The proposed methods are based on the deformable model approach, and can be used efficiently in the segmentation of complex geometries from various imaging modalities. A novel deformable model that is based on a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions is presented. This external force field is based on hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contributes to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and give the deformable model a high invariance in initialization configurations. The voxel interactions across the whole image domain provides a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force held allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, it is shown that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. A comparative study on the segmentation of various geometries with different topologies from both synthetic and real images is provided, and the proposed method is shown to achieve significant improvements against several existing techniques. A robust framework for the segmentation of vascular geometries is described. In particular, the framework consists of image denoising, optimal object edge representation, and segmentation using implicit deformable model. The image denoising is based on vessel enhancing diffusion which can be used to smooth out image noise and enhance the vessel structures. The image object boundaries are derived using an edge detection technique which can produce object edges of single pixel width. The image edge information is then used to derive the geometric interaction field for optimal object edge representation. The vascular geometries are segmented using an implict deformable model. A region constraint is added to the deformable model which allows it to easily get around calcified regions and propagate across the vessels to segment the structures efficiently. The presented framework is ai)plied in the accurate segmentation of carotid geometries from medical images. A new segmentation model with statistical shape prior using a variational approach is also presented in this thesis. The proposed model consists of an image attraction force that propagates contours towards image object boundaries, and a global shape force that attracts the model towards similar shapes in the statistical shape distribution. The image attraction force is derived from gradient vector interactions across the whole image domain, which makes the model more robust to image noise, weak edges and initializations. The statistical shape information is incorporated using kernel density estimation, which allows the shape prior model to handle arbitrary shape variations. It is shown that the proposed model with shape prior can be used to segment object shapes from images efficiently

QUANTIFICATION OF CORONARY FLOW VELOCITY VIA CONTRAST DISPERSION PATTERNS: INSIGHTS FROM COMPUTATIONAL MODELING AND COMPUTED TOMOGRAPHY EXPERIMENTS

Advances in multi-detector cardiac computed tomography (CT) have expanded its use beyond coronary atherosclerosis to a suite of functional myocardial imaging options that now closely parallels magnetic resonance imaging; including ventricular function, viability and perfusion. Despite these advances, there are currently no existing CT based methods to assess coronary luminal blood flow/hemodynamics. Recent studies have shown that CT derived axial transluminal contrast gradients (TCG) are greater in coronary arteries with atherosclerotic lesions when compared with normal arteries; suggesting TCG may be related to local coronary hemodynamics. Despite this provocative observation, the basic mechanisms responsible for TCG and their possible connection with coronary hemodynamics have not been explained. In the current work, we hypothesize that TCG is related to the temporal gradients of the contrast bolus and that TCG encodes coronary flow velocity. An analytical relationship between spatial (TCG) and temporal measurements of contrast dispersion is proposed and this allows for estimation of coronary flow velocity from TCG. This is a novel method (called transluminal attenuation flow encoding-TAFE) integrates: a) anatomic features of the coronary vessels, b) TCG and c) temporal gradients in contrast associated with the arterial input function (AIF) that are readily available in conventional CT to allow non-invasive CT derived coronary flow quantification. The TAFE formulation is validated in computational models as well as in CT-compatible experimental phantom studies with configurations that mimic coronary vessels. The experimental studies revealed factors that were absent in computational modeling including imaging artifacts and imaging reconstruction kernels where by imaging analysis TAFE has been modified. In addition, computational simulations of the aortic arch including a semi-patient specific aortic valve model were performed to study contrast dispersion through the arch. This study was done to assess a key assumption in TAFE, that the clinically available AIF at the descending aorta can be used as an accurate estimate of the AIF at the coronary ostium.. The work provides support for the ability of TAFE to provide quantitative estimates of coronary flow velocity but also reveals a number of issues that require further assessment for improved accuracy of TAFE

Feasibility of patient specific aortic blood flow CFD simulation

Patient specific modelling of the blood flow through the human aorta is performed using computational fluid dynamics (CFD) and magnetic resonance imaging (MRI). Velocity patterns are compared between computer simulations and measurements. The workflow includes several steps: MRI measurement to obtain both geometry and velocity, an automatic levelset segmentation followed by meshing of the geometrical model and CFD setup to perform the simulations follwed by the actual simulations. The computational results agree well with the measured data