WEBVTT 00:00:00.000 --> 00:00:04.000 I will start by posing a little bit of a challenge: 00:00:04.000 --> 00:00:07.000 the challenge of dealing with data, 00:00:07.000 --> 00:00:09.000 data that we have to deal with 00:00:09.000 --> 00:00:11.000 in medical situations. 00:00:11.000 --> 00:00:13.000 It's really a huge challenge for us. 00:00:13.000 --> 00:00:15.000 And this is our beast of burden -- 00:00:15.000 --> 00:00:17.000 this is a Computer Tomography machine, 00:00:17.000 --> 00:00:19.000 a CT machine. 00:00:19.000 --> 00:00:21.000 It's a fantastic device. 00:00:21.000 --> 00:00:23.000 It uses X-rays, X-ray beams, 00:00:23.000 --> 00:00:26.000 that are rotating very fast around the human body. 00:00:26.000 --> 00:00:28.000 It takes about 30 seconds to go through the whole machine 00:00:28.000 --> 00:00:30.000 and is generating enormous amounts of information 00:00:30.000 --> 00:00:32.000 that comes out of the machine. 00:00:32.000 --> 00:00:34.000 So this is a fantastic machine 00:00:34.000 --> 00:00:36.000 that we can use 00:00:36.000 --> 00:00:38.000 for improving health care, 00:00:38.000 --> 00:00:40.000 but as I said, it's also a challenge for us. 00:00:40.000 --> 00:00:43.000 And the challenge is really found in this picture here. 00:00:43.000 --> 00:00:45.000 It's the medical data explosion 00:00:45.000 --> 00:00:47.000 that we're having right now. 00:00:47.000 --> 00:00:49.000 We're facing this problem. 00:00:49.000 --> 00:00:51.000 And let me step back in time. NOTE Paragraph 00:00:51.000 --> 00:00:54.000 Let's go back a few years in time and see what happened back then. 00:00:54.000 --> 00:00:56.000 These machines that came out -- 00:00:56.000 --> 00:00:58.000 they started coming in the 1970s -- 00:00:58.000 --> 00:01:00.000 they would scan human bodies, 00:01:00.000 --> 00:01:02.000 and they would generate about 100 images 00:01:02.000 --> 00:01:04.000 of the human body. 00:01:04.000 --> 00:01:06.000 And I've taken the liberty, just for clarity, 00:01:06.000 --> 00:01:09.000 to translate that to data slices. 00:01:09.000 --> 00:01:11.000 That would correspond to about 50 megabytes of data, 00:01:11.000 --> 00:01:13.000 which is small 00:01:13.000 --> 00:01:16.000 when you think about the data we can handle today 00:01:16.000 --> 00:01:18.000 just on normal mobile devices. 00:01:18.000 --> 00:01:20.000 If you translate that to phone books, 00:01:20.000 --> 00:01:23.000 it's about one meter of phone books in the pile. 00:01:23.000 --> 00:01:25.000 Looking at what we're doing today 00:01:25.000 --> 00:01:27.000 with these machines that we have, 00:01:27.000 --> 00:01:29.000 we can, just in a few seconds, 00:01:29.000 --> 00:01:31.000 get 24,000 images out of a body, 00:01:31.000 --> 00:01:34.000 and that would correspond to about 20 gigabytes of data, 00:01:34.000 --> 00:01:36.000 or 800 phone books, 00:01:36.000 --> 00:01:38.000 and the pile would then be 200 meters of phone books. 00:01:38.000 --> 00:01:40.000 What's about to happen -- 00:01:40.000 --> 00:01:42.000 and we're seeing this; it's beginning -- 00:01:42.000 --> 00:01:44.000 a technology trend that's happening right now 00:01:44.000 --> 00:01:47.000 is that we're starting to look at time-resolved situations as well. 00:01:47.000 --> 00:01:50.000 So we're getting the dynamics out of the body as well. 00:01:50.000 --> 00:01:52.000 And just assume 00:01:52.000 --> 00:01:55.000 that we will be collecting data during five seconds, 00:01:55.000 --> 00:01:57.000 and that would correspond to one terabyte of data -- 00:01:57.000 --> 00:01:59.000 that's 800,000 books 00:01:59.000 --> 00:02:01.000 and 16 kilometers of phone books. 00:02:01.000 --> 00:02:03.000 That's one patient, one data set. 00:02:03.000 --> 00:02:05.000 And this is what we have to deal with. NOTE Paragraph 00:02:05.000 --> 00:02:08.000 So this is really the enormous challenge that we have. 00:02:08.000 --> 00:02:11.000 And already today -- this is 25,000 images. 00:02:11.000 --> 00:02:13.000 Imagine the days 00:02:13.000 --> 00:02:15.000 when we had radiologists doing this. 00:02:15.000 --> 00:02:17.000 They would put up 25,000 images, 00:02:17.000 --> 00:02:20.000 they would go like this, "25,0000, okay, okay. 00:02:20.000 --> 00:02:22.000 There is the problem." 00:02:22.000 --> 00:02:24.000 They can't do that anymore. That's impossible. 00:02:24.000 --> 00:02:27.000 So we have to do something that's a little bit more intelligent than doing this. 00:02:28.000 --> 00:02:30.000 So what we do is that we put all these slices together. 00:02:30.000 --> 00:02:33.000 Imagine that you slice your body in all these directions, 00:02:33.000 --> 00:02:36.000 and then you try to put the slices back together again 00:02:36.000 --> 00:02:38.000 into a pile of data, into a block of data. 00:02:38.000 --> 00:02:40.000 So this is really what we're doing. 00:02:40.000 --> 00:02:43.000 So this gigabyte or terabyte of data, we're putting it into this block. 00:02:43.000 --> 00:02:45.000 But of course, the block of data 00:02:45.000 --> 00:02:47.000 just contains the amount of X-ray 00:02:47.000 --> 00:02:49.000 that's been absorbed in each point in the human body. 00:02:49.000 --> 00:02:51.000 So what we need to do is to figure out a way 00:02:51.000 --> 00:02:54.000 of looking at the things we do want to look at 00:02:54.000 --> 00:02:57.000 and make things transparent that we don't want to look at. 00:02:57.000 --> 00:02:59.000 So transforming the data set 00:02:59.000 --> 00:03:01.000 into something that looks like this. 00:03:01.000 --> 00:03:03.000 And this is a challenge. 00:03:03.000 --> 00:03:06.000 This is a huge challenge for us to do that. NOTE Paragraph 00:03:06.000 --> 00:03:09.000 Using computers, even though they're getting faster and better all the time, 00:03:09.000 --> 00:03:11.000 it's a challenge to deal with gigabytes of data, 00:03:11.000 --> 00:03:13.000 terabytes of data 00:03:13.000 --> 00:03:15.000 and extracting the relevant information. 00:03:15.000 --> 00:03:17.000 I want to look at the heart. 00:03:17.000 --> 00:03:19.000 I want to look at the blood vessels. I want to look at the liver. 00:03:19.000 --> 00:03:21.000 Maybe even find a tumor, 00:03:21.000 --> 00:03:23.000 in some cases. 00:03:24.000 --> 00:03:26.000 So this is where this little dear comes into play. 00:03:26.000 --> 00:03:28.000 This is my daughter. 00:03:28.000 --> 00:03:30.000 This is as of 9 a.m. this morning. 00:03:30.000 --> 00:03:32.000 She's playing a computer game. 00:03:32.000 --> 00:03:34.000 She's only two years old, 00:03:34.000 --> 00:03:36.000 and she's having a blast. 00:03:36.000 --> 00:03:39.000 So she's really the driving force 00:03:39.000 --> 00:03:42.000 behind the development of graphics-processing units. 00:03:43.000 --> 00:03:45.000 As long as kids are playing computer games, 00:03:45.000 --> 00:03:47.000 graphics is getting better and better and better. 00:03:47.000 --> 00:03:49.000 So please go back home, tell your kids to play more games, 00:03:49.000 --> 00:03:51.000 because that's what I need. NOTE Paragraph 00:03:51.000 --> 00:03:53.000 So what's inside of this machine 00:03:53.000 --> 00:03:55.000 is what enables me to do the things that I'm doing 00:03:55.000 --> 00:03:57.000 with the medical data. 00:03:57.000 --> 00:04:00.000 So really what I'm doing is using these fantastic little devices. 00:04:00.000 --> 00:04:02.000 And you know, going back 00:04:02.000 --> 00:04:04.000 maybe 10 years in time 00:04:04.000 --> 00:04:06.000 when I got the funding 00:04:06.000 --> 00:04:08.000 to buy my first graphics computer -- 00:04:08.000 --> 00:04:10.000 it was a huge machine. 00:04:10.000 --> 00:04:13.000 It was cabinets of processors and storage and everything. 00:04:13.000 --> 00:04:16.000 I paid about one million dollars for that machine. 00:04:17.000 --> 00:04:20.000 That machine is, today, about as fast as my iPhone. 00:04:22.000 --> 00:04:24.000 So every month there are new graphics cards coming out, 00:04:24.000 --> 00:04:27.000 and here is a few of the latest ones from the vendors -- 00:04:27.000 --> 00:04:30.000 NVIDIA, ATI, Intel is out there as well. 00:04:30.000 --> 00:04:32.000 And you know, for a few hundred bucks 00:04:32.000 --> 00:04:34.000 you can get these things and put them into your computer, 00:04:34.000 --> 00:04:37.000 and you can do fantastic things with these graphics cards. 00:04:37.000 --> 00:04:39.000 So this is really what's enabling us 00:04:39.000 --> 00:04:42.000 to deal with the explosion of data in medicine, 00:04:42.000 --> 00:04:44.000 together with some really nifty work 00:04:44.000 --> 00:04:46.000 in terms of algorithms -- 00:04:46.000 --> 00:04:48.000 compressing data, 00:04:48.000 --> 00:04:51.000 extracting the relevant information that people are doing research on. NOTE Paragraph 00:04:51.000 --> 00:04:54.000 So I'm going to show you a few examples of what we can do. 00:04:54.000 --> 00:04:57.000 This is a data set that was captured using a CT scanner. 00:04:57.000 --> 00:05:00.000 You can see that this is a full data [set]. 00:05:00.000 --> 00:05:03.000 It's a woman. You can see the hair. 00:05:03.000 --> 00:05:06.000 You can see the individual structures of the woman. 00:05:06.000 --> 00:05:09.000 You can see that there is [a] scattering of X-rays 00:05:09.000 --> 00:05:11.000 on the teeth, the metal in the teeth. 00:05:11.000 --> 00:05:14.000 That's where those artifacts are coming from. 00:05:14.000 --> 00:05:16.000 But fully interactively 00:05:16.000 --> 00:05:19.000 on standard graphics cards on a normal computer, 00:05:19.000 --> 00:05:21.000 I can just put in a clip plane. 00:05:21.000 --> 00:05:23.000 And of course all the data is inside, 00:05:23.000 --> 00:05:26.000 so I can start rotating, I can look at it from different angles, 00:05:26.000 --> 00:05:29.000 and I can see that this woman had a problem. 00:05:29.000 --> 00:05:31.000 She had a bleeding up in the brain, 00:05:31.000 --> 00:05:33.000 and that's been fixed with a little stent, 00:05:33.000 --> 00:05:35.000 a metal clamp that's tightening up the vessel. 00:05:35.000 --> 00:05:37.000 And just by changing the functions, 00:05:37.000 --> 00:05:40.000 then I can decide what's going to be transparent 00:05:40.000 --> 00:05:42.000 and what's going to be visible. 00:05:42.000 --> 00:05:44.000 I can look at the skull structure, 00:05:44.000 --> 00:05:47.000 and I can see that, okay, this is where they opened up the skull on this woman, 00:05:47.000 --> 00:05:49.000 and that's where they went in. 00:05:49.000 --> 00:05:51.000 So these are fantastic images. 00:05:51.000 --> 00:05:53.000 They're really high resolution, 00:05:53.000 --> 00:05:55.000 and they're really showing us what we can do 00:05:55.000 --> 00:05:58.000 with standard graphics cards today. NOTE Paragraph 00:05:58.000 --> 00:06:00.000 Now we have really made use of this, 00:06:00.000 --> 00:06:03.000 and we have tried to squeeze a lot of data 00:06:03.000 --> 00:06:05.000 into the system. 00:06:05.000 --> 00:06:07.000 And one of the applications that we've been working on -- 00:06:07.000 --> 00:06:10.000 and this has gotten a little bit of traction worldwide -- 00:06:10.000 --> 00:06:12.000 is the application of virtual autopsies. 00:06:12.000 --> 00:06:14.000 So again, looking at very, very large data sets, 00:06:14.000 --> 00:06:17.000 and you saw those full-body scans that we can do. 00:06:17.000 --> 00:06:20.000 We're just pushing the body through the whole CT scanner, 00:06:20.000 --> 00:06:23.000 and just in a few seconds we can get a full-body data set. 00:06:23.000 --> 00:06:25.000 So this is from a virtual autopsy. 00:06:25.000 --> 00:06:27.000 And you can see how I'm gradually peeling off. 00:06:27.000 --> 00:06:30.000 First you saw the body bag that the body came in, 00:06:30.000 --> 00:06:33.000 then I'm peeling off the skin -- you can see the muscles -- 00:06:33.000 --> 00:06:36.000 and eventually you can see the bone structure of this woman. NOTE Paragraph 00:06:36.000 --> 00:06:39.000 Now at this point, I would also like to emphasize 00:06:39.000 --> 00:06:41.000 that, with the greatest respect 00:06:41.000 --> 00:06:43.000 for the people that I'm now going to show -- 00:06:43.000 --> 00:06:45.000 I'm going to show you a few cases of virtual autopsies -- 00:06:45.000 --> 00:06:47.000 so it's with great respect for the people 00:06:47.000 --> 00:06:49.000 that have died under violent circumstances 00:06:49.000 --> 00:06:52.000 that I'm showing these pictures to you. 00:06:53.000 --> 00:06:55.000 In the forensic case -- 00:06:55.000 --> 00:06:57.000 and this is something 00:06:57.000 --> 00:06:59.000 that ... there's been approximately 400 cases so far 00:06:59.000 --> 00:07:01.000 just in the part of Sweden that I come from 00:07:01.000 --> 00:07:03.000 that has been undergoing virtual autopsies 00:07:03.000 --> 00:07:05.000 in the past four years. 00:07:05.000 --> 00:07:08.000 So this will be the typical workflow situation. 00:07:08.000 --> 00:07:10.000 The police will decide -- 00:07:10.000 --> 00:07:12.000 in the evening, when there's a case coming in -- 00:07:12.000 --> 00:07:15.000 they will decide, okay, is this a case where we need to do an autopsy? 00:07:15.000 --> 00:07:18.000 So in the morning, in between six and seven in the morning, 00:07:18.000 --> 00:07:20.000 the body is then transported inside of the body bag 00:07:20.000 --> 00:07:22.000 to our center 00:07:22.000 --> 00:07:24.000 and is being scanned through one of the CT scanners. 00:07:24.000 --> 00:07:26.000 And then the radiologist, together with the pathologist 00:07:26.000 --> 00:07:28.000 and sometimes the forensic scientist, 00:07:28.000 --> 00:07:30.000 looks at the data that's coming out, 00:07:30.000 --> 00:07:32.000 and they have a joint session. 00:07:32.000 --> 00:07:35.000 And then they decide what to do in the real physical autopsy after that. NOTE Paragraph 00:07:37.000 --> 00:07:39.000 Now looking at a few cases, 00:07:39.000 --> 00:07:41.000 here's one of the first cases that we had. 00:07:41.000 --> 00:07:44.000 You can really see the details of the data set. 00:07:44.000 --> 00:07:46.000 It's very high-resolution, 00:07:46.000 --> 00:07:48.000 and it's our algorithms that allow us 00:07:48.000 --> 00:07:50.000 to zoom in on all the details. 00:07:50.000 --> 00:07:52.000 And again, it's fully interactive, 00:07:52.000 --> 00:07:54.000 so you can rotate and you can look at things in real time 00:07:54.000 --> 00:07:56.000 on these systems here. 00:07:56.000 --> 00:07:58.000 Without saying too much about this case, 00:07:58.000 --> 00:08:00.000 this is a traffic accident, 00:08:00.000 --> 00:08:02.000 a drunk driver hit a woman. 00:08:02.000 --> 00:08:05.000 And it's very, very easy to see the damages on the bone structure. 00:08:05.000 --> 00:08:08.000 And the cause of death is the broken neck. 00:08:08.000 --> 00:08:10.000 And this women also ended up under the car, 00:08:10.000 --> 00:08:12.000 so she's quite badly beaten up 00:08:12.000 --> 00:08:14.000 by this injury. NOTE Paragraph 00:08:14.000 --> 00:08:17.000 Here's another case, a knifing. 00:08:17.000 --> 00:08:19.000 And this is also again showing us what we can do. 00:08:19.000 --> 00:08:21.000 It's very easy to look at metal artifacts 00:08:21.000 --> 00:08:24.000 that we can show inside of the body. 00:08:24.000 --> 00:08:27.000 You can also see some of the artifacts from the teeth -- 00:08:27.000 --> 00:08:29.000 that's actually the filling of the teeth -- 00:08:29.000 --> 00:08:32.000 but because I've set the functions to show me metal 00:08:32.000 --> 00:08:34.000 and make everything else transparent. 00:08:34.000 --> 00:08:37.000 Here's another violent case. This really didn't kill the person. 00:08:37.000 --> 00:08:39.000 The person was killed by stabs in the heart, 00:08:39.000 --> 00:08:41.000 but they just deposited the knife 00:08:41.000 --> 00:08:43.000 by putting it through one of the eyeballs. 00:08:43.000 --> 00:08:45.000 Here's another case. 00:08:45.000 --> 00:08:47.000 It's very interesting for us 00:08:47.000 --> 00:08:49.000 to be able to look at things like knife stabbings. 00:08:49.000 --> 00:08:52.000 Here you can see that knife went through the heart. 00:08:52.000 --> 00:08:54.000 It's very easy to see how air has been leaking 00:08:54.000 --> 00:08:56.000 from one part to another part, 00:08:56.000 --> 00:08:59.000 which is difficult to do in a normal, standard, physical autopsy. 00:08:59.000 --> 00:09:01.000 So it really, really helps 00:09:01.000 --> 00:09:03.000 the criminal investigation 00:09:03.000 --> 00:09:05.000 to establish the cause of death, 00:09:05.000 --> 00:09:08.000 and in some cases also directing the investigation in the right direction 00:09:08.000 --> 00:09:10.000 to find out who the killer really was. NOTE Paragraph 00:09:10.000 --> 00:09:12.000 Here's another case that I think is interesting. 00:09:12.000 --> 00:09:14.000 Here you can see a bullet 00:09:14.000 --> 00:09:17.000 that has lodged just next to the spine on this person. 00:09:17.000 --> 00:09:20.000 And what we've done is that we've turned the bullet into a light source, 00:09:20.000 --> 00:09:22.000 so that bullet is actually shining, 00:09:22.000 --> 00:09:25.000 and it makes it really easy to find these fragments. 00:09:25.000 --> 00:09:27.000 During a physical autopsy, 00:09:27.000 --> 00:09:29.000 if you actually have to dig through the body to find these fragments, 00:09:29.000 --> 00:09:31.000 that's actually quite hard to do. NOTE Paragraph 00:09:33.000 --> 00:09:35.000 One of the things that I'm really, really happy 00:09:35.000 --> 00:09:38.000 to be able to show you here today 00:09:38.000 --> 00:09:40.000 is our virtual autopsy table. 00:09:40.000 --> 00:09:42.000 It's a touch device that we have developed 00:09:42.000 --> 00:09:45.000 based on these algorithms, using standard graphics GPUs. 00:09:45.000 --> 00:09:47.000 It actually looks like this, 00:09:47.000 --> 00:09:50.000 just to give you a feeling for what it looks like. 00:09:50.000 --> 00:09:53.000 It really just works like a huge iPhone. 00:09:53.000 --> 00:09:55.000 So we've implemented 00:09:55.000 --> 00:09:58.000 all the gestures you can do on the table, 00:09:58.000 --> 00:10:02.000 and you can think of it as an enormous touch interface. 00:10:02.000 --> 00:10:04.000 So if you were thinking of buying an iPad, 00:10:04.000 --> 00:10:07.000 forget about it. This is what you want instead. 00:10:07.000 --> 00:10:10.000 Steve, I hope you're listening to this, all right. 00:10:11.000 --> 00:10:13.000 So it's a very nice little device. 00:10:13.000 --> 00:10:15.000 So if you have the opportunity, please try it out. 00:10:15.000 --> 00:10:18.000 It's really a hands-on experience. 00:10:18.000 --> 00:10:21.000 So it gained some traction, and we're trying to roll this out 00:10:21.000 --> 00:10:23.000 and trying to use it for educational purposes, 00:10:23.000 --> 00:10:25.000 but also, perhaps in the future, 00:10:25.000 --> 00:10:28.000 in a more clinical situation. 00:10:28.000 --> 00:10:30.000 There's a YouTube video that you can download and look at this, 00:10:30.000 --> 00:10:32.000 if you want to convey the information to other people 00:10:32.000 --> 00:10:35.000 about virtual autopsies. NOTE Paragraph 00:10:35.000 --> 00:10:37.000 Okay, now that we're talking about touch, 00:10:37.000 --> 00:10:39.000 let me move on to really "touching" data. 00:10:39.000 --> 00:10:41.000 And this is a bit of science fiction now, 00:10:41.000 --> 00:10:44.000 so we're moving into really the future. 00:10:44.000 --> 00:10:47.000 This is not really what the medical doctors are using right now, 00:10:47.000 --> 00:10:49.000 but I hope they will in the future. 00:10:49.000 --> 00:10:52.000 So what you're seeing on the left is a touch device. 00:10:52.000 --> 00:10:54.000 It's a little mechanical pen 00:10:54.000 --> 00:10:57.000 that has very, very fast step motors inside of the pen. 00:10:57.000 --> 00:10:59.000 And so I can generate a force feedback. 00:10:59.000 --> 00:11:01.000 So when I virtually touch data, 00:11:01.000 --> 00:11:04.000 it will generate forces in the pen, so I get a feedback. 00:11:04.000 --> 00:11:06.000 So in this particular situation, 00:11:06.000 --> 00:11:08.000 it's a scan of a living person. 00:11:08.000 --> 00:11:11.000 I have this pen, and I look at the data, 00:11:11.000 --> 00:11:13.000 and I move the pen towards the head, 00:11:13.000 --> 00:11:15.000 and all of a sudden I feel resistance. 00:11:15.000 --> 00:11:17.000 So I can feel the skin. 00:11:17.000 --> 00:11:19.000 If I push a little bit harder, I'll go through the skin, 00:11:19.000 --> 00:11:22.000 and I can feel the bone structure inside. 00:11:22.000 --> 00:11:24.000 If I push even harder, I'll go through the bone structure, 00:11:24.000 --> 00:11:27.000 especially close to the ear where the bone is very soft. 00:11:27.000 --> 00:11:30.000 And then I can feel the brain inside, and this will be the slushy like this. NOTE Paragraph 00:11:30.000 --> 00:11:32.000 So this is really nice. 00:11:32.000 --> 00:11:35.000 And to take that even further, this is a heart. 00:11:35.000 --> 00:11:38.000 And this is also due to these fantastic new scanners, 00:11:38.000 --> 00:11:40.000 that just in 0.3 seconds, 00:11:40.000 --> 00:11:42.000 I can scan the whole heart, 00:11:42.000 --> 00:11:44.000 and I can do that with time resolution. 00:11:44.000 --> 00:11:46.000 So just looking at this heart, 00:11:46.000 --> 00:11:48.000 I can play back a video here. 00:11:48.000 --> 00:11:50.000 And this is Karljohan, one of my graduate students 00:11:50.000 --> 00:11:52.000 who's been working on this project. 00:11:52.000 --> 00:11:55.000 And he's sitting there in front of the Haptic device, the force feedback system, 00:11:55.000 --> 00:11:58.000 and he's moving his pen towards the heart, 00:11:58.000 --> 00:12:00.000 and the heart is now beating in front of him, 00:12:00.000 --> 00:12:02.000 so he can see how the heart is beating. 00:12:02.000 --> 00:12:04.000 He's taken the pen, and he's moving it towards the heart, 00:12:04.000 --> 00:12:06.000 and he's putting it on the heart, 00:12:06.000 --> 00:12:09.000 and then he feels the heartbeats from the real living patient. 00:12:09.000 --> 00:12:11.000 Then he can examine how the heart is moving. 00:12:11.000 --> 00:12:13.000 He can go inside, push inside of the heart, 00:12:13.000 --> 00:12:16.000 and really feel how the valves are moving. 00:12:16.000 --> 00:12:19.000 And this, I think, is really the future for heart surgeons. 00:12:19.000 --> 00:12:22.000 I mean it's probably the wet dream for a heart surgeon 00:12:22.000 --> 00:12:25.000 to be able to go inside of the patient's heart 00:12:25.000 --> 00:12:27.000 before you actually do surgery, 00:12:27.000 --> 00:12:29.000 and do that with high-quality resolution data. 00:12:29.000 --> 00:12:31.000 So this is really neat. NOTE Paragraph 00:12:32.000 --> 00:12:35.000 Now we're going even further into science fiction. 00:12:35.000 --> 00:12:38.000 And we heard a little bit about functional MRI. 00:12:38.000 --> 00:12:41.000 Now this is really an interesting project. 00:12:41.000 --> 00:12:43.000 MRI is using magnetic fields 00:12:43.000 --> 00:12:45.000 and radio frequencies 00:12:45.000 --> 00:12:48.000 to scan the brain, or any part of the body. 00:12:48.000 --> 00:12:50.000 So what we're really getting out of this 00:12:50.000 --> 00:12:52.000 is information of the structure of the brain, 00:12:52.000 --> 00:12:54.000 but we can also measure the difference 00:12:54.000 --> 00:12:57.000 in magnetic properties of blood that's oxygenated 00:12:57.000 --> 00:13:00.000 and blood that's depleted of oxygen. 00:13:00.000 --> 00:13:02.000 That means that it's possible 00:13:02.000 --> 00:13:04.000 to map out the activity of the brain. 00:13:04.000 --> 00:13:06.000 So this is something that we've been working on. 00:13:06.000 --> 00:13:09.000 And you just saw Motts the research engineer, there, 00:13:09.000 --> 00:13:11.000 going into the MRI system, 00:13:11.000 --> 00:13:13.000 and he was wearing goggles. 00:13:13.000 --> 00:13:15.000 So he could actually see things in the goggles. 00:13:15.000 --> 00:13:18.000 So I could present things to him while he's in the scanner. 00:13:18.000 --> 00:13:20.000 And this is a little bit freaky, 00:13:20.000 --> 00:13:22.000 because what Motts is seeing is actually this. 00:13:22.000 --> 00:13:25.000 He's seeing his own brain. 00:13:25.000 --> 00:13:27.000 So Motts is doing something here, 00:13:27.000 --> 00:13:29.000 and probably he is going like this with his right hand, 00:13:29.000 --> 00:13:31.000 because the left side is activated 00:13:31.000 --> 00:13:33.000 on the motor cortex. 00:13:33.000 --> 00:13:35.000 And then he can see that at the same time. 00:13:35.000 --> 00:13:37.000 These visualizations are brand new. 00:13:37.000 --> 00:13:40.000 And this is something that we've been researching for a little while. NOTE Paragraph 00:13:40.000 --> 00:13:43.000 This is another sequence of Motts' brain. 00:13:43.000 --> 00:13:46.000 And here we asked Motts to calculate backwards from 100. 00:13:46.000 --> 00:13:48.000 So he's going "100, 97, 94." 00:13:48.000 --> 00:13:50.000 And then he's going backwards. 00:13:50.000 --> 00:13:53.000 And you can see how the little math processor is working up here in his brain 00:13:53.000 --> 00:13:55.000 and is lighting up the whole brain. 00:13:55.000 --> 00:13:57.000 Well this is fantastic. We can do this in real time. 00:13:57.000 --> 00:13:59.000 We can investigate things. We can tell him to do things. 00:13:59.000 --> 00:14:01.000 You can also see that his visual cortex 00:14:01.000 --> 00:14:03.000 is activated in the back of the head, 00:14:03.000 --> 00:14:05.000 because that's where he's seeing, he's seeing his own brain. 00:14:05.000 --> 00:14:07.000 And he's also hearing our instructions 00:14:07.000 --> 00:14:09.000 when we tell him to do things. 00:14:09.000 --> 00:14:11.000 The signal is really deep inside of the brain as well, 00:14:11.000 --> 00:14:13.000 and it's shining through, 00:14:13.000 --> 00:14:15.000 because all of the data is inside this volume. 00:14:15.000 --> 00:14:17.000 And in just a second here you will see -- 00:14:17.000 --> 00:14:19.000 okay, here. Motts, now move your left foot. 00:14:19.000 --> 00:14:21.000 So he's going like this. 00:14:21.000 --> 00:14:23.000 For 20 seconds he's going like that, 00:14:23.000 --> 00:14:25.000 and all of a sudden it lights up up here. 00:14:25.000 --> 00:14:27.000 So we've got motor cortex activation up there. 00:14:27.000 --> 00:14:29.000 So this is really, really nice, 00:14:29.000 --> 00:14:31.000 and I think this is a great tool. 00:14:31.000 --> 00:14:33.000 And connecting also with the previous talk here, 00:14:33.000 --> 00:14:35.000 this is something that we could use as a tool 00:14:35.000 --> 00:14:37.000 to really understand 00:14:37.000 --> 00:14:39.000 how the neurons are working, how the brain is working, 00:14:39.000 --> 00:14:42.000 and we can do this with very, very high visual quality 00:14:42.000 --> 00:14:45.000 and very fast resolution. NOTE Paragraph 00:14:45.000 --> 00:14:47.000 Now we're also having a bit of fun at the center. 00:14:47.000 --> 00:14:50.000 So this is a CAT scan -- Computer Aided Tomography. 00:14:51.000 --> 00:14:53.000 So this is a lion from the local zoo 00:14:53.000 --> 00:14:56.000 outside of Norrkoping in Kolmarden, Elsa. 00:14:56.000 --> 00:14:58.000 So she came to the center, 00:14:58.000 --> 00:15:00.000 and they sedated her 00:15:00.000 --> 00:15:02.000 and then put her straight into the scanner. 00:15:02.000 --> 00:15:05.000 And then, of course, I get the whole data set from the lion. 00:15:05.000 --> 00:15:07.000 And I can do very nice images like this. 00:15:07.000 --> 00:15:09.000 I can peel off the layer of the lion. 00:15:09.000 --> 00:15:11.000 I can look inside of it. 00:15:11.000 --> 00:15:13.000 And we've been experimenting with this. 00:15:13.000 --> 00:15:15.000 And I think this is a great application 00:15:15.000 --> 00:15:17.000 for the future of this technology, 00:15:17.000 --> 00:15:20.000 because there's very little known about the animal anatomy. 00:15:20.000 --> 00:15:23.000 What's known out there for veterinarians is kind of basic information. 00:15:23.000 --> 00:15:25.000 We can scan all sorts of things, 00:15:25.000 --> 00:15:27.000 all sorts of animals. 00:15:27.000 --> 00:15:30.000 The only problem is to fit it into the machine. 00:15:30.000 --> 00:15:32.000 So here's a bear. 00:15:32.000 --> 00:15:34.000 It was kind of hard to get it in. 00:15:34.000 --> 00:15:37.000 And the bear is a cuddly, friendly animal. 00:15:37.000 --> 00:15:40.000 And here it is. Here is the nose of the bear. 00:15:40.000 --> 00:15:43.000 And you might want to cuddle this one, 00:15:43.000 --> 00:15:46.000 until you change the functions and look at this. 00:15:46.000 --> 00:15:48.000 So be aware of the bear. NOTE Paragraph 00:15:48.000 --> 00:15:50.000 So with that, 00:15:50.000 --> 00:15:52.000 I'd like to thank all the people 00:15:52.000 --> 00:15:54.000 who have helped me to generate these images. 00:15:54.000 --> 00:15:56.000 It's a huge effort that goes into doing this, 00:15:56.000 --> 00:15:59.000 gathering the data and developing the algorithms, 00:15:59.000 --> 00:16:01.000 writing all the software. 00:16:01.000 --> 00:16:04.000 So, some very talented people. 00:16:04.000 --> 00:16:07.000 My motto is always, I only hire people that are smarter than I am 00:16:07.000 --> 00:16:09.000 and most of these are smarter than I am. NOTE Paragraph 00:16:09.000 --> 00:16:11.000 So thank you very much. NOTE Paragraph 00:16:11.000 --> 00:16:15.000 (Applause)