What is it about?
The paper is an additional extension to the methodology illustrated in: https://doi.org/10.1002/ima.22256 used to create high pass filters from model polynomial functions fitted to MRI data of the human brain. The paper illustrates the mathematics of the ICF based high pass filters and the methodology to create ICF based k-space filters.
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Why is it important?
The importance is in to show that ICF is an image and a k-space filter. This concept is shown to be extendible to a variety of model polynomial functions fitted to MR images. This work uses once again the intensity-curvature concept which merges the image intensity and the second order partial derivatives of the model polynomial function into a new domain: intensity-curvature.
Perspectives
This work needs to be framed within the context determined by earlier work: https://doi.org/10.1002/ima.22256 and extends the features of the intensity-curvature functional (ICF) from an image space filter to a k-space filter. Results of k-space filtering are illustrated for several model polynomial functions.
Dr. Carlo Ciulla
Western Balkans University
Read the Original
This page is a summary of: Intensity‐curvature functional‐based filtering in image space and k‐space: Applications in magnetic resonance imaging of the human brain, High Frequency, February 2019, Wiley,
DOI: 10.1002/hf2.10031.
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