I believe that we can both unravel the mysteries of the universe and save human lives at the same time through interdisciplinary research. And I'm going to share with you today just one story, my story, that has crossed these paths. We start the in supernova remnant Cassiopeia A. It's one of the youngest ones in our galaxy, about 330 years old. An astronomy colleague approached me one day, and she had over eight years of magnificent data, just trying to understand the 3-D structure of this nebula, the supernova remnant. But she had no way to look at it. So I looked at the data with her and said, "I think I can help you." And although -- and this is all real data you're seeing on the screen above me -- this is the Hollywood rendering version, but the rough draft I made with her looks something more like this. And she was able to make novel discoveries about how supernovas explode and how shells explode within it, using a piece of software developed at Brigham and Women's Hospital here in Boston, called 3D Slicer. It was originally developed for looking at patients' brain scans, doing surgical planning and doing 3-D renderings of anatomy. Who knew our solution was lurking just across the river? Now, people don't believe me when I tell them that astronomy and medical imaging -- these two seemingly different fields -- are really similar. So we're going to play a little game I like to call "Which is which?" I play this with new doctors and astronomers I work with. I'm going to show you two images on the screen. One of them is biomedical and one of them is astronomical, and you have to pick them correctly in your head. So here is the first set. And again, one of these is biomedical and one is astronomical. I'll give you a second to make your little vote mentally. So it turns out the one on the left is some of the raw data of the supernova remnant we were just looking at, and on the right, we have an angiogram of a patient's heart and coronary arteries. OK, we're going to try another one. Now, this one is much closer to my daily bread and butter. Tell me which is which. And one of these is literally millimeters across, and the other is billions of miles. So, it turns out the one on the left is a confocal microscopy image of a human cornea, and on the right, we have a radio telescope image of the star-forming region NGC-1333. Now, aside from the fact that these images look similar and that doctors trying to find a tumor in a patient's brain or a young star forming is similar, the way the data comes from the machine or the telescope is remarkably similar. Here's an MRI scanner. And if you've never seen the raw data of a patient's brain, this is what it looks like. When the MRI scanner is acquiring the data, it goes in slices. So you can see the patient's nose, their eyes; it kind of progresses towards the middle of the head; you can start to see the cortex, and it steps through to the back of the brain. Now, believe it or not, telescopes, and particularly radio telescopes, operate in a similar manner. If we were to look at the raw data from these telescopes ... We're going to look at a nebula called M16. We start with this radio telescope at the front of the nebula, stepping back towards the middle of the nebula, just like the middle of the patient's brain -- those bright regions are where young stars are forming -- all the way to the back of the nebula, just like the back of the patient's head. Now, although the doctors are able to then take this data and look at it in 3-D and do surgical planning, this is cutting-edge, just about as good as you get with any astronomer, and this is what they have to look at to understand the 3-D structure and velocity's momentum in our universe. But we can do better. So, you might recognize this nebula more like this: the famous Hubble image of the Pillars of Creation or the Eagle Nebula. And, I'm going to fade this out onto a radio image, it's a false color in the background, and fade away the Hubble image you're used to. But we don't need to just look at this in 3-D, we can look at it in 2-D, and here I'm using a radiology tool kit called OsiriX. When I showed this to astronomer Marc Pound, whose data this is, he was amazed, because he had been trying so hard to study the impact of a young group of stars. And he had this theory that there's this wind crashing and tossing the pillars over, and it took him months to prove this with conventional visualization. But in one shot, you can see the shock wave of wind blasting through across to the left-hand side of the screen. Now, I don't think myself or any of my collaborators would've anticipated how far this has gone, and by sharing the medical technology with astronomy and astronomy with medical, we've been able to find new stars and supernova remnants, and revolutionize how you do heart diagnostics and look at data for different patients and organize it and data-mine it. I don't have time to show you all these great projects, but I'll show you one of them. This is a collaboration I've been working on, called The Multiscale Hemodynamics Project. I'm working with doctors at Brigham and Women's Hospital. Now, what this represents is a novel way of doing heart disease diagnostics. And instead of the conventional invasive angiography, this is just a CT scan. What you see here are the coronary arteries. So you have your heart, and the arteries wrap around the outside. These are the arteries you worry about getting blocked and giving you a heart attack and killing you. So it's really important that we look at them. Now, this is a CT scan of a patient with a blood-flow simulation -- that's the coloring up there. That simulation was originally developed for studying the structure of DNA, and then the visualization was done with a tool kit called VisIt, originally developed for physics simulations. Interdisciplinary. My assignment was to try and come up with a new way of looking at this to make it optimal for the doctors and hospital: How can we make it the most efficient for them for a diagnosis? And I came up with this image. It's 2-D; I took the whole artery and collapsed everything into a 2-D plane. I got some very quizzical looks when I showed this to the doctors originally. But I was inspired to do this representation from my astronomy work, where we've been using these tree diagrams along the bottom to understand the structure of nebulae. Well, we were inspired in that work from the bioinformatics and genome community, where they use these tree diagrams to understand their gene expression data. They were inspired by the evolutionary biologists, who use these tree diagrams to understand how species evolve and are related, the first of which was drawn by Sir Charles Darwin. Here's an example from his "Origin of the Species." So, straight from Darwin, through biology, physics, astronomy, back to medical imaging. Interdisciplinary. One may say, "Well, is this 2-D representation better?" I did a study at Harvard Medical School to answer just that question. And it turns out, if you present the image on the left to a doctor, on average, they find about 39% of the high-risk regions that could explode or block your heart and kill you. On the right, we can do a little better, and they're able to find 62% of these high-risk, dangerous regions. But we can do even better, simply by changing the colors. The rainbow color map is a sin most doctors and astronomers and physicists are guilty of using. (Laughs) And it doesn't focus the best qualities of your visual system. The human system can see brightness variation, contrast ... not really good at that whole "green-yellow-blue" thing. But now, if you look in the shades of red and highlight the regions that are most diseased with dark red, now doctors can find 91% of the high-risk regions, simply by changing the colors. (Applause) And I would have never known the importance of color if it was not for my computer science and visualization collaborators showing this to me. So again: interdisciplinary collaboration. How do you even get a collaboration like this? In the case of astronomical medicine, it started with a Harvard Astronomy professor, Alyssa Goodman, serendipitously meeting a computer scientist and imaging specialist from Brigham and Woman’s Hospital, and their recruitment of a very adventurous, open-minded, young student. (Laughter) And from there, it has exploded: we've pulled in cardiologists and computer scientists and radiologists and astronomers, physicists, chemists, computational physicists -- I mean, we've brought so many people together. And it's been enlightening to share domains and information across borders. And we're still going. And although most of the people up on the screen are from Harvard or Harvard Med, now we cross different institutions and continents to work together. All I can say is, it has just been wonderful. We're continuing to make new discoveries. And I just urge you: attend conferences not in your own domain, read books and journals not in your own discipline, watch TED talks and come to events like this and say hi to the neighbor sitting next to you, because you really never know where your next great idea is going to come from. Thank you. (Applause)