Understand K-Space: How Does the White Circle Form?

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Discussion Overview

The discussion revolves around the formation of a white circle in k-space images, specifically how different representations of k-space contribute to this phenomenon. Participants explore the properties of Fourier transforms and their implications in imaging, with a focus on the technical aspects of k-space and its transformations.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant expresses confusion about how the white circle is formed from k-space images, questioning the addition of pixel values and the presence of a black background.
  • Another participant clarifies that only one image represents k-space, while others represent different spaces, suggesting that they should not be combined.
  • There is a discussion on the separability of the Fourier transform, indicating that transforming in one direction leads to a different representation in k-space.
  • A participant questions why patterns appear in the image after decoding k-space, suggesting that a uniform signal should result instead.
  • Another participant introduces the concept of the sinc function and its relationship to boxcar functions, explaining how different rows in the image domain lead to varying sinc functions in k-space.
  • There is an acknowledgment of the varying intensity in k-space images being related to the relative amplitude and width of the sinc functions.

Areas of Agreement / Disagreement

Participants generally agree on the properties of the Fourier transform and its separability, but there remains some uncertainty regarding the interpretation of the resulting patterns in k-space and how different spaces interact.

Contextual Notes

Some participants reference specific resources and literature related to MRI and k-space, indicating a reliance on external materials for deeper understanding.

Who May Find This Useful

This discussion may be useful for individuals interested in MRI technology, Fourier transforms, and the mathematical underpinnings of imaging techniques, particularly engineers and physicists involved in research and development.

BobP
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kspace.png


In this image the circle in the top left is the original. Next to it lies its K-space. We then see the result of running through k-space vertically and horizontally and then the combination of these two views.

I do not understand how the white circle can be reproduced. I though we would be adding pixel values in the two k-space data sets hence there should not be a black baground. This is obviously now what is happening. Please can someone explain how the two stripy images form the white circle. thanks
 
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I believe that this is an image from Denis Hoa's website. You should probably give a link or a citation. It is an excellent resource.

On this slide there is only one picture of k-space, the one on the top row, middle column. The picture on the top row right column and bottom row middle column are NOT pictures of k-space. They don't really have a name, but they would be called something like x*ky space and kx*y space. You would never add them together.
 
DaleSpam said:
I believe that this is an image from Denis Hoa's website. You should probably give a link or a citation. It is an excellent resource.

On this slide there is only one picture of k-space, the one on the top row, middle column. The picture on the top row right column and bottom row middle column are NOT pictures of k-space. They don't really have a name, but they would be called something like x*ky space and kx*y space. You would never add them together.
Absolutely. here is the link if anyone wants it
http://www.revisemri.com/tutorials/what_is_k_space/

What my question meant was how would you combine the "x*ky space and kx*y space" to produce the image? Thank you
 
BobP said:
What my question meant was how would you combine the "x*ky space and kx*y space" to produce the image? Thank you
You don't combine them.

So, the Fourier transform has the property that it is separable. In keeping with my above notation k-space is kx*ky space. If you Fourier transform in the X direction then you go from kx*ky space to x*ky space. If you then apply another Fourier transform, but in the y direction then you go from x*ky space to x*y space, which is the image.
 
DaleSpam said:
You don't combine them.

So, the Fourier transform has the property that it is separable. In keeping with my above notation k-space is kx*ky space. If you Fourier transform in the X direction then you go from kx*ky space to x*ky space. If you then apply another Fourier transform, but in the y direction then you go from x*ky space to x*y space, which is the image.

OK so once we have decoded kx space --> x*ky space shouldn't we have a uniform signal at a particular x-value across the whole of y. My reasoning for this is that we know there is a y signal from ky but we don't know where it is. I don't understand why we see fancy patterns?
Thank you for your help by the way
 

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Are you familiar with the sinc function and how it Fourier transforms into a boxcar function?

If you go the other way it may be easier to understand. Start from the image domain and Fourier transform each row to go to the kx*y domain.

If the row is all zeros then it Fourier transforms to all zeros. Otherwise the row looks like a boxcar function so it Fourier transforms to a sinc function.

Different rows will have different boxcar widths and therefore different sinc functions. The "fancy pattern" is just all of those sinc functions put together.
 
DaleSpam said:
Are you familiar with the sinc function and how it Fourier transforms into a boxcar function?

If you go the other way it may be easier to understand. Start from the image domain and Fourier transform each row to go to the kx*y domain.

If the row is all zeros then it Fourier transforms to all zeros. Otherwise the row looks like a boxcar function so it Fourier transforms to a sinc function.

Different rows will have different boxcar widths and therefore different sinc functions. The "fancy pattern" is just all of those sinc functions put together.

Oh I see. So in any given row, the varying intensity on the figure displaying the kx*y domain arises because of the relative amplitude of the sinc function at the position in kx space?
 
BobP said:
Oh I see. So in any given row, the varying intensity on the figure displaying the kx*y domain arises because of the relative amplitude of the sinc function at the position in kx space?
Yes, relative amplitude and width.
 
DaleSpam said:
Yes, relative amplitude and width.
Ah. thanks so much!
 
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You are welcome. By the way, D M Higgins site is an excellent resource as is Denis Hoa's site
https://www.imaios.com/en/e-Courses/e-MRI/The-Physics-behind-it-all

If you are an engineer or physicist looking to do MRI research and development then the Haacke, Brown, Thompson, and Venkatesan book is great. If you are a radiologist or technologist looking to understand MR physics then the Dale, Brown, Semelka book is excellent.
 
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