Lecture 6-5 Non-Cartesian Imaging

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Do curso por Korea Advanced Institute of Science and Technology

MRI Fundamentals

1 classificação

Korea Advanced Institute of Science and Technology

MRI Fundamentals

1 classificação

Welcome! In this course learners will develop expertise in basic magnetic resonance imaging (MRI) physics and principles and gain knowledge of many different data acquisition strategies in MRI. In particular, learners will get to know what is magnetic resonance phenomenon, how magnetic resonance signals are generated, how an image can be formulated using MRI, how soft tissue contrast can change with imaging parameters. Also introduced will be MR imaging sequences of spin echo, gradient echo, fast spin echo, echo planar imaging, inversion recovery, etc.

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6 MRI DATA ACQUISITION STRATEGIES

In this module, you will learn about mri data acquisition strategies. Also, you will be exposed to MR imaging sequences of spin echo, gradient echo, fast spin echo, echo planar imaging, and inversion recovery.

Lecture 6-1 2D and 3D Imaging13:04

Lecture 6-2 Gradient Echo Imaging11:50

Lecture 6-3 Spin Echo Imaging and Fast Spin Echo Imaging17:39

Lecture 6-4 Echo Planar Imaging15:11

Lecture 6-5 Non-Cartesian Imaging9:06

Conheça os instrutores

Sunghong Park
Associate Professor
Department of Bio and Brain Engineering

[MUSIC]

Okay, we'll talk about the non-Cartesian imaging in this video.

So now, Cartesian imaging sequence

is performed when we acquire data in a Fourier domain.

So the acquired data points are directly stored in a Cartesian grid.

So Fourier transform directly generates the imaging.

This Cartesian imaging is performed in most clinical scanners these days

too, okay.

But there are many non-Cartesian imaging sequences.

In this case, K-space trajectory is not on a Cartesian grid.

So in case of Cartesian grid, the samples are shown like as shown here.

So we just acquire data based on frequency encoding and the phase encoding, okay?

That is conventional Cartesian imaging.

But we can acquire data in a slightly different manner

like as shown here in the spiral way or some radial way, okay?

We can acquire data in so many different ways.

So this K-space trajectory can be manipulated by modifying frequency

encoding and phase encoding gradient.

So as I mentioned when we talk about week four.

The integration area of phase encoding and frequency encoding gradient

determines location of that acquired point on the K-space, okay?

So, because of that we can manipulate the location or trajectory of data location.

So we can manipulate a spiral way or a radial way or

a conventional Cartesian way, okay.

They can be manipulated by just changing the gradient

strength in combination with data location.

So most anatomical contrast of an image is determined

by low spatial frequency components like a central portion of the K-space.

So because of that sampling more at the low spatial frequency offers a better

image quality.

So that is generally true.

And in that sense, non-cartesian imaging has some advantage and

it's one of the efficient ways to achieve this,

to take advantage of this method, okay?

So advantage of non-Cartesian imaging is that a faster

acquisition is possible, okay?

You can fill K-space with fewer excitation than the conventional

Cartesian imaging, okay?

So it can be generally used in cardiac MR imaging, okay?

And it's more robust to motion.

And K-space center is sampled every TR, okay, every excitation.

So it provides some averaging effect across motion.

There is no ghost artifact which occurs in EPI imaging.

There is a lot of problem too, But compared to the EPI,

so it's one of the efficient and fast aquisition way,

but the artifact pattern is slightly different from EPI.

So blurring may exist if we acquire data in a non-Cartesian domain.

If the undersampling of the high spatial frequency component or

some gradient imperfections, so

this caused some blurring effect if they are not compensated well okay?

They are not compensated well, okay?

The reconstruction is not straightforward, okay?

That's another disadvantage of non-Cartesian imaging.

And that's why just the Cartesian imaging has been so popular, okay?

Just applying a Fourier transform generates an image, okay?

In case of non-Cartesian imagin, we cannot just directly apply 2D Fourier transform.

We need spatial reconstruction algorithms to get images.

Let's talk about one example of non-Cartesian imaging, the spiral imaging.

So this is pulse sequence diagram.

So after slice-selection and refocusing, we can apply gradient.

We typically apply the phase encoding or readout prephasing in case of Cartesian.

But in case of spiral imaging, it starts from K-space center.

So we can directly start from data location,

from right after the slice refocusing gradient is applied.

And then we can keep acquiring data.

During this data acquisition readout and phase encoding,

both of them, can change in this waveform as shown here.

And then the, Acquired trajectory is going to move like that, okay?

So this is single trajectory and also this is example of multiple trajectories, okay?

And then the K-space can be acquired using single or

interleaved multiple trajectories, which is obvious.

And the single trajectory is more time efficient, okay?

This is one shot EPI, same as one shot EPI.

And this can be considered as corresponds to multishot EPI, okay?

It's similar.

So single shot is more time efficient but

it also is more susceptible to artifacts from field inhomogeneity.

So definitely multishot improves image quality,

much better than the single shot, but it takes longer time.

K-space corners are sometimes ignored because the corners hardly

contribute to the final image.

Okay, this is example of radial K-space sampling.

So excitation and slice refocusing is the same.

But this phasing encoding, prephasing and

readout prephasing is applied all together.

So for instance, if we fill K-space line along this oblique direction

then to move the position here, both readout and phase encoding and

readout, prephasing gradient are necessary to move all the way here.

And then we can read all the way from here to here and

these gradient should be applied together.

So depending on the angle of that K-space lines

these gradient strengths can be manipulated, okay?

This is concept of radial K-space sampling.

So historically, radial K-space sampling was the first data acquisition method

introduced in MR imaging.

Like filter backprojection for the X-ray Computed Tomography CT, okay?

So K-space center can be continuously updated because of this sampling scheme.

K-space sample lines are continuously acquired.

So this is desired to minimize the motion problem for

the dynamic MR imaging like heart or lung, okay?

And there may be some streak artifacts if this number of samples

are not large enough, then there'll be some streak artifacts.

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.

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