Measuring tortuosity of the intracerebral vasculature from MRA images

Abstract

The clinical recognition of abnormal vascular tortuosity, or excessive bending, twisting, and winding, is important to the diagnosis of many diseases. Automated detection and quantitation of abnormal vascular tortuosity from three-dimensional (3-D) medical image data would, therefore, be of value. However, previous research has centered primarily upon two-dimensional (2-D) analysis of the special subset of vessels whose paths are normally close to straight. This report provides the first 3-D tortuosity analysis of clusters of vessels within the normally tortuous intracerebral circulation. We define three different clinical patterns of abnormal tortuosity. We extend into 3-D two tortuosity metrics previously reported as useful in analyzing 2-D images and describe a new metric that incorporates counts of minima of total curvature. We extract vessels from MRA data, map corresponding anatomical regions between sets of normal patients and patients with known pathology, and evaluate the three tortuosity metrics for ability to detect each type of abnormality within the region of interest. We conclude that the new tortuosity metric appears to be the most effective in detecting several types of abnormalities. However, one of the other metrics, based on a sum of curvature magnitudes, may be more effective in recognizing tightly coiled, "corkscrew" vessels associated with malignant tumors.

Automatic brain tumor segmentation by subject specific modification of atlas priors1

“…The clinical consequences of the lower level of agreement with the manual raters cannot be fully explored due to the limited number of cases. The results have been used in the analysis of vessel attributes in tumor volumes (21,22). Compared to the results obtained with manual tumor segmentations, we observed no significant difference.…”

Tumor Therapeutic Response and Vessel Tortuosity: Preliminary Report in Metastatic Breast Cancer

“…This earlier study concluded via discriminant analysis of multiple vessel shape parameters that only a combination of two tortuosity metrics appeared effective in generically separating benign from malignant disease [9]. One of these metrics, the "Sum of Angles Metric" (SOAM), sums curvature along a space curve and normalizes by vessel length [10]. The second metric, the "Inflection Count Metric" (ICM), multiplies the total path length of a space curve by the number of inflection points and divides by the distance between endpoints [10].…”

PCA-Based Retinal Vessel Tortuosity Quantification

“…The index may be useful in selecting infants with Plus disease, one of the most important prognostic indicators in ROP. However, the clinical definition of abnormal tortuosity can vary from disease to disease [7]; therefore, extensive testing on a large number of infants with vessels ranging widely in tortuosity is needed to further validate the index. The tortuosity values that have been computed thus far apply to vessel segments.…”

“…However, it failed in differentiating the tortuosity of structures that visually appear to be different in tortuosity. For better accuracy of tortuosity calculation, Bullitt et al [7] generalized Harts estimates to 3D images obtained by means of the Magnetic Resonance Angiography. Measurement of tortuosity using relative length variation was proposed by Kylstra et al [8].…”