I believe that we can both unravel the mysteries of the universe and save human lives at the same time through interdisciplinary research.

我相信， 通过跨学科研究， 我们既可以解开宇宙的奥秘， 也可以拯救人类的生命。

And I'm going to share with you today just one story, my story, that has crossed these paths.

今天我要和大家分享 一个故事，我自己的故事， 这个故事证明我说的一切可以发生。

We start the in supernova remnant Cassiopeia A.

我们从超新星残骸仙后座 A 开始，

It's one of the youngest ones in our galaxy, about 330 years old.

它是我们星系中最年轻的 超新星之一，大约 330 岁。

An astronomy colleague approached me one day, and she had over eight years of magnificent data,

有一天，一位天文学的同事找到我， 她有超过八年的大量数据，

just trying to understand the 3-D structure of this nebula, the supernova remnant.

只是想了解这个星云的三维结构， 超新星遗迹。

But she had no way to look at it.

但是她没有办法去了解它。

So I looked at the data with her and said, "I think I can help you."

所以我和她一起看了数据， 然后说：“我想我可以帮助你。

And although -- and this is all real data you're seeing on the screen above me -- this is the Hollywood rendering version,

尽管——这些都是我身后屏幕上 呈现的真实数据—— 这是好莱坞的渲染版本，

but the rough draft I made with her looks something more like this.

但我和她做的草稿看起来更像这样。

And she was able to make novel discoveries about how supernovas explode and how shells explode within it,

她能够对超新星爆炸 做出新颖的发现， 包括超新星的壳如何在内部炸开，

using a piece of software developed at Brigham and Women's Hospital here in Boston, called 3D Slicer.

使用一款由布莱根妇女医院 （Brigham and Women’s Hospital） 开发的软件， 就在波士顿。

It was originally developed for looking at patients' brain scans, doing surgical planning and doing 3-D renderings of anatomy.

这软件叫做三维切片机。3D 切片机最初是为了 查看患者的脑部扫描结果， 制定手术计划而开发的 和绘制三维解剖图而开发的。

Who knew our solution was lurking just across the river?

谁能想到我们的解决方案就近在咫尺?

Now, people don't believe me when I tell them that astronomy and medical imaging -- these two seemingly different fields -- are really similar.

人们不相信我 当我说天文学和医学成像—— 这两个看似不同的领域—— 其实非常相似时。

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.

所以我们要玩一个小游戏， 我喜欢叫它“哪个是哪个？” 我和要跟我一起工作的 医生和天文学家一起玩这个。

I'm going to show you two images on the screen.

我会在屏幕上给你们看两张图片。

One of them is biomedical and one of them is astronomical, and you have to pick them correctly in your head.

其中一个是生物医学的， 另一个是天文的， 而你需要在脑子里选择 他们对应的正确领域。

So here is the first set.

所以这是第一组。

And again, one of these is biomedical and one is astronomical.

其中一个是生物医学的， 另一个是天文的。

I'll give you a second to make your little vote mentally.

我给你一点时间， 让你在心里投票。

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,

所以结果是左边的是我们刚刚看到 超新星遗迹的一些原始数据， 而右边的是

we have an angiogram of a patient's heart and coronary arteries.

病人心脏 和冠状动脉的血管造影。

OK, we're going to try another one.

好的，我们再试一个。

Now, this one is much closer to my daily bread and butter.

现在，这个更接近我的日常生活。

Tell me which is which.

告诉我哪个是哪个。

And one of these is literally millimeters across, and the other is billions of miles.

其中一个直径只有毫米， 另一个有数十亿英里。

So, it turns out the one on the left is a confocal microscopy image of a human cornea, and on the right,

所以，左边的是人类角膜的 共聚焦显微镜图像， 而右边的是

we have a radio telescope image of the star-forming region NGC-1333.

恒星形成区域 NGC-1333 的射电望远镜图像。

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,

现在，除了这些图像看起来很相似， 医生试图在病人的大脑中寻找肿瘤 或年轻恒星的形成是相似的，

the way the data comes from the machine or the telescope is remarkably similar.

来自机器或望远镜的数据的方式 是非常相似的。

Here's an MRI scanner.

这是核磁共振扫描仪。

And if you've never seen the raw data of a patient's brain, this is what it looks like.

如果你从未见过病人 大脑的原始数据， 这就是它的样子。

When the MRI scanner is acquiring the data, it goes in slices.

当核磁共振扫描仪获取数据时， 它是切片的。

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,

所以你可以看到病人的鼻子、眼睛；它向头部中部发展；你可以看到大脑皮层，

and it steps through to the back of the brain.

它一直延伸到大脑后部。

Now, believe it or not, telescopes, and particularly radio telescopes, operate in a similar manner.

信不信由你， 望远镜，尤其是射电望远镜， 也是以类似的方式运作的。

If we were to look at the raw data from these telescopes ...

如果我们观察这些望远镜的原始数据......

We're going to look at a nebula called M16.

我们要看一个叫 M16 的星云。

We start with this radio telescope at the front of the nebula, stepping back towards the middle of the nebula,

我们从这个射电望远镜 开始在星云的前面， 后退到星云的中间，

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,

就像病人大脑的中间—— 那些明亮的区域是年轻 恒星形成的地方—— 直到星云的后面，

just like the back of the patient's head.

就像病人脑袋的后面。

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,

现在，尽管医生们能够利用这些数据 并以 3D 的方式观察它并制定手术计划，这是最先进的技术，

just about as good as you get with any astronomer,

就像任何天文学家必须要看的东西

and this is what they have to look at to understand the 3-D structure and velocity's momentum in our universe.

来 了解三维结构 和宇宙中的速度动量。

But we can do better.

但我们可以做得更好。

So, you might recognize this nebula more like this: the famous Hubble image of the Pillars of Creation or the Eagle Nebula.

所以，你可能更像是这样 认识到这个星云：著名的哈勃图像的创造之柱或鹰状星云。

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.

我要让这张图片褪色， 它的背景颜色是假的， 褪去你们熟悉的哈勃图像。

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.

但我们不需要只看 3D，我们可以看 2D， 这里我用的是一个叫做 OsiriX 的放射学工具包。

When I showed this to astronomer Marc Pound, whose data this is, he was amazed,

当我把这个数据 给天文学家马克·庞德 （Marc Pound）看时， 他很惊讶，

because he had been trying so hard to study the impact of a young group of stars.

因为他一直在努力研究 一群年轻恒星的影响。

And he had this theory that there's this wind crashing and tossing the pillars over,

他有一个理论，关于 风冲击柱子并将它们吹翻，

and it took him months to prove this with conventional visualization.

他花了几个月的时间用传统的 可视化方法来证明这一点。

But in one shot, you can see the shock wave of wind blasting through across to the left-hand side of the screen.

但在其中一个镜头中， 你可以看到风的震荡波 横扫过屏幕的左侧。

Now, I don't think myself or any of my collaborators would've anticipated how far this has gone,

现在，我不认为我自己 或我的任何合作者 都能预料到已经发展这么多了,

and by sharing the medical technology with astronomy and astronomy with medical, we've been able to find new stars and supernova remnants,

通过与天文学分享医疗技术， 以及与医学分享天文学相关技术， 我们已经能够发现新的恒星和超新星遗迹，

and revolutionize how you do heart diagnostics and look at data for different patients and organize it and data-mine it.

并革命性的改变了心脏诊断术 以及医生对不同患者数据的查看方式、 组织方式和数据挖掘方式。

I don't have time to show you all these great projects, but I'll show you one of them.

我没有时间给你们展示 所有这些伟大的项目， 但我会给你们展示其中一个。

This is a collaboration I've been working on, called The Multiscale Hemodynamics Project.

这是我一直在做的一个合作项目， 叫做多尺度血流动力学项目。

I'm working with doctors at Brigham and Women's Hospital.

我和布莱根妇女医院的医生一起合作。

Now, what this represents is a novel way of doing heart disease diagnostics.

这代表了一种心脏病诊断的新方法。

And instead of the conventional invasive angiography, this is just a CT scan.

而不是传统的侵入性血管造影， 这只是一个 CT 扫描。

What you see here are the coronary arteries.

你们看到的是冠状动脉。

So you have your heart, and the arteries wrap around the outside.

这是心脏，动脉环绕在外面。

These are the arteries you worry about getting blocked and giving you a heart attack and killing you.

这些是动脉 让人担心的是它们 阻塞并导致心脏病发作 然后杀死你。

So it's really important that we look at them.

所以研究它们是非常重要的。

Now, this is a CT scan of a patient with a blood-flow simulation -- that's the coloring up there.

现在，这是一个病人的 CT 与血流模拟——这是上面的颜色。

That simulation was originally developed for studying the structure of DNA, and then the visualization was done with a tool kit called VisIt,

这个模拟最初是为了研究 DNA 的结构而开发的， 然后可视化是用一个叫做 VisIt 的工具包完成的，

originally developed for physics simulations.

这个工具包最初是为物理模拟开发的。

Interdisciplinary.

跨了学科。

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:

我的任务是尝试并提出 一种新的方法来看待这个问题， 使它对医生和医院来说是最优的：

How can we make it the most efficient for them for a diagnosis?

我们如何才能使他们得出更有效的诊断?

And I came up with this image.

然后我想到了这个图像。

It's 2-D; I took the whole artery and collapsed everything into a 2-D plane.

这是二维图片；我把整个动脉都折成二维平面。

I got some very quizzical looks when I showed this to the doctors originally.

当我一开始把这个给医生看的时候， 他们都很疑惑。

But I was inspired to do this representation from my astronomy work,

但我从我的天文学工作中 获得启发做出了这个演示，

where we've been using these tree diagrams along the bottom to understand the structure of nebulae.

我们一直在用这些底部的树形图 来理解星云的结构。

Well, we were inspired in that work from the bioinformatics and genome community,

我们在生物信息学和基因组社区的 工作中受到了启发，

where they use these tree diagrams to understand their gene expression data.

他们使用这些树形图来了解基因表达数据。

They were inspired by the evolutionary biologists, who use these tree diagrams to understand how species evolve and are related,

他们受到了进化生物学家的启发， 进化生物学家使用这些树状图来了 解物种是如何进化和相互关联的，

the first of which was drawn by Sir Charles Darwin.

其中第一个树状图是由 查尔斯·达尔文爵士绘制的。

Here's an example from his "Origin of the Species." So, straight from Darwin, through biology, physics, astronomy, back to medical imaging.

这是他的《物种起源》中的一个例子。所以，从达尔文开始， 经过生物学，物理学，天文学， 再回到医学成像。

Interdisciplinary.

跨学科。

One may say, "Well, is this 2-D representation better?" I did a study at Harvard Medical School to answer just that question.

有人可能会说，“用二维演示真的更好吗?” 我在哈佛医学院做了一项 研究来回答这个问题。

And it turns out, if you present the image on the left to a doctor, on average,

结果是，如果你把 左边的图片给医生看， 平均来说，

they find about 39% of the high-risk regions that could explode or block your heart and kill you.

他们会发现 大约 39% 的高危区 可能会爆掉或阻塞你的 心脏并杀死你。

On the right, we can do a little better, and they're able to find 62% of these high-risk, dangerous regions.

在右边，我们可以做 得稍微更好一点， 然后他们大约能够 找到 62%的高风险，危险区域。

But we can do even better, simply by changing the colors.

但我们只要改变颜色 就可以做得更加好，

The rainbow color map is a sin most doctors and astronomers and physicists are guilty of using.

彩虹色地图是大多数医生、 天文学家和物理学家 都曾内疚地用过。

(Laughs) And it doesn't focus the best qualities of your visual system.

（笑声） 而且它并没有聚焦 你视觉系统的最佳品质。

The human system can see brightness variation, contrast ...

人体系统可以看到 亮度变化，对比度…...

not really good at that whole "green-yellow-blue" thing.

不太擅长那种“绿黄蓝“的东西。

But now, if you look in the shades of red and highlight the regions that are most diseased with dark red,

但是现在，如果你看着红色区域的阴影， 并把得病最严重的区域高标为深红色，

now doctors can find 91% of the high-risk regions, simply by changing the colors.

医生就能找到 91% 的高风险区域。仅仅因为颜色的改变。

(Applause)

（掌声）

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.

如果不是我的计算机科学和可视化 合作者给我展示了颜色的重要性， 我永远不会知道 颜色的重要性。

So again: interdisciplinary collaboration.

所以再次强调：跨学科合作。

How do you even get a collaboration like this?

你是怎么得到这样的合作？

In the case of astronomical medicine, it started with a Harvard Astronomy professor, Alyssa Goodman,

以天文医学为例， 它始于哈佛大学天文学 阿丽莎·古德曼教授（Alyssa Goodman）

serendipitously meeting a computer scientist and imaging specialist from Brigham and Woman's Hospital, and their recruitment of a very adventurous,

偶然地遇到了布莱根妇女医院的 一位计算机科学家和成像专家， 她们招募了一位非常冒险、

open-minded, young student.

思想开放的年轻学生。

(Laughter) And from there, it has exploded: we've pulled in cardiologists and computer scientists and radiologists and astronomers, physicists,

从那刻起，我们开始迅速的扩张：我们吸引了心脏病学家、 计算机科学家、放射科医生、 天文学家、

chemists, computational physicists -- I mean, we've brought so many people together.

化学家、计算物理学家—— 我的意思是， 我们把这么多人聚集在一起。

And it's been enlightening to share domains and information across borders.

跨国界共享域名和信息 是很有启发性的。

And we're still going.

我们还在继续。

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.

尽管屏幕上的大多数人都来自哈佛 或哈佛医学院， 但现在我们跨越 不同的机构和大洲一起合作。

All I can say is, it has just been wonderful.

我只能说，这真是太棒了。

We're continuing to make new discoveries.

我们还在继续有新的发现。

And I just urge you: attend conferences not in your own domain, read books and journals not in your own discipline,

在这里我只能敦促你：参加不属于你自己领域的会议， 阅读不只是你自己领域的书籍和期刊，

watch TED talks and come to events like this and say hi to the neighbor sitting next to you,

观看 TED 演讲，参加这样的活动， 和坐在你旁边的邻居打个招呼，

because you really never know where your next great idea is going to come from.

因为你真的不知道 你下一个伟大的想法会从哪里来。

Thank you.

谢谢。

(Applause)

（掌声）