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I will start by posing a little bit of a challenge:

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the challenge of dealing with data,

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data that we have to deal with

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in medical situations.

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It's really a huge challenge for us.

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And this is our beast of burden --

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this is a Computer Tomography machine,

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a CT machine.

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It's a fantastic device.

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It uses X-rays, X-ray beams,

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that are rotating very fast around the human body.

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It takes about 30 seconds to go through the whole machine

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and is generating enormous amounts of information

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that comes out of the machine.

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So this is a fantastic machine

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that we can use

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for improving health care,

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but as I said, it's also a challenge for us.

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And the challenge is really found in this picture here.

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It's the medical data explosion

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that we're having right now.

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We're facing this problem.

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And let me step back in time.

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Let's go back a few years in time and see what happened back then.

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These machines that came out --

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they started coming in the 1970s --

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they would scan human bodies,

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and they would generate about 100 images

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of the human body.

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And I've taken the liberty, just for clarity,

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to translate that to data slices.

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That would correspond to about 50 megabytes of data,

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which is small

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when you think about the data we can handle today

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just on normal mobile devices.

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If you translate that to phone books,

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it's about one meter of phone books in the pile.

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Looking at what we're doing today

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with these machines that we have,

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we can, just in a few seconds,

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get 24,000 images out of a body,

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and that would correspond to about 20 gigabytes of data,

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or 800 phone books,

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and the pile would then be 200 meters of phone books.

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What's about to happen --

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and we're seeing this; it's beginning --

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a technology trend that's happening right now

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is that we're starting to look at time-resolved situations as well.

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So we're getting the dynamics out of the body as well.

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And just assume

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that we will be collecting data during five seconds,

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and that would correspond to one terabyte of data --

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that's 800,000 books

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and 16 kilometers of phone books.

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That's one patient, one data set.

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And this is what we have to deal with.

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So this is really the enormous challenge that we have.

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And already today -- this is 25,000 images.

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Imagine the days

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when we had radiologists doing this.

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They would put up 25,000 images,

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they would go like this, "25,0000, okay, okay.

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There is the problem."

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They can't do that anymore. That's impossible.

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So we have to do something that's a little bit more intelligent than doing this.

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So what we do is that we put all these slices together.

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Imagine that you slice your body in all these directions,

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and then you try to put the slices back together again

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into a pile of data, into a block of data.

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So this is really what we're doing.

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So this gigabyte or terabyte of data, we're putting it into this block.

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But of course, the block of data

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just contains the amount of X-ray

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that's been absorbed in each point in the human body.

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So what we need to do is to figure out a way

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of looking at the things we do want to look at

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and make things transparent that we don't want to look at.

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So transforming the data set

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into something that looks like this.

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And this is a challenge.

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This is a huge challenge for us to do that.

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Using computers, even though they're getting faster and better all the time,

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it's a challenge to deal with gigabytes of data,

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terabytes of data

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and extracting the relevant information.

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I want to look at the heart.

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I want to look at the blood vessels. I want to look at the liver.

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Maybe even find a tumor,

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in some cases.

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So this is where this little dear comes into play.

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This is my daughter.

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This is as of 9 a.m. this morning.

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She's playing a computer game.

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She's only two years old,

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and she's having a blast.

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So she's really the driving force

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behind the development of graphics-processing units.

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As long as kids are playing computer games,

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graphics is getting better and better and better.

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So please go back home, tell your kids to play more games,

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because that's what I need.

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So what's inside of this machine

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is what enables me to do the things that I'm doing

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with the medical data.

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So really what I'm doing is using these fantastic little devices.

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And you know, going back

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maybe 10 years in time

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when I got the funding

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to buy my first graphics computer --

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it was a huge machine.

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It was cabinets of processors and storage and everything.

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I paid about one million dollars for that machine.

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That machine is, today, about as fast as my iPhone.

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So every month there are new graphics cards coming out,

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and here is a few of the latest ones from the vendors --

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NVIDIA, ATI, Intel is out there as well.

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And you know, for a few hundred bucks

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you can get these things and put them into your computer,

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and you can do fantastic things with these graphics cards.

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So this is really what's enabling us

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to deal with the explosion of data in medicine,

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together with some really nifty work

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in terms of algorithms --

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compressing data,

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extracting the relevant information that people are doing research on.

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So I'm going to show you a few examples of what we can do.

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This is a data set that was captured using a CT scanner.

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You can see that this is a full data [set].

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It's a woman. You can see the hair.

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You can see the individual structures of the woman.

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You can see that there is [a] scattering of X-rays

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on the teeth, the metal in the teeth.

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That's where those artifacts are coming from.

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But fully interactively

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on standard graphics cards on a normal computer,

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I can just put in a clip plane.

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And of course all the data is inside,

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so I can start rotating, I can look at it from different angles,

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and I can see that this woman had a problem.

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She had a bleeding up in the brain,

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and that's been fixed with a little stent,

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a metal clamp that's tightening up the vessel.

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And just by changing the functions,

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then I can decide what's going to be transparent

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and what's going to be visible.

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I can look at the skull structure,

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and I can see that, okay, this is where they opened up the skull on this woman,

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and that's where they went in.

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So these are fantastic images.

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They're really high resolution,

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and they're really showing us what we can do

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with standard graphics cards today.

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Now we have really made use of this,

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and we have tried to squeeze a lot of data

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into the system.

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And one of the applications that we've been working on --

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and this has gotten a little bit of traction worldwide --

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is the application of virtual autopsies.

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So again, looking at very, very large data sets,

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and you saw those full-body scans that we can do.

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We're just pushing the body through the whole CT scanner,

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and just in a few seconds we can get a full-body data set.

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So this is from a virtual autopsy.

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And you can see how I'm gradually peeling off.

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First you saw the body bag that the body came in,

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then I'm peeling off the skin -- you can see the muscles --

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and eventually you can see the bone structure of this woman.

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Now at this point, I would also like to emphasize

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that, with the greatest respect

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for the people that I'm now going to show --

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I'm going to show you a few cases of virtual autopsies --

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so it's with great respect for the people

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that have died under violent circumstances

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that I'm showing these pictures to you.

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In the forensic case --

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and this is something

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that ... there's been approximately 400 cases so far

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just in the part of Sweden that I come from

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that has been undergoing virtual autopsies

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in the past four years.

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So this will be the typical workflow situation.

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The police will decide --

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in the evening, when there's a case coming in --

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they will decide, okay, is this a case where we need to do an autopsy?

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So in the morning, in between six and seven in the morning,

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the body is then transported inside of the body bag

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to our center

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and is being scanned through one of the CT scanners.

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And then the radiologist, together with the pathologist

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and sometimes the forensic scientist,

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looks at the data that's coming out,

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and they have a joint session.

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And then they decide what to do in the real physical autopsy after that.

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Now looking at a few cases,

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here's one of the first cases that we had.

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You can really see the details of the data set.

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It's very high-resolution,

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and it's our algorithms that allow us

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to zoom in on all the details.

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And again, it's fully interactive,

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so you can rotate and you can look at things in real time

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on these systems here.

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Without saying too much about this case,

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this is a traffic accident,

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a drunk driver hit a woman.

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And it's very, very easy to see the damages on the bone structure.

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And the cause of death is the broken neck.

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And this women also ended up under the car,

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so she's quite badly beaten up

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by this injury.

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Here's another case, a knifing.

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And this is also again showing us what we can do.

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It's very easy to look at metal artifacts

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that we can show inside of the body.

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You can also see some of the artifacts from the teeth --

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that's actually the filling of the teeth --

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but because I've set the functions to show me metal

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and make everything else transparent.

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Here's another violent case. This really didn't kill the person.

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The person was killed by stabs in the heart,

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but they just deposited the knife

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by putting it through one of the eyeballs.

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Here's another case.

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It's very interesting for us

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to be able to look at things like knife stabbings.

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Here you can see that knife went through the heart.

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It's very easy to see how air has been leaking

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from one part to another part,

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which is difficult to do in a normal, standard, physical autopsy.

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So it really, really helps

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the criminal investigation

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to establish the cause of death,

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and in some cases also directing the investigation in the right direction

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to find out who the killer really was.

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Here's another case that I think is interesting.

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Here you can see a bullet

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that has lodged just next to the spine on this person.

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And what we've done is that we've turned the bullet into a light source,

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so that bullet is actually shining,

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and it makes it really easy to find these fragments.

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During a physical autopsy,

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if you actually have to dig through the body to find these fragments,

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that's actually quite hard to do.

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One of the things that I'm really, really happy

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to be able to show you here today

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is our virtual autopsy table.

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It's a touch device that we have developed

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based on these algorithms, using standard graphics GPUs.

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It actually looks like this,

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just to give you a feeling for what it looks like.

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It really just works like a huge iPhone.

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So we've implemented

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all the gestures you can do on the table,

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and you can think of it as an enormous touch interface.

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So if you were thinking of buying an iPad,

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forget about it. This is what you want instead.

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Steve, I hope you're listening to this, all right.

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So it's a very nice little device.

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So if you have the opportunity, please try it out.

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It's really a hands-on experience.

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So it gained some traction, and we're trying to roll this out

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and trying to use it for educational purposes,

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but also, perhaps in the future,

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in a more clinical situation.

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There's a YouTube video that you can download and look at this,

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if you want to convey the information to other people

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about virtual autopsies.

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Okay, now that we're talking about touch,

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let me move on to really "touching" data.

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And this is a bit of science fiction now,

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so we're moving into really the future.

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This is not really what the medical doctors are using right now,

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but I hope they will in the future.

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So what you're seeing on the left is a touch device.

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It's a little mechanical pen

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that has very, very fast step motors inside of the pen.

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And so I can generate a force feedback.

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So when I virtually touch data,

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it will generate forces in the pen, so I get a feedback.

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So in this particular situation,

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it's a scan of a living person.

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I have this pen, and I look at the data,

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and I move the pen towards the head,

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and all of a sudden I feel resistance.

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So I can feel the skin.

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If I push a little bit harder, I'll go through the skin,

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and I can feel the bone structure inside.

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If I push even harder, I'll go through the bone structure,

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especially close to the ear where the bone is very soft.

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And then I can feel the brain inside, and this will be the slushy like this.

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So this is really nice.

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And to take that even further, this is a heart.

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And this is also due to these fantastic new scanners,

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that just in 0.3 seconds,

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I can scan the whole heart,

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and I can do that with time resolution.

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So just looking at this heart,

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I can play back a video here.

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And this is Karljohan, one of my graduate students

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who's been working on this project.

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And he's sitting there in front of the Haptic device, the force feedback system,

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and he's moving his pen towards the heart,

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and the heart is now beating in front of him,

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so he can see how the heart is beating.

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He's taken the pen, and he's moving it towards the heart,

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and he's putting it on the heart,

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and then he feels the heartbeats from the real living patient.

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Then he can examine how the heart is moving.

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He can go inside, push inside of the heart,

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and really feel how the valves are moving.

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And this, I think, is really the future for heart surgeons.

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I mean it's probably the wet dream for a heart surgeon

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to be able to go inside of the patient's heart

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before you actually do surgery,

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and do that with high-quality resolution data.

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So this is really neat.

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Now we're going even further into science fiction.

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And we heard a little bit about functional MRI.

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Now this is really an interesting project.

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MRI is using magnetic fields

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and radio frequencies

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to scan the brain, or any part of the body.

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So what we're really getting out of this

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is information of the structure of the brain,

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but we can also measure the difference

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in magnetic properties of blood that's oxygenated

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and blood that's depleted of oxygen.

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That means that it's possible

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to map out the activity of the brain.

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So this is something that we've been working on.

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And you just saw Motts the research engineer, there,

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going into the MRI system,

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and he was wearing goggles.

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So he could actually see things in the goggles.

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So I could present things to him while he's in the scanner.

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And this is a little bit freaky,

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because what Motts is seeing is actually this.

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He's seeing his own brain.

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So Motts is doing something here,

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and probably he is going like this with his right hand,

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because the left side is activated

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on the motor cortex.

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And then he can see that at the same time.

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These visualizations are brand new.

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And this is something that we've been researching for a little while.

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This is another sequence of Motts' brain.

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And here we asked Motts to calculate backwards from 100.

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So he's going "100, 97, 94."

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And then he's going backwards.

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And you can see how the little math processor is working up here in his brain

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and is lighting up the whole brain.

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Well this is fantastic. We can do this in real time.

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We can investigate things. We can tell him to do things.

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You can also see that his visual cortex

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is activated in the back of the head,

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because that's where he's seeing, he's seeing his own brain.

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And he's also hearing our instructions

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when we tell him to do things.

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The signal is really deep inside of the brain as well,

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and it's shining through,

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because all of the data is inside this volume.

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00:14:15,000 --> 00:14:17,000
And in just a second here you will see --

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okay, here. Motts, now move your left foot.

364
00:14:19,000 --> 00:14:21,000
So he's going like this.

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00:14:21,000 --> 00:14:23,000
For 20 seconds he's going like that,

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and all of a sudden it lights up up here.

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00:14:25,000 --> 00:14:27,000
So we've got motor cortex activation up there.

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00:14:27,000 --> 00:14:29,000
So this is really, really nice,

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and I think this is a great tool.

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And connecting also with the previous talk here,

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this is something that we could use as a tool

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to really understand

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how the neurons are working, how the brain is working,

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and we can do this with very, very high visual quality

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and very fast resolution.

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00:14:45,000 --> 00:14:47,000
Now we're also having a bit of fun at the center.

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00:14:47,000 --> 00:14:50,000
So this is a CAT scan -- Computer Aided Tomography.

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00:14:51,000 --> 00:14:53,000
So this is a lion from the local zoo

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00:14:53,000 --> 00:14:56,000
outside of Norrkoping in Kolmarden, Elsa.

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00:14:56,000 --> 00:14:58,000
So she came to the center,

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and they sedated her

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00:15:00,000 --> 00:15:02,000
and then put her straight into the scanner.

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00:15:02,000 --> 00:15:05,000
And then, of course, I get the whole data set from the lion.

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00:15:05,000 --> 00:15:07,000
And I can do very nice images like this.

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00:15:07,000 --> 00:15:09,000
I can peel off the layer of the lion.

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00:15:09,000 --> 00:15:11,000
I can look inside of it.

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00:15:11,000 --> 00:15:13,000
And we've been experimenting with this.

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And I think this is a great application

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00:15:15,000 --> 00:15:17,000
for the future of this technology,

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00:15:17,000 --> 00:15:20,000
because there's very little known about the animal anatomy.

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00:15:20,000 --> 00:15:23,000
What's known out there for veterinarians is kind of basic information.

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00:15:23,000 --> 00:15:25,000
We can scan all sorts of things,

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00:15:25,000 --> 00:15:27,000
all sorts of animals.

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00:15:27,000 --> 00:15:30,000
The only problem is to fit it into the machine.

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00:15:30,000 --> 00:15:32,000
So here's a bear.

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00:15:32,000 --> 00:15:34,000
It was kind of hard to get it in.

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00:15:34,000 --> 00:15:37,000
And the bear is a cuddly, friendly animal.

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00:15:37,000 --> 00:15:40,000
And here it is. Here is the nose of the bear.

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00:15:40,000 --> 00:15:43,000
And you might want to cuddle this one,

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00:15:43,000 --> 00:15:46,000
until you change the functions and look at this.

401
00:15:46,000 --> 00:15:48,000
So be aware of the bear.

402
00:15:48,000 --> 00:15:50,000
So with that,

403
00:15:50,000 --> 00:15:52,000
I'd like to thank all the people

404
00:15:52,000 --> 00:15:54,000
who have helped me to generate these images.

405
00:15:54,000 --> 00:15:56,000
It's a huge effort that goes into doing this,

406
00:15:56,000 --> 00:15:59,000
gathering the data and developing the algorithms,

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00:15:59,000 --> 00:16:01,000
writing all the software.

408
00:16:01,000 --> 00:16:04,000
So, some very talented people.

409
00:16:04,000 --> 00:16:07,000
My motto is always, I only hire people that are smarter than I am

410
00:16:07,000 --> 00:16:09,000
and most of these are smarter than I am.

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00:16:09,000 --> 00:16:11,000
So thank you very much.

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(Applause)