Interactive Transcript
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Let's talk short axis 2D imaging.
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2D imaging, the slice thickness, is
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going to be thicker than 3-dimensional
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imaging with reconstruction.
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Typically, in 3D imaging, you're acquiring a
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slab, and your slice thickness is going to
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be as thin as 5, as thick as 2 millimeters.
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In 3D imaging, it's usually about 3 to 3.5
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millimeters, and that's what we used here.
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3.5 millimeters.
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12 00:00:29,110 --> 00:00:32,919 Now, the beauty of 3D imaging is, uber thin
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slices allow you to see Ubersmall tears.
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This is particularly valuable
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when you get into tiny structures.
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And sometimes you don't want to
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see things that are too small.
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Because then you become an
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over-reader and an over-caller.
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But one area where that's incredibly
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valuable, not appropriate here, but I'll
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tell you right now, is in the plantar plate.
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You need uber thin 3D slices to diagnose
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plantar plate tears, and you can do it.
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At any field strength, 0.3, 0.2, 3 tesla.
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27 00:01:05,685 --> 00:01:07,035
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But we're here to talk about the ankle right now.
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And I've got two 2D images surrounding my
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sagittal, just to show you how they're acquired.
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On the left, in the blue corner, is a
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3.5 millimeter proton density fat suppression cut.
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Now, the proton density fat suppression
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is a mainstay of every joint.
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You cannot do joint imaging
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without at least one of these.
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And if tendons, long tendons, are your area of
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interest, you should absolutely have a short
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axis projection to the tendon of interest.
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So if my tendon of interest was,
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let's say, the posterior tibial tendon, let me
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see if I can get my pen to work, and the
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posterior tibial tendon comes down like this,
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then I can get my acquisition like this.
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Or if I'm really super sophisticated,
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I could get my angle of acquisition like this,
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perpendicular, perpendicular, and then I change
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direction as the tendon changes direction.
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Now where can you do that?
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I'm going to show you in a minute with 3D imaging.
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But most of the time, you pick an
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angle that you like that's pretty
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close, and you stay with that angle.
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So, most tears occur in the retromalleolar
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area, so I'll probably pick something that looks
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like this, and I'll take it all the way down.
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Now, one of the beauties of seeing
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these sequences together is that you
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can tell the foot is plantar flexed.
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So when the foot is plantar flexed,
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look at what that does to the tendon.
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It straightens out the tendon.
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It's more like this.
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Whereas if the foot is dorsiflexed, the
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tendon is going to be more like that.
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And that's going to make it very difficult for
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you when you get down here, if you've got a
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series of straight angles going all the way down.
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It's going to get very oblique to the tendon.
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And that makes anatomic diagnosis difficult,
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And it also exacerbates magic angle phenomenon.
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So now let's go back to the sequences themselves.
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PD fat suppression.
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It is the bone scan, the
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detector sequence for everything.
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You're looking for focal, well-defined bright
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spots with associated secondary inflammation.
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So let's just, for instance, take the longus.
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We follow them down, and let's put them up
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on this, um, on this sagittal 3D sequence.
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Which has straightened them out pretty well.
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Not completely, but they're pretty straight.
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And look at our angle.
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Our angle is pretty perpendicular to the tendons.
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There's the brevis, there's the longus,
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there's the calcaneofibular ligament.
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So we have our most sensitive
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sequence, and the tendons are nice
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and black until that spot right there.
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Then the peroneus longus starts to turn white.
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And we have a decision to make.
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Magic angle effect, or real?
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This one's real, but that's
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not why I'm showing it.
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I'm showing it for the sequence.
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You might say, well, okay, you've got this
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lovely, water-sensitive, angled, proton density
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sequence on the left, whose strength is tendons.
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Another strength of it is its
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ability to look at intra-articular
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abnormalities, bodies, defects, masses.
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Cross-sectional imaging, axial imaging,
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is always the best way to look at masses.
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So it has other strengths as well.
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And if you have an abnormality in the
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tendon, another strength of the short
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axis view is that you can grade it.
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25%, 50%, 75%, or rupture.
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So you can tell how much cross-sectional
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area is involved.
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Length, that's the job of the long-axis view.
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But cross-sectional involvement, that's
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the job of the short-axis view.
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Now, on the far right, we've
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got the T2-weighted image.
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It's a T2 without fat suppression, sitting
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next to a proton density with fat suppression.
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Detector, modifier.
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What does it modify?
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Age.
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If something is brighter here and less
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bright here, it's probably more chronic.
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Another valuable aspect of the T2 is
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when things are uber inflamed, swollen,
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ill-defined, buried in a swath of edema.
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The T2 dissipates the edema and cleans up the
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area so you can see the structure of interest.
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So it helps you hone in on a
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structure that may be obscured.
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For instance, let's take another
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strength of the short axis view.
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One of its great strengths, collaterals.
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Here we've got our anterior talofibular ligament.
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It's a little ill-defined.
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It's a little thin.
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There's some swelling around
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it, anterior to the fibular.
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But on the T2, varshi blows.
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It's a delicate, but pretty
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smooth, well-defined structure.
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We've got it.
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That's how you use the T2 relative to the PD.
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You want to go up higher and see another ligament?
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We've got the anterior tib fib ligament.
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This time we see it very well.
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And on the far right, we also see it very well.
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T2 modifier, PD detector.
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Now when you're looking at
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intrasubstance abnormalities of the
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tendons, this is tremendously helpful.
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Because it will tell you the cross-sectional
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area. And it'll help clean up lots of edema
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that may surround the tendon and give you
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a better feel for the percent involvement
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of that tendon in the cross-section.
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There are some other valuable aspects
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of short-axis imaging like looking at
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entrapment neuropathies and nerves, etc.
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But that, it will be a story for another day.
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But to summarize, the great strengths
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anatomically of the short axis view
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are masses, entrapment neuropathy,
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tendons, OCDs, bodies, and collaterals.
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The strength of these two
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sequences we have outlined.
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Detection, refinement.
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Let's move on, shall we?
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