Measuring tortuosity of the intracerebral vasculature from MRA images

• Published in: IEEE transactions on medical imaging 2003-09, Vol.22 (9), p.1163-1171
• Main Authors: Bullitt, E., Gerig, G., Pizer, S.M., Weili Lin, Aylward, S.R.
• Format: Article
• Language: English
• Subjects: Associate members; Biological and medical sciences; Biomedical imaging; Brain - blood supply; Brain - pathology; Brain Neoplasms - complications; Brain Neoplasms - diagnosis; Cerebral Angiography - methods; Cerebrovascular Circulation; Cerebrovascular Disorders - diagnosis; Cerebrovascular Disorders - etiology; Data mining; Diseases; Humans; Image analysis; Image Interpretation, Computer-Assisted - methods; Image recognition; Imaging, Three-Dimensional - methods; Magnetic Resonance Angiography - methods; Malignant tumors; Medical diagnostic imaging; Medical sciences; Neovascularization, Pathologic - diagnosis; Neovascularization, Pathologic - etiology; Predictive Value of Tests; Severity of Illness Index; Shape measurement; Two dimensional displays
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2003.816964

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.