So if you are interested in one of the two jobs that I announced or even more, just the

two fields, we need as many students as we can get.

Just send me a brief email and I will come up with a link and I will link you to the

PhD students or the former employees of my lab and you can directly get in touch with

them and discuss the details.

Really?

It all starts again on 7th of January.

And then we just have three weeks left or something like that, right?

That's not good.

We are currently talking about reconstruction.

So good morning.

Yes?

You just talked about one job about the mechanical 3D camera thing.

No and I talked about a Siemens job.

A Siemens job where I don't know what exactly has to be done.

It's software related because the guy who is supervising it is a computer scientist or

a computational engineer that learned the real stuff in my lab.

Okay and it's about surgery, imaging applications within surgery.

There is a new, ramping up a new activity and we are looking for brilliant students

and our experiences that the brilliant ones are in this lecture here.

That's why I'm on it.

And international students, no problem at all.

You all know the working language of the engineer, also in industry, also in spin-offs, it's

just broken English.

Good.

So we are currently talking in broken English with a German-Franconian accent about reconstruction.

And last Monday I was leaving the lecture rather frustrated because at some point I

had the feeling that I totally lost you in your imagination what's going on.

So let me briefly restart the basic concepts, the milestones in the development and then

we will continue to look at three more classes of reconstruction algorithms.

One is the cone beam reconstruction, so-called FDK reconstruction algorithm where we use

a flat panel detector.

The second one is Grunjad's algorithm that is making use of derivatives with respect

to the motion of the camera or the x-ray system.

And the third algorithm is the so-called cut-savage algorithm that is an artifact-free exact reconstruction

method that is highly patented and that's the reason why it's not used in industry currently.

It's also interesting to learn about these links that if it's too much patented, people

always try to find a workaround.

So if you aggressively approach the big ones in the market that build the scanners with

a cool algorithm and with the patents in the background, you are not yet rich.

So there is still some more work to be done to make it applicable in the field.

So patents, reconstruction.

What do we need to know about reconstruction?

The first thing is we need to have a mental or a graphical understanding what's going

on.

We have our x-ray source.

It's always the same.

There we produce x-rays.

From a physics point of view, we know what's happening there.

We have our x-ray tube and the x-ray quanta.