Perfect.

Hey.

So how are the exercises going?

Everybody enjoyed them?

Are they too hard?

Are they too easy?

Just checking in to get a little bit of a feeling.

Who found them too easy?

Nobody?

Who found them too hard?

So everybody thinks they are perfect.

That's great.

Yeah, you probably are all done by now, right?

And just going to upload them.

This week, we're going to continue

with the second set of what I would consider

the introductory lectures.

So last week, we talked about the MR acquisition process.

And we ended with the case-based Fourier representation.

So you knew how you got your measurement data

in the Fourier space based on the encoding of the gradient.

So that's where we left off.

And this week, we're going to talk about properties

of Fourier imaging.

So it's all going to be about the Fourier transform

and how we manipulate and what the properties of our Fourier

data have.

And the exercise is going to be exactly in the same direction.

So you're going to play around with case-based trade-off

of resolution and ringing artifacts, the sampling theory,

things like that.

And then after today's lecture and this week's

exercise, then you are equipped with all the basics.

And then we can switch to the algorithmic components, where

you will actually code algorithms, where you

reconstruct images from not full Fourier data.

You're going to introduce some constraints.

You're going to exploit symmetries

and all these type of things.

But today, again, it's more introductory.

And the exercise also will be fairly basic again.

So no algorithm coding so far.

So before we get started, I just want

to point out two administrative details.

So first of all, if you look in Stodon,

then you may have seen that we have added

some new features and elements.

First, I want to talk about is the booking for the computer

exercises.

So last week, no, not last week, because the week before,