Education, tips and tricks to help you conduct better fMRI experiments.
Sure, you can try to fix it during data processing, but you're usually better off fixing the acquisition!

Wednesday, June 15, 2011

Physics for understanding fMRI artifacts: Part Five

An introduction to the Fourier transform - what it does and how it works

Moving along from bathroom design and complex numbers, it's time to look at one of the most fundamental mathematical relationships underpinning all of modern MRI: the Fourier transform (FT). Accordingly, I would strongly suggest that you make sure you fully understand everything in this post before continuing into future posts.

You may not believe it by looking at us, but take it from me that MR physicists do "mental FTs" every day as we compare pulse sequences, try to understand artifacts and so on. Many of us (me) are not mathematically inclined, either. But being able to switch your mind between domains, as they are called, is a really useful skill if you want to be good at artifact recognition. An MR physicist will see something in one domain and will immediately try to imagine how it would appear in the alternate domain. Thus, if an image contains an artifact - an intense stripe, say - the MR physicist tries to imagine what signal-acquisition domain feature is implied by it, then tries to track it down. Alternatively, when comparing two different pulse sequences - GRAPPA on versus GRAPPA off, perhaps - the MR physicist will project the implications of each into the image domain in order to comprehend the likely practical consequences. You don't have to remember the equations or even understand the maths itself, but it is really useful to grasp the concepts!

Fourier analysis: the art of decomposition

As you have seen in your introductory texts and in previous video lectures, MRI signals are actually time-varying voltages that are induced in receiver coils as a result of magnetization oscillating (precessing) about the polarizing magnetic field. The detection of time-varying signals has several implications for obtaining MR images, the very first of which is choosing a representation for the information content in the signals.

Consider the two waveforms on the left-hand side of the figure below, which have the same amplitude but differ in their frequency:

Courtesy: Karla Miller, FMRIB, University of Oxford.

It is possible in principle - and in practice for simple examples such as these - to take a ruler and measure the amplitude and frequency and then draw a graph representing the time varying signals as a plot of amplitude (along y) against frequency (along x), as shown on the right-hand side of the above figure. Piece of cake, right?