Open AccessIntensity‐curvature functional‐based filtering in image space and k‐space: Applications in magnetic resonance imaging of the human brain
Author(s) -
Ciulla Carlo
Publication year - 2019
Publication title -
high frequency
Language(s) - English
Resource type - Journals
ISSN - 2470-6981
DOI - 10.1002/hf2.10031
Subject(s) - k space , curvature , space (punctuation) , filter (signal processing) , magnetic resonance imaging , functional magnetic resonance imaging , intensity (physics) , physics , mathematics , computer science , artificial intelligence , computer vision , mathematical analysis , optics , geometry , fourier transform , medicine , radiology , operating system
This research examines the use of the intensity‐curvature functional (ICF) as filter in image space and in k‐space. The novelty of this study is three‐folded: (a) The evidence that the ICF calculated from three additional (International Journal of Imaging Systems and Technology, 28, 2018, 54) two‐dimensional model polynomial functions is an image space filter; (b) An additional (The use of the intensity‐curvature functional as k‐space filter: Applications in magnetic resonance imaging of the human brain, 2018) ICF‐based k‐space filtering technique applicable to two‐dimensional magnetic resonance images; (c) Results obtained through the calculation of the ICF of the trivariate cubic Lagrange model polynomial function (LGR3D). Although ICF‐based k‐space filtering delivers clear and well‐defined images, ICF‐based image space filtering remains superior when reconstructing vessel images in T2 MRI. The ICF of the LGR3D function provides sharp images too.