Okay, and then how can we reconstruct a non-Cartesian K-space?

I'm not going to talk details about that, but

let me just briefly talk about introduce some representative methods.

So one is using filtered backprojection algorithm which is used for

the computed tomography image reconstruction.

So for the radial acquisition that I just mentioned in the previous slide,

the radial acquisition is almost the same as the CT, okay, acquisition.

So that is also performed along multiple angles.

X-ray imaging is performed along multiple angles for the CT imaging case.

And similar thing is true for the MR imaging for

the radial imaging acquisition.

But that domain is on the frequency domain.

The only difference is the MR acquisition is performed on the frequency domain but

the CT acquisition is preformed on the spatial domain.

So that is the difference.

But we can use very similar algorithm called

a filtered backprojection to reconstruct images.

Or sometimes we can interpolate data to predict the data

points on the Cartesian grid from the data acquired in the non-Cartesian grid, okay?

And then we can use some interpolation algorithm to find the data

points on the Cartesian grid.

And then we can apply two-dimensional Fourier transform to get the images.

That is another way to generate images from the non-Cartesian K-space data.

Okay, this is going to be end of this course, and I thank you, all of you,

to join this course.

And I wish best luck in your future career.

Thank you very much.