An Algorithm for MR Angiography Image Enhancement

Abstract

This study describes a three-dimensional (3D) filtering algorithm for magnetic resonance angiography (MRA) image enhancement. Based on the properties of 3D MRA data, the algorithm uses the dispersion range of the 13 outputs of a 3D directional low-pass filter bank to identify the vessel structure and control the processing of MRA data. Vascular structures in the data are preserved while the components of static tissues and isolated noise impulses are reduced by the processing. As a result, the small vessels have been enhanced and visibility of vessels in the projected image has been improved.

Multiscale vessel enhancement filtering

“…Vo,s and 7~o,s are the gradient vector and Hessian matrix of the image computed in xo at scale s. To calculate these differential operators of L in a well-posed fashion we use concepts of linear scale space theory [5,6]. In this framework differentiation is defined as a convolution with derivatives of Gaussians: (2) where the D-dimensional Gaussian is defined as:…”

Model-based quantitation of 3-D magnetic resonance angiographic images

“…A number of approaches analyze the images at a single scale [10]- [16], which limits their applicability to images in which the range of vessel widths is small. This is probably the reason for the observation made in [10], [11], and [13] that small vessels are better enhanced than large vessels. A number of papers have acknowledged the importance of multiscale algorithms that can cope with vessel width variability [17]- [26].…”

Segmentation of arteries in MPRAGE images of the ventral medial prefrontal cortex

“…Many of these solutions are single scale [12][13][14][15][16][17][18]. Several multiscale methods exist as well [19][20][21][22].…”