The use of the intensity‐curvature measurement approaches: Applications in magnetic resonance imaging of the human brain

Abstract

This research describes signal processing techniques useful to highlight and contrast enhance human brain vessels detected with magnetic resonance imaging (MRI). The signal is here represented as multiplication between image intensity and the sum of second‐order partial derivatives of the model function fitted to MRI data. The implication of this novel and different concept is the introduction of the new variable called intensity‐curvature. The following six intensity‐curvature measurement approaches (ICMAs) can hence be calculated: classic‐curvature, intensity‐curvature functional (ICF), signal resilient to interpolation, resilient curvature, and intensity‐curvature terms (ICTs) before and after interpolation. This paper explores the use of ICMAs with applications in human brain MRI. Studying vasculature of the human brain is possible through two techniques apt to measure human brain MRI intensity‐curvature. The first technique is the inverse Fourier transformation procedure, which uses as k‐space filters the ICTs before and after interpolation. The other technique is called intensity‐curvature masking procedure, and it raises each ICMA to a power determining a nonlinear combination of ICMAs to sum up to MRI image intensity on a pixel‐by‐pixel basis. This paper reports that ICTs are k‐space filters, ICF is a high‐pass filter, and ICMAs are mask images. The major implication of these methodologies is effective k‐space filtering. The techniques here presented support visualization of human brain vessels. The advantage of these methods relies in speed of computation.