And what we're going to do now is we do an interactive summary of the lecture.

So I will start.

And so something which is a common question when you go to a lecture, you may ask yourself,

well, what is this lecture all about?

Can we do a summary of the lecture?

Well, let's see if we can do a summary.

And if we start with the summary, the first thing we may want to point out is that this

lecture was called Interventional Medical Image Processing.

And it was a nice lecture, but there was also some criticism.

So it's no sun, but it's slightly cloudy.

So we have a cloud here.

No, it's not raining.

It's only slightly cloudy.

But this is our cloud.

And now we can think about the content of the lecture.

So what did we talk about in this lecture?

Who remembers?

There's all exams in two weeks.

Tools.

Tools, yes.

Tools.

Tools.

What kind of tools do you remember?

SVD.

SVD.

So what's SVD good for?

Everything.

Everything.

Can you say some examples what SVD is good for?

Solving linear equations.

Yes?

Solving linear problems.

Can you think of a linear problem that you want to solve now with SVD?

No.

No, you don't know.

What's a linear problem?

Can you tell me a linear problem?

A times x equals b.

Excellent.

Linear problem.

This is an excellent example.

This is all applied.

You immediately realize the relation to the medical imaging here.

Yeah?

A times x equals b.

So this is why our lecture is very applied, because we have these applied examples.

OK.

What can we do now with SVD?

We can get the pseudo inverse at x.

So we want to know x.