Who of you needs an overview on the lecture?

So, would you like to summarize the previous lecture?

Because we're now starting a new chapter.

And should we do a summary of the previous lecture?

Or do you say, okay, I got everything in focus?

Rather not.

Rather not?

Rather yes.

Yeah, should we do a summary of the lecture?

What we've heard so far?

We can vote.

We can vote.

How about a vote?

Some new, some new tool that we could explore in this lecture.

A vote.

So who wants to summarize the lecture so far?

Okay, who would rather skip a summary?

Okay.

Why do you want to skip the summary?

Maybe we have next week another summary.

Or maybe we have next week, who wants to do the summary next week?

It's a pretty clear vote.

Okay, so today we're starting a new chapter.

So because so far, okay, then let's do the summary.

So far we did everything we have discussed so

far was of course interventional medical image processing.

And what we heard so far were a couple of refreshers, right?

So we were talking about a couple of tools.

Who remembers one of these tools?

Yes, you voted against the summary, so you must know everything, right?

SVD.

See, SVD, that's the answer.

Okay, so SVD.

Good.

What's that good for, SVD?

For linear equations.

So can you give an example?

And basically every equation system where we can,

where we need to do inverse for example.

Yes, so what's a famous example for linear equation?

Yes, awesome.

Ax equals, so this is a vector, equals to b.

And then exactly what you said, we solved with the pseudo inverse.

How do we do that?

Well, yes.

Yeah, you take the SVD, okay.

So you multiply from the left side with the pseudo inverse.

And then you get a pseudo inverse here, times the vector b,

and there you go, you have solved for x.

Of course we need to say that x is the unknown, yeah?