Last revised by Andrew Murphy on 2 Apr 2020

k-space is an abstract concept and refers to a data matrix containing the raw MRI data. This data is subjected to mathematical function or formula called a transform to generate the final image. A discrete Fourier or fast Fourier transform 1-3 is generally used though other transforms such as the Hartley 4 can also work.


A single slice corresponds to a k-space plane acquired in real-time. Each point on the k-space contains specific frequency, phase (x,y coordinates) and signal intensity information (brightness). Inverse FT is applied after k-space acquisition to derive the final image. Each pixel in the resultant image is the weighted sum of all the individual points in the k-space. Hence, disruption of any point in the k-space translates into some form of final image distortion, determined by the frequency- and phase-related data stored in that particular point.  In general:

  • central regions of the k-space encode contrast information
  • peripheral regions of the k-space encode spatial resolution


Knowledge of the k-space is essential as it relates to different techniques of image acquisition and explains several MRI artifacts.

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