Improved characterisation of aortic tortuosity

Tortuosity can be described as the variation in blood vessel curvature. Abnormal tortuosity is an important clinical indicator of various conditions. Despite considerable research, there has been very little agreement on an accurate, unique measure of this phenomenon for clinical applications. It has been demonstrated that a single value is insufficient to describe vessel tortuosity. In this work, the fast Fourier transform of the vessel's curvature as a measure of tortuosity is introduced. Spectral analysis of a suite of computed-simulated vessels, a phantom and clinical data is carried out. Observation of the acquired spectra permits detection of the local curvature variations. Spectral analysis of curvature provides a compact and graphic representation of tortuosity. This paper also describes two new highly automated MATLAB algorithms for obtaining the vessel centrelines: a heuristic image processing algorithm, and an algorithm based on the probabilistic Hough transform. We demonstrate the accuracy of both algorithms comparing with a manual method to extract the vessel centreline. Both algorithms reduce potential errors and user time and only require the manual selection of one centroid.