Virtual Intraluminal Endoscopy

Virtual intraluminal endoscopy (VIE) is a recently developed technique for assessing the inside of the vascular wall (Fig. 9). It combines the features of endoscopic viewing and cross-sectional volumetric imaging and involves the generation of a sequence of perspective views calculated from points (flight path) located within the vascular lumen. These views can be computed using both surface rendering and volume rendering algorithms. In the most current systems, the flight path is automatically calculated, based on a preliminary extraction of the vessel centerline. Despite being a high-level method of image post-processing, VIE is not yet a popular application. Most errors and artifacts are related to the somewhat arbitraryna-ture of data thresholding. The goal of VIE is to define aset of thresholds that will perfectly define the

Fig. 8. Volume rendered view of the aortic arch and branches

Pseudo Aneursym Aorta
Fig. 7. Volume rendered view of a pseudo aneurysm of the descending aorta

Fig. 8. Volume rendered view of the aortic arch and branches

Descending Aorta And Branches
Fig. 9. Virtual endoscopy of the abdominal aorta guided by the vessel centerline (red line)

edge of thevessel while excluding everything else. This is most likelyto occur when the 3D data set is optimal (i.e. signal intensity within the vessel is uniformly high and signal intensity outside the vessel uniformly low). The most common error is trying togenerate VIE images from suboptimal data sets with threshold problems. This leads to two typical problems: holes in vessel walls, which can be mistakenfor the origins of small vessels such as accessory renal arteries, and floating shape artifacts within the vessel lumen. As yet there is no corresponding clinical technique and no clear evi-dencethat this form of data presentation provides any advantages over traditional methods. Nevertheless, MR-generated VIE can produce striking endovascular images, and its potential importance as a clinical tool should not be underestimated. The techniqueis still relatively new, and important clinical applicationsmay yet be demonstrated.

Vascular Analysis

The purpose of vascular analysis is to allow the clinician to perform quantitative assessment of vessel morphology in order: 1) to decide on the appropriate approach to treatment (surgical or pharmacological) according to the degree of stenosis, and 2) to monitor the progress of the disease. In-traluminal diameters and cross-sectional areas are needed to accurately quantify the degree of stenosis. Traditionally, only diameters were used for the estimation of stenosis, with the degree of stenosis calculated according to the following equation:

Ddistal - Dmin

Ddistal where Dmin = the smallest diameter within the stenosis and Ddhtal = the diameter of the normal vessel beyond the diseased segment. Unfortunately, for non-elliptic or amorphous stenoses, estimation of the smallest (as well as of the largest) diameter is not clear. Moreover, some authors argue that, in this case, cross-sectional area reduction better correlates with the hemodynamic impact of the stenosis than diameter reduction. For these reasons, vessel cross-sectional area has been proposed as a more accurate parameter for stenosis calculation [22].

Most post-processing systems provide interactive operator tools to manually perform these measurements. These tools considerably increase the accuracy of stenosis quantification compared to a purely visual appreciation. However, manual tracing of the lumen centerline as well as delineation of vessel boundaries are time-consuming tasks and are subject to variability between operators. These major drawbacks have thus been the motivation for the development of new techniques for automatic quantification of the vascular morphology.

In terms of image processing, the first step necessary to quantitatively evaluate a vessel is to separate it from surrounding structures. This procedure is called "segmentation". Two main approaches to vessel segmentation have been suggested. The first approach relies on purely photometric criteria and focuses mainly on thresholding and region-growing techniques [23-26]. The major advantage of this technique is that it is reasonably easy to implement. However, an additional modeling step is necessary to extract meaningful measurements from the segmented images.

The second approach exploits the geometrical specificity of the vessels, in particular their orien

Fig. 10. Automatic quantification along the vessel centerline

Vessel contour detection

Vessel contour detection

Automatic Quantification > L'russĀ» sectional iri

MannMente curves Sceiusis degree (^fe)

Automatic Quantification > L'russĀ» sectional iri

MannMente curves Sceiusis degree (^fe)

tation and tubular shape. Techniques that use this approach tend to use vessel-tracking [27-31] and (often implicitly) a generalized-cylinder model, i.e. an association of an axis (centerline) and a surface (vessel wall) [32-33]. Consequently, the segmentation process involves two tasks: centerline extraction and vessel contour detection in the planes perpendicular to the axis. This procedure results in a stack of 2D contours along the vessel (Fig. 10) which allow quantitative cross-sectional measurements and visualization by means of triangula-tion-based surface rendering. Other recent approaches have looked at using 3D models of the vessel surface [34,35].

Was this article helpful?

0 0
Essentials of Human Physiology

Essentials of Human Physiology

This ebook provides an introductory explanation of the workings of the human body, with an effort to draw connections between the body systems and explain their interdependencies. A framework for the book is homeostasis and how the body maintains balance within each system. This is intended as a first introduction to physiology for a college-level course.

Get My Free Ebook


Post a comment