Here's the plan for this post. We will complete our look at functions undergoing Fourier transformation; there are some really useful relationships to see and to commit to memory (even when the figures are hand drawn for expediency!). Then we will look at the effects of the FT on real signals. We have two issues to consider: 1) a finite sampling window, and 2) digitization. Off we go!
Fourier pairs
We saw in the last post (Part Five) how a single frequency - a sinusoid - can be represented by a single line - a delta function - in a frequency domain plot. This is an example of a Fourier pair because the relationship holds both ways, i.e. if you take a delta function in the time domain and FT it you get a sinusoid in the frequency domain, and vice versa:
What about other useful Fourier pairs? Here's a useful relationship. An exponential decay Fourier transforms into what's known as a Lorentzian line:
Again, remember that the exponential can be in either the time domain or the frequency domain, although in MRI we generally deal with exponential decays (signals) in the time domain. It's also worth pointing out here that the faster the exponential decay, the broader the Lorentzian line in the other domain. This inverse relationship has a number of practical consequences for fMRI. I'll come back to this point below.
I found an interactive online tool on the National High Magnetic Field Lab's website that allows you to change the rate of decay as well as the frequency of an oscillation in the time domain, and see the resulting Lorentzian line in the frequency domain. Tinker with it here. (It's Java, it takes a couple of seconds to load.)
Next, let's look at what could be arguably the most useful Fourier pair for MRI. A boxcar (or top hat) function Fourier transforms into a sinc function, where sinc(x) = sin(x)/x:
