Blood flow rate is a critical parameter for diagnosing dialysis access function during fistulography where a flow rate of 600 ml/min in arteriovenous graft or 400-500 ml/min in arteriovenous fistula is considered the clinical threshold for fully functioning access. In this study, a flow rate computational model for calculating intra-access flow to evaluate dialysis access patency was developed and validated in an in vitro set up using digital subtraction angiography. Flow rates were computed by tracking the bolus through two regions of interest using cross correlation (XCOR) and mean arrival time (MAT) algorithms, and correlated versus an in-line transonic flow meter measurement. The mean difference (mean ± standard deviation) between XCOR and in-line flow measurements for in vitro setup at 3, 6, 7.5 and 10 frames/s was 118±63; 37±59; 31±31; and 46±57 ml/min respectively while for MAT method it was 86±56; 57±72; 35±85; and 19±129 ml/min respectively. The result of this investigation will be helpful for selecting candidate algorithms while blood flow computational tool is developed for clinical application.
KEYWORDS: 3D modeling, Image segmentation, Chromium, 3D image processing, Calcium, 3D metrology, Surgery, Angiography, Medical imaging, Image processing
Aortic Aneurysms (AA) are the 13th leading cause of death in the US. In standard clinical
practice, intervention is initiated when the maximal diameter cross-sectional reaches
5.5cm. However, this is a 1D measure and it has been suggested in the literature that
higher order measurements (area, volume) might be more appropriate clinically.
Unfortunately, no commercially available tools exist for extracting a 3D model of the
epithelial layer (versus the lumen) of the vessel. Therefore, we present work towards
semi-automatically recovering the aorta from CT angiography volumes with the aim to
facilitate such studies.
We build our work upon a previous approach to this problem. Bodur et. al.,
presented a variant of the iso-perimetric algorithm to semi-automatically segment several
individual aortic cross-sections across longitudinal studies, quantifying any growth. As a
by-product of these sparse cross-sections, it is possible to form a series of rough 3D
models of the aorta.
In this work we focus on creating a more detailed 3D model at a single time point
by automatically recovering the aorta between the sparse user-initiated segmentations.
Briefly, we fit a tube model to the sparse segmentations to approximate the cross-sections
at intermediate regions, refine the approximations and apply the isoperimetric algorithm
to them. From these resulting dense cross-sections we reconstruct our model. We applied
our technique to 12 clinical datasets which included significant amounts of thrombus.
Comparisons of the automatically recovered cross-sections with cross-sections drawn by
an expert resulted in an average difference of .3cm for diameter and 2cm^2 for area.
KEYWORDS: Arteries, Image segmentation, Spherical lenses, Visualization, Magnetic resonance imaging, Computed tomography, 3D modeling, Visual process modeling, Ultrasonography, Ray tracing
It is standard practice for physicians to rely on empirical, population based models to define the relationship
between regions of left ventricular (LV) myocardium and the coronary arteries which supply them with
blood. Physicians use these models to infer the presence and location of disease within the coronary arteries
based on the condition of the myocardium within their distribution (which can be established non-invasively
using imaging techniques such as ultrasound or magnetic resonance imaging). However, coronary artery
anatomy often varies from the assumed model distribution in the individual patient; thus, a non-invasive
method to determine the correspondence between coronary artery anatomy and LV myocardium would have
immediate clinical impact. This paper introduces an image-based rendering technique for visualizing maps of
coronary distribution in a patient-specific approach. From an image volume derived from computed
tomography (CT) images, a segmentation of the LV epicardial surface, as well as the paths of the coronary
arteries, is obtained. These paths form seed points for a competitive region growing algorithm applied to the
surface of the LV. A ray casting procedure in spherical coordinates from the center of the LV is then
performed. The cast rays are mapped to a two-dimensional circular based surface forming our coronary
distribution map. We applied our technique to a patient with known coronary artery disease and a qualitative
evaluation by an expert in coronary cardiac anatomy showed promising results.
KEYWORDS: Image segmentation, 3D modeling, Image processing algorithms and systems, Prototyping, Tissues, Calcium, Medical imaging, Visualization, Data modeling, Finite element methods
Aortic aneurysms are the 13th leading cause of death in the United States. In
standard clinical practice, assessing the progression of disease in the aorta, as well as
the risk of aneurysm rupture, is based on measurements of aortic diameter. We
propose a method for automatically segmenting the aortic vessel border allowing the
calculation of aortic diameters on CTA acquisitions which is accurate and fast,
allowing clinicians more time for their evaluations. While segmentation of aortic
lumen is straightforward in CTA, segmentation of the outer vessel wall (epithelial
layer) in a diseased aorta is difficult; furthermore, no clinical tool currently exists to
perform this task. The difficulties are due to the similarities in intensity of
surrounding tissue (and thrombus due to lack of contrast agent uptake), as well as the
complications from bright calcium deposits.
Our overall method makes use of a centerline for the purpose of resampling
the image volume into slices orthogonal to the vessel path. This centerline is
computed semi-automatically via a distance transform. The difficult task of
automatically segmenting the aortic border on the orthogonal slices is performed via
a novel variation of the isoperimetric algorithm which incorporates circular
constraints (priors). Our method is embodied in a prototype which allows the loading
and registration of two datasets simultaneously, facilitating longitudinal
comparisons. Both the centerline and border segmentation algorithms were evaluated
on four patients, each with two volumes acquired 6 months to 1.5 years apart, for a
total of eight datasets. Results showed good agreement with clinicians' findings.
One of NASA’s objectives is to be able to perform a complete pre-flight evaluation of possible cardiovascular changes in astronauts scheduled for prolonged space missions. Blood flow is an important component of cardiovascular function. Lately, attention has focused on using computational fluid dynamics (CFD) to analyze flow with realistic vessel geometries. MRI can provide detailed geometrical information and is the only clinical technique to measure all three spatial velocity components. The objective of this study was to investigate the reliability of MRI-based model reconstruction for CFD simulations. An aortic arch model and a carotid bifurcation model were scanned in a 1.5T MRI scanner. Axial MRI acquisitions provided images for geometry reconstruction using different resolution settings. The vessel walls were identified and the geometry was reconstructed using existing software. The geometry was then imported into a commercial CFD package for meshing and numerical solution. MRI velocity acquisitions provided true inlet boundary conditions for steady flow, as well as three-directional velocity data at several locations. In addition, an idealized version of each geometry was created from the model drawings. Contour and vector plots of the velocity showed identical features between the MRI velocity data, the MRI-based CFD data, and the idealized-geometry CFD data, with mean differences <10%. CFD results from different MRI resolution settings did not show significant differences (<5%). This study showed quantitatively that reliable CFD simulations can be performed in models reconstructed from MRI acquisitions and gives evidence that a future, subject-specific, computational evaluation of the cardiovascular system is possible.
Post myocardial infarction, the identification and assessment of non-viable (necrotic) tissues is necessary for effective development of intervention strategies and treatment plans. Delayed Enhancement Magnetic Resonance (DEMR) imaging is a technique whereby non-viable cardiac tissue appears with increased signal intensity. Radiologists typically acquire these images in conjunction with other functional modalities (e.g., MR Cine), and use domain knowledge and experience to isolate the non-viable tissues. In this paper, we present a technique for automatically segmenting these tissues given the delineation of myocardial borders in the DEMR and in the End-systolic and End-diastolic MR Cine images. Briefly, we obtain a set of segmentations furnished by an expert and employ an artificial intelligence technique, Support Vector Machines (SVMs), to "learn" the segmentations based on features culled from the images. Using those features we then allow the SVM to predict the segmentations the expert would provide on previously unseen images.
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