So I have one announcement about next week.

We will have to skip next week's Tuesday lecture because I have to go onto a business trip.

So next week we will only have the lecture on Thursday, only disappointment.

Hopefully not disappointment.

Good.

So welcome back to Interventional Medical Image Processing.

And what I would like to do today is discuss a bit scatter, scatter correction, scatter

estimation and later on a very fast algorithm for scatter correction and image processing.

And what I essentially want to show today is that you can simplify a model if you have

certain assumptions that you place into that model and by simplification you can reduce

the amount of complexity in the correction and the necessary corrections that's considerably.

But of course the accuracy of your correction or of your estimation may be reduced.

But if you are working towards a particular task it may be valid to do these assumptions

and to sacrifice accuracy in order to get an improved image quality.

And so I like this example because it shows on the one hand that when you're working with

an imaging device or if you work with an image, the image processing most of the time, so

you solve your image processing problems with image processing methods.

But of course all the image processing that you do is also related to the imaging modality

itself.

And if you want to process an image it's good to have a feeling of the underlying physics.

So we will have some physical equations in this class today but I don't want to emphasize

too much on the physics.

And also concerning this point this is more about that it's good to have an understanding

of the physics but this is not a physics class and none of you is required to have deep in-depth

knowledge about physics.

But if you have a feeling what's happening then you can design much better algorithms

and get a feeling for much more efficient algorithms.

And we will do that by looking at a couple of examples and the example that I'm putting

in here is scatter correction.

So what I want to convey in the class today is that if you have a feeling how stuff works

then you can adopt your image processing methods in a way that you can actually use them more

efficiently.

There's guys out there who don't care about the actual formation of the image.

They just apply image processing and they say an image is an image is an image.

So it's always the same thing but if you understand how your imaging modality works it's always

a pro.

But of course this is not a physics class.

So let's talk a bit about the effect of scatter and in principle when we are talking about

scatter we are often so this example is related to x-ray imaging.

And when you do x-ray imaging you typically have the constellation that you have some

kind of x-ray tube that is positioned here and then you have your patient.

So this is the general setup.

If you attended diagnostic medical image processing you already know this graph.

And then you have an x-ray detector here.

So this is generally how it works.

And what you do is you shoot an x-ray through your patient, through your object.

Often we call this patient then f of x, y.

So this is generally the name of our patients.

They're called f of x, y.

And then we measure the intensity here at our detector.