Hey, everybody, it's Rishi Agrawal This video will be all about
MIP images, what they are and what they're used for in lung imaging.
MIP stands for maximum intensity projection.
To understand what
that is, we have to review the difference between a pixel and a voxel.
A pixel is a two dimensional square that you see on your computer screen,
and I've represented one here.
For CT, a pixel is a shade of gray between white and black.
But that pixel is actually a representation
of a three dimensional structure called a voxel.
And for CT, a voxel has a third dimension along the Z axis of the patient.
Which goes in this direction.
That third dimension is the slice thickness.
Most of the time when you're reviewing a CT, you're
reviewing the axial slices using Average intensity projection.
That means that the pixel value that's displayed
is an average of the densities in that voxel.
In this example, I have a vessel going through this voxel,
which is displayed as bright white,
but the reason why the pixel isn't white is because it's
averaging that vessel with the surrounding black lung.
So instead you get a gray value.
On the other hand, with a MIP, the pixel value that's displayed is not
the average intensity of the whole voxel, it's the maximum intensity in that voxel.
Now there's two other common features of MIP images that you will commonly see,
but are not intrinsic to the images themselves.
The first is that these are commonly reconstructed
to be thicker slices than the regular axial images.
So you may normally read the lung at a 2 mm slice thickness,
but your MIP images will probably be more like 8 mm or 10 mm.
The other
feature is that most MIP images you will encounter have a fair amount of overlap,
meaning that even though the slice thickness may be 10 mm,
the slice interval may still be 2 mm.
Again, these two features are not intrinsic to MIPS.
You can have 2 mm MIP images reconstructed without overlap,
but that's not commonly how you will see these made.
Now let's take a look at a real life example.
This is a CT of the chest and I have two series up.
This series on the left is a regular lung reconstruction
with 2 mm slice thickness and no overlap.
This series on the right are the MIP images with a 10 mm
slice thickness and an interval of 2 mm,
which means that each slice moves 2 mm.
So there's 8 mm of overlap between two consecutive slices.
In this example, I'm going to demonstrate the primary purpose of MIPS
in lung imaging--looking for lung nodules.
And first I'm going to scroll through the regular lung recons
and I want you to look for nodules in the left lower lobe.
Now, I'm
going to scroll through the MIP images and I want you to do the same thing.
Look for lung nodules in the left lower lobe.
If you were looking
closely, you probably saw these two nodules
right here, there and there.
But I'll bet they were much easier to see on the MIP images.
And I want to show you why.
First, if we just take a look at the images,
you can see that the vessels are displayed as long branching structures
as opposed to the nodules which look like nodules.
On the lung recons though, if we just have a static image,
it can be difficult to tell the difference between a vessel and a nodule.
For example, is this a vessel right here or is it a nodule?
And if we look over the MIP images,
we can see that that's actually a vessel.
The second reason
why it's easier to see the nodules on the MIPS is because of the overlap.
The nodule showed up on many more slices compared to the regular lung recons.
So let's count how many slices we saw this nodule
right here on the regular lung recons.
OK, I'm going to scroll down now.
We catch a little bit of it there.
So that's one slice,
that's two slices
and then it's gone.
So this nodule right here is only on two slices.
Now let's count how many slices we saw this nodule on the MIPS.
I'm going to scroll up first
so we partially see it there.
That's one, two, three, four,
five, six,
and then it's gone.
So we see it on six images compared to only two images
on the regular lung recons.
Now, there is one big drawback to the MIPS,
and that's when you're looking for nodules close to the hilum.
It's difficult to see those on the MIPS.
And the reason is there's multiple crowded vessels in this area,
so it's easy for a nodule to be obscured in the perihilar region.
Let me show you one other practical application of MIP images.
In this example, the patient has a profusion of small pulmonary nodules.
On the regular lung reconstructions with thin cuts, it may be difficult
to tell the distribution of the pulmonary nodules, but with the MIP images, it
effectively puts all the nodules in a 10 mm slice thickness on the same plane,
making the distribution easier to distinguish on the MIP images.
It's easy to see that these nodules are in a tree-in-bud distribution,
whereas on the regular thin cuts it's not as obvious,
especially if you're just starting out on chest imaging.
To summarize, we talked about two different
applications of MIPS in lung imaging.
The first most commonly used application is the detection of lung nodules
and the second is classification of the distribution of lung nodules.
If you have any questions about this topic or any other chest topic,
let me know in the comments below. Thanks.