Speaker 1:	Thanks a lot, Eric, and thanks for being brief, and thank you also on behalf of Ann Marie, I will tell her.
	I appreciate the briefness, because I'm always concerned about going too long, and as an oral outline for you, I really have two subjects to talk to you about. One is, of course, as the title implies, preeminently here, Diffusive Optic Tomography on breast cancer, but that's a project I've been involved in a lot, but I'm no longer involved in, it's in the clinic right now. The other is a new project we started in the group. The group is the, as Eric mentioned, Biomedical Imaging Group at the University of Pennsylvania, Department of Physics, Professor Arjun Yodh. That area is Laser Speckle Imaging, and it has relevance in ischemic stroke. 
	As some of you might know, and as Eric implied, my background is in traditional Laser Atomic Physics and Spectroscopy, but as my abstract to you implies, I've become amazed and infatuated and aware of the fact that in the 21st century Physics is making a lot of contributions in areas outside of the traditional purview of Physics. Medical Physics is just one of those areas, and this is biomedical imaging, which is part of that. The field Medical Physics is a vast field, and we've been able to offer a freshman seminar for the last two years, and it looks like again next fall, which I'm very happy about. A few of the students are here, which I'm very happy about, thank you especially for coming. It's such a beautiful day, as Eric said. You could be outside playing.
	Let me set up the problem as I've come to understand it. The idea is imaging cancer. At the very basic level there's a problem. Cancer cells, especially in the early stage, are very indistinguishable from your normal cells, in the macroscopic sense. Cancer is not an invading organism, your autoimmune system, your immune system has no defense against it. Basically, cancer is part of you. If you want to really be frightened, and also be entertained, read a book entitled cancer "The Emperor of all Maladies," by a physician named Mukherjee in New York. 
	Basically, the issue is trying to discern cancerous regions from non-cancerous regions. Radiologically speaking, with respect to x-rays, we have to content ourselves with certain, if you will,  proxies, or markers. One ... and each of these I'm going to tell you about in radiology has, x-ray radiography, has some problems. First is blood. Cancer, as you know, is rapidly multiplying. It needs blood, vampire-like. What happens is regions that are cancerous manage to grow new vessels, new arterioles. These vessels are often imperfect ... literally some of them have dead ends, and they're also a bit leaky. The first issue is there is more blood in a cancerous region. Even from very early on. Because the new vessels are leaky, there's differences in osmotic pressure that cause there to be a higher profusion of water in cancerous regions, also. Water is another proxy, if you will.
	The other thing is the chemistry starts going on in the area where there's leaky blood and leaky water, I think it has to do with reductions of pH, but that causes certain cations that are normally in your body to precipitate out, like calcium in particular. Calcium, little micro-calcifications, solid calcium can be present in cancerous regions, also, especially early cancers.
	Now, the problems are the following ... If you wish to image blood water as a cancer marker, especially with x-rays, the problem is contrast. An x-ray image works by absorption. You're seeing this slide because the light is coming through a piece of film in the slide ... that's not true. It's being absorbed in various regions more than the other, that's basically how an x-ray works. But the amount of absorption, like there's a lot of absorption right here where the "M" is, and very little where the white is. 
	Blood water, and in the presence of the background tissue, mostly lipid in body, has very, very small contrast. With the typical x-ray energy of 20 kilovolts in a ... for example, a mammogram, the contrast is about 4%. The maximum contrast is 4%. That's a problem we did in seminar. Think of a black and white image that you see that's suddenly very grayed out. The difference between the maximum and the minimum intensity is not much more than 4%. Many x-ray studies ... many mammography installations have digital enhancement afterwards, although I read this morning that not all, even at present, not all mammography installations have digital detection. At your next mammography, ask if it's film or digital. That's the issue with x-rays, it's the contrast.
	Calcium on the other hand, that precipitates out, is like bone. Calcium is magnificently imaged with x-rays. They are small, so that's a problem, but worse than that ... in fact they're often less than a millimeter ... but worse than that, it's a rather poor proxy for cancer. It is neither necessary nor sufficient for a diagnosis of cancer. 
	Indeed some cancers don't have calcium precipitate out, some do. Some patients do, and some don't. Traditional mammography relies on these sort of situations. This makes mammography a somewhat ... well, as you've read as much as I have ... there are a number of false positives. The number is huge. It's between 60 and 85% of positive screenings for breast cancer and mammography are false positives. There's also a bunch of false negatives. There's more false positives than negatives, otherwise it wouldn't be good at all. You know the situation, if you have a false positive, the next step you're gonna get is, in addition to being very scared, is to have an invasive procedure. That's pretty much the issue with breast cancer ... with breast cancer and mammography in the language of medical statistics, the specificity and the sensitivity of mammography to detecting cancer is not very high.
	The issue is, then, what to think about. The next thing to think about is, again, x-rays are electromagnetic radiation of about 20 kilovolt energy. What about going down on the energy scale? Down on the energy scale to visible light even, and so you start thinking, at least at first, and part of this is ... I'm a little bit autobiographical here, it's how I became interested in the field. First of all, thinking that visible light, you can't shine light through the body, the body is not transparent. Maybe some people are transparent, but the fact that I don't see light coming through the body, but that actually not very true. 
	You've seen a picture like this. Maybe you've played with a flashlight in your bedroom, in the dark of night, you have white light behind it you get a bunch of light through it. In fact, white light behind it, red light's coming through, so obviously the flesh, the tissue is absorbing more green and blue light than red. The red is getting through pretty well. It's call transillumination. Here, you can transilluminate yourself, if you want. I have a red laser pointer, you can see a lot of red light going through, and even a much brighter green laser won't get through very much. Transilluminate yourself. Who wants it? Go ahead. Parts of your body, but not your eye, and nobody else's eye. Oh, there's also transillumination ... my friend, Randy Exon, told me about an artist named George Latour, and yes, in art they knew about transillumination. Here's a young Jesus having his hand transilluminated. We learned today in the New York Times he may have had a wife.
	This is the reason that so much light is being seen. Here is the absorption coefficient of flesh, of human tissue. Actually, not human tissue, the components, the so called chromophores of human tissue, as a function of wavelength, which is color, of course, and as you can see from the red to the near infrared, there's almost no absorption on this scale. The components here are lipid, most of your body, alas, is fat ... well, besides water ... and blood. You remember that hemoglobin, of course, one of the roles of blood, is to carry oxygen around. Oxygenated hemoglobin and non-oxygenated hemoglobin actually have different spectra, adding the oxygen molecule to the heme group changes its spectra. I don't know the biochemistry.
	Blowing this up a little bit more is very interesting because you see that's the same graph from the previous slide blown up ... Y-axis ... expand that X-axis, again, the various chromophores in tissue, they have contrast depending on the wavelength. Look at this. The non-oxygenated hemoglobin has much more absorption than the oxygenated hemoglobin, and that's very interesting, because I told you that the problem was contrast with x-rays. Here we have some in various regions good contrast.
	In Physics, this is characterized by the absorption coefficient. It's units are inverse centimeters. What that is is the inverse of that, which of course is 10 centimeters, is the amount of distance light would travel and get attenuated down to 37%. That's actually pretty impressive. 10 centimeters is about that big, and light coming in ... with absorption alone, light coming in and exiting would be attenuated by 73% down to 37. That's the issue with respect to absorption, and that's sort of the good news.
	On the other hand, if you're transilluminating yourself on your hand, and ... that hand ... the problem is not absorption, but the problem is scattering with human tissue. Scattering is seemingly the nail in the coffin about using visible light. Scattering is not the loss of light, like absorption, but scattering is the change in the direction of light, because it interacts with intervening particles. 
	This is a very nice set of pictures taken by one of the graduate students. It's a laser beam coming straight down, and entering water, and this water is particularly clear, and you could see all of the laser light ... all of the photons, if you will ... are traveling straight down. Their direction is not changed. If you start titrating in some scattering, and plain milk will do, a few couple of drops of scattering, right? You can see that most of the light is still has its original direction, but there's a lot of light coming from here. There's a lot of light that simply got zigzagged around, and out to you. 
	If you titrate it even further, you can get to a point where almost all, indeed all, memory of the initial direction is lost. It's almost like there's a point source of light here, right? This is what scattering is, and for human tissue, scattering is huge. The scattering coefficient for human tissue is 10 inverse centimeters, flip that around, is one millimeter. If you will, in one millimeter of travel in flesh, in human tissue, in one millimeter, the direction of light is lost. That's a problem, because you're seeing this slide, you're seeing me, only because the light that reflects from me travels to your eyes by a straight line. That's the information. If it were to not travel in a straight line, if would be randomized, you wouldn't see me. You'd see a cloud instead of me.
	The issue is that although the absorption is pretty low in flesh, the scattering is about 100 times higher. This particular regime where the scattering dominates the absorption is called the diffusive optic regime, and it's about 100 times greater. 
	You would think this is a fatal issue, and let me explain how it isn't, and what impressed me. This is possibly the most important slide of the talk, for you to comprehend what's going on. The letter "S". The letter "S" carries information. You know it's the letter "S". You're seeing it again, because the light is reflecting straight to you. If I start changing, if I start putting scattering material in front of that "S", some of the light will continue to reach your eye, you could still see that it's an "S", but of course there's some light that is not coming straight to your eye. Even more so, there's a lot of light that is scattered, I could sort of still make out the "S". 
	Finally, we get to a point where there's so much scattering that, at least to our eye right here, there's no information. The information is lost. This light, the peripheral scattered light, doesn't carry the information of the "S". The information of the "S" here is progressively lost. Diffusive optic tomography using visible light is what extracts from this pattern, the original "S". 
	Now, I read about this in a "Physics Today," and I thought it was impossible. I didn't understand how it could be done. After all, what's happening here ... again, these rays that are traveling to ... by the way, interrupt any time if you need to with a question, that's okay. These light rays that are traveling to you carry the information, when they get randomized that information is lost, and more of it is lost when there is more scattering, and more of it is lost here. So much of it is lost here that you can't gather the original information that that's an "S". 
	How can I undo that when information is lost to a random process, it may sound non-Kosher to you that you can't get it back, and you can't. I thought it was impossible, and if it sounds un-Kosher to you, you're right. There's a law. Of course, the second law of thermodynamics forbids getting information that is lost to random process and reconstructing that image.
	That really got me hooked, and I started delving into the issue, and it's very clever in that I wasn't reading the law correctly, and I didn't understand the loopholes of the law. I'm not a lawyer. You pay a lot of money for lawyers. That's the following ... if you have a lossy system, and you don't have enough information coming out, you don't have enough information that you want, what do you gotta do at the input end? If you want more information out at the output end, it's a lossy system. You better put more information in the input end, right? Okay?
	Well, how can I do that? I'm not going to get all the information out. Let's face it, all I want is to determine that's "S" for Swarthmore, not an H for [inaudible 00:16:34], right? I need to pack that "S" with other information that might get lost, but I need it. At the moment it's red. Suppose we made it red and then blue and then green, or something. This pattern would change. There would be a different distribution of scattered light if it were red, blue, green. While it's one of the colors, or during, suppose I moved it. If I moved it around, this pattern would be different. Well, it won't be different, because I'm assuming here if it's homogenous, if the scattering is due to smoke in the room, that would be homogenous. But if the scattering is due to a tumor over here, I certainly could move it around, and that pattern would change. I can not only have color as data, I can pack position of the source as data, I can also move the detection part around, and that's another piece of data. The detection position ... source, data, wavelength.
	Now, a really important one is the following, when it has a particular color, red, blue, green, the color right now is constant in time. It's DC. Suppose I flash it on and off regularly, smoothly. Suppose I modulate it. If I go from DC to AC, that's always a big step in science, because you pick up two for one. If I modulate it between, say zero and one, I may get this pattern blinking between 0.5 and 0.8. Ah ha. Data. A change in modulation amplitude. Not only that is the degree of in sync-ness. I could be flashing the "S" like this, and my receiving could be flashing ... it certainly better be at the same frequency, but they'll be out of sync. That's phase. When you go from DC to AC, you pick up two for one.
	As you can see, we're packing more data into the problem. Again, I don't care about the phase or the color, I care that this is the letter "S". If I'm putting in a lot of information, and I lose it, but I just get the "S" back, I have not violated any law. There's no violation of physics in doing this. It's still very hard, but there's no violation of physics.
	We'll start to ... I mean, again, that's the key slide. Here is the source plane. This is the source of light that is used in these imaging experiments. This is a source of light, there is a diffusing object, an object obscured by diffusion in front of it, and then there's a detector plane. The source is 11 by 19 rows, there are 209 sources there, and we scan through them. I think this movie might work ... or maybe it won't. It's the world's boring movie, but okay. Those are distinct sources. 219. The detection plane has a bunch of detectors, it's the film plane of a camera, a CCD camera, so it has 512 by 512 detectors, pixels. We have those as well. We have as much as nine wavelengths, and each is modulated at 70 ... it turns out to be 70 megahertz.
	Let's follow the information budget here. You have 209 times 262,000 ... why don't I just stand here and just ... no, that would scare you. You have about 50 million source detector pairs. If you multiply that by nine wavelengths, then multiply that by three for DC, AC, and phase, you get in fact three times 10 to the 8th, 300 million independent data points. Pieces of information. As long as I extract out some information that is less than three times 10 to the 8th, again, I haven't violated any physics law. That's the scenario. That's the basic scenario. We'll get a little deeper into things.
	Why diffusive optic tomography of the human breast? Well, first of all, the breast is of the order of 10 centimeters, with variation, or at least the distance between the system and any object you want to image is on the order of 10 centimeters. It's an easy organ to have access to, it's not an internal organ, it's semi-internal, and of course the issue of breast cancer is a major concern today. In the U.S. alone there are 300,000 new cases per year. 40,000 deaths. It's the second highest morbidity for women with cancer behind lung cancer. There are 2.8 million survivors, or current active patients, of breast cancer, and it's 12%. One in eight women in the country today, or in this room, will have an experience with breast cancer. There are also 2,000 men who get breast cancer, by the way. Especially men with larger breasts. I'm a hypochondriac. 
	Here is the scenario for the instrument. The patient lays on her tummy. The table is what's called a biopsy table. It has a hole in it, and the breast hangs down. The most clever thing ... I mean, this is that switching system that takes nine wavelengths and puts it into 209 positions, the intensity of which is modulated. The important thing is right here. The object that is being imaged is, in fact, not the breast. This thing is a tank. It's a rectangular parallelopiped tank. The tank is filled with a fluid, a liquid whose scattering and absorption coefficients match the scattering and absorption coefficients of healthy breast tissue. 
	Why is that important? That means the object now is not the breast, which is a complicated mathematical surface, to say the least, the object is this cartisian rectangular slab. I'm going to show you in the next slide that the master equation for this is a differential equation, and if anybody here in math, or anybody in ... any physical scientist, or anyone else, knows that solving a differential equation involves solving the equation, but also knowing the boundaries. The boundary here is rectangular. Cartisian boundary. The boundary is considered, listen carefully, because this is important, it's a finite width here, D if you will ... the assumptions for solving the differential equation, the assumption of the boundary, it's a finite width here, it's infinite in this direction, and also infinite in this direction. It's a slab. It's called a slab solution of the differential equation I'm gonna show you. 
	If this were to work, what are the advantages? Well, of course, it's non-ionizing radiation, so actually the dose ... because the energy of the x-rays has to be low in mammography, the dose is fairly high. This is non-ionizing radiation. Frequent measurements could be made during the treatment of a patient. Low cost ... who knows what that means. Well, as soon as it works, it won't be low cost. 
	The other thing is what's called neo ... it could do neoadjuvant measurements, that is to say after, or even before a patient undergoes treatment for cancer, most often surgery, the physicians might start chemotherapy. You might know that one of the important issues with chemotherapy is ... and this is very different for different chemotherapies, and different patients ... does the chemo get taken up by the tumor? It's very important if the chemo doesn't get taken up by the tumor, and that's a long process. Normally what they would do is start the chemo treatment, and see if the tumor changes over time. Because of the contrast that we can do ... in other words, the graph I showed you before of absorption coefficient versus wavelength, if you knew the absorption coefficient versus wavelength for a particular chemotherapy, particular chemo, chemical, you can also dial in an image of the chemo, a map of the chemo. A functional image. That could be done instantly, in just a few hours, cycling through each chemo. The possibility of doing this as neoadjuvant imaging is important.
	It's also ... I'll show you the very last slide. A lot is happening today with integrating various systems of imaging, of diagnosing ... MRI, ultrasound ... so, it's feasible to integrate. 
	I'm not going to ... I don't have time about the hardware of the machine. Techies in the audience could talk to me later. I'll just show the laser engine room. This is where the lasers live. There's only five of them right now, but there should be more. We're adding more wavelengths. Much of this was designed and actually made here in the shops of Swarth. 
	Here we go, for a little bit of math, but don't panic right yet. I'm just going to give you a guided tour of this equation. This is the master equation of diffusive optics. It's the F equals M A of the subject. It comes from standard physics, plus an approximation. What we have is a term that represents the source of the light, it's right here, this means it's a point source. That's all that means. The mu sub A is what we talked about. The mu sub A is the diffusion, is not ... it's the absorption coefficient at R at the position throughout the scattering medium. The D is the scattering coefficient in various parts. It's what we want. We want mu, and we want mu sub S and mu sub A. The phi is the scattered light. It's what you detect. 
	This is the master equation, except that in say a physics class, a class of mathematical physics, the problem would be given in mathematical physics in the following way, this is important, we're going to dwell on it a bit. It would say, "Given a source of light, and a particular absorption, distributed in the space, and a particular scattering distributed in the space, find the scattered light." That's how it would be given in a physics class. 
	What we want here instead is the inverse of that problem. We want to say, "Given the source of light, and the scattered light, find the mu sub A and the mu sub S." Now, that might seem like a trivial difference. An equation like that is eminently solvable on a computer, difficult but possible, not impossible. The fact that it's an inverse might not worry you too much, but here's where I have an example. A simple example. Here's a problem. Let's take a simple forward problem. F of X equals 3X plus 2. Find F of 2. F of 2 is 8. I debated for a while whether to make this a question in the audience. Maybe one of my humanities friends, but I ... the only humanities friend I know well enough is my very dear friend William Turpin, and when I told him I might do this, he remembered he had to be in Italy today. Right Ellen? He's in Italy. Would he know the answer to this? You don't have to tell me. No, I didn't think so either, but we never talk about it.
	This is a forward problem, very simple forward problem. The inverse problem would look like the inverse. "Given F of 2, find F of X." Well, okay. You might, again, you get a computer to guess. Computers are very good at guessing. It might spit out the one we wanted. F of X equals 3X plus 2. It's a solution. But you know what? If you waited around any bit longer, the computer would start spitting out other answers, and they are all right answers. All of these F's are such that F of 2 is 8. My gosh, the inverse problem is solution is generally not unique. The forward problem is generally unique, I don't want to get into theorems, and my math friends, none of which are here. Oh, Deborah. The inverse problem is almost always not unique, and the forward problem is almost unique. We're dealing with an inverse problem.
	Well, we can take it a little bit further, and things get a little bit better. What if the inverse problem has more data? Suppose I'm given F of 2 equals 8 and F of 3 equals 11. Find F of X. Well, of the five functions spit out in the previous slide, three of them would be vetoed. By going to more data, I've gotten somewhat more unique. I've reduced the non-uniqueness. That's a factor. It's obvious what I said here, forward problems usually unique, inverse not usually unique, more data helps, but not too much data. You could actually over specify the problem, there can be too much data in which there ... that becomes no solution. The mathematicians call this an ill-posed problem. That can be an issue as well.
	The bottom line is what do you do with this non-uniqueness of an inverse problem? You have to make some additional guesses. For example, if this was a physics problem, and I knew at priory that the answer that I'm looking for couldn't be quadratic, I could eliminate this. See? With other information. Other information. I would be left with one. That's the kind of thing you need. 
	For example, in the breast situation, before the fluids are introduced, and before the procedure is started, but in the position, a profilometric picture is taken of the breast. A three dimensional picture, so that the breast boundary can be seen. What that means is if you'd go ahead and do the inverse problem, and a target reconstructs, and it's outside the region of the breast, that's obviously not a solution. You'd throw it away. I believe the computer scientists, who are also not here, call this regularization. When you have to make yes, yeses. An inverse problem I want that to be the takeaway. In other words, it needs help. Now, most of these patients will have already come with MRI pictures, so that can guide us as well, but ... I say us, but I'm involved in the clinical part of it.
	All right, now we can talk about some examples. This is a structural image of two subjects, two patients, and we're gonna make believe that we don't know what's going on in these two patients. We're going to use physics to tell the answer. If there were a cancer, a radiation oncologist, or a radiologist in the audience, he or she would notice immediately the spiculated fractal nature of the borderline of this tumor, which is a signature of a very aggressive cancer. This is a bad diagnosis. But nevertheless, let's examine these two cases under diffusive optical tomography. This, as I said, our instrument at the moment has no results. Getting its wrinkles ironed out slowly in the clinic. But this is the second generation, we're working on a third generation instrument. The second generation instrument was only DC, had no AC, it had less source detector pairs. It did use five wavelengths. 
	This is the patient on the right. These are the maps of what we are able to reconstruct. Here's the map of mu sub S. This is the scattering, and sure enough, in the region in question, there is a higher scattering than in the surrounding areas, so there's more dense tissue there. This is not an early diagnosis of cancer. There's a great deal of scattering there. That just tells you there's more tissue, denser tissue. 
	Let's look at blood, though. Hemoglobin. Total hemoglobin concentration. Ah ha. Hot spot. The total hemoglobin concentration in the suspected area is a lot higher than the side area, than the surrounding regions. Very suspicious of cancer. It has angiogenesis, it's high in blood. What about ... I said we can ... because the chromophores of oxygenated and de-oxygenated hemoglobin have different spectra, you can produce a map of O2 saturation. How much is the tissue and the blood oxygen containing compared to the surrounding tissue? In this image, it doesn't show up. This does not show a contrast between the suspicious region and the surrounding region on the degree of concentration. Now, that might be ... this is an interesting little panel here ... that might be a failure of this machine, or it might be the fact that not a lot is known, not everything is known about the metabolism of cancer. Normally, metabolism is aerobic, that is to say it ... you probably know it changes glucose into energy, into ATP, in the presence of oxygen ... the dreaded krebs cycle ... that chemists, pre-medical students have to memorize ... in the presence of oxygen, so that's aerobic metabolism. It's the kind of metabolism you do in the gym when you go on the treadmill.
	It may be anaerobic. There's an anaerobic route to metabolism, also, and maybe cancer does, as a guy named Warburg has hypothesized that. This slide may have some interesting-
	What the physicist would do is say, assuming you would expect the lower O2 sac here, is you can generate a cancer index. It would be the amount of blood multiplied by the amount of scattering, in other words this guy, suspicious. Times this guy suspicious, divided by how low the O2 sac here, and that would be what you'd want to sort of call cancer. I want to be careful. We're doing okay. I'm sorry, yes Ellen?
Ellen:	In the three images where you see the hot spots, are you focusing on the dark part in the middle of it, or in the dark plus the red, plus the yellow?
Speaker 1:	Well, basically this outline is what's given by the analysis, the 3D analysis. This is an axial and a sagittal image from the MRI, they construct it into three dimensional- so this black outline is quote unquote I guess some criteria for the tumor boundary. We're just looking at what's really inside, and it's way higher inside. The scattering is way higher. Yeah, does it diminish gradually outside? Sure. [inaudible 00:35:39]
Speaker 3:	But that line is not an artifact of the skin?
Speaker 1:	No, I'm sorry. That line is artificial. That line is put there. No, no, absolutely not. We'll get to artifacts in a minute. 
	Here's the other case. The other case of suspicious lesions in there, and the region that was given by the MRI is this little circle, and we see that this little circle here does show somewhat higher density. This is the scattering. Higher density, but no hot spot at all in blood. Again, we're led to want to conclude that this is probably not cancer, because of the lack of angiogenesis here. It's just what they call fibroadenoma, fibrous breast disease. FBD. 
	Now, speaking of artifacts, what's going on there? There? Over here? Well, the answer is nothing, but yet there is some image there. For that we'll look at a little breast anatomy, and we'll talk about the assumptions I made before with that slab. 
	This is the breast anatomy, in fact, of a very pre-menopausal breast. It's got fatty tissue, and glandular material, and various ducts. That over time, with age, becomes much less prominent in glandular, and becomes totally adipose, totally fatty. Physicians refer to post-menopausal breasts as fatty breasts, this is a glandular breast. That's good news, by the way, that's Murphy's law working for you, because the median age of onset of a diagnosis of breast cancer is 61 years of age. It's much easier, no matter what imaging technique you're using to pick out a target in a simple area, as opposed to a complex area. 
	But, back to the artifacts. The artifact is that, again, with the slab solution, I'm finite in this direction, I'm infinite in this direction, and I assume infinite in this direction, but are we really infinite in this direction? Hell no. Right? There's a boundary here, the chest wall. What you're seeing on the previous picture is artifacts due to the chest wall. This questionable area is too close to the chest wall, to give a reliable ... If you notice the other one was ... that particular cancer was very far from the chest wall. That's the issue. The chest wall is an issue in the approximation that we have made.
	We did an experiment on the bench. A bench top experiment, not a clinical experiment, in which we imaged a target, and in a solution of intralipid, and it had the proper contrast only in absorption. The background intralipid 0.05, the bar target typical of healthy breasts. The bar target typical of a tumor. The scattering was assumed to be the same because it would be too hard to get scattering, this was only a DC experiment it turns out.
	Images were taken as a function of the distance of this phantom chest wall. Sure enough ... oh, well, this just shows you how impressive ... I mean this is the set up. This is a clear tank, this tank is finite thickness, that's the target, 0.9 centimeter bar, opening point 9.9, it's then filled up with the intralipid, and the idea is you shine light at one end, you barely see light coming out of this end, but with cameras you can capture the scattered image, and then you can reconstruct. You do that as a function of this sort of guillotine-like thing coming down. Sure enough, as the chest wall gets closer to the tumor, or vice-versa, the ability to reconstruct the image disappears. 
	We looked into, what can [inaudible 00:39:44]. We tried two things. One is a different algorithm and computer program. A totally different reconstruction method. The different reconstruction method computer algorithm yielded no results. Nothing different. What did yield difference is vetoing some data. Numerically reducing some of the data. Basically what you saw on the previous slide, the best reproduction involves not using the 35 by 35 point sources, in this case, but by vetoing successive rows at certain distances from the chest wall ... so, in other words, in our 10 ... so, in other words, without any vetoing, they're 35 by 35 independent sources, of course. Then the source detector pairs are 20 million. If you veto row by row, if you veto 10 rows, you're only going from 20 million to 13 million data points, but the improvement is dramatic. The most dramatic thing you can do in this particular scenario to improve the image as a function of the closeness of the chest wall is to veto data. 
	Remember I said previously that maybe sometimes too much data can ruin you. All that's done in this case is this data is set equal to what it would be if there was no target there, the so called homogenous result.
	That worked, and it's a published paper last February. I'm okay with time just still. Oh, by the way, this experiment is what we call the Free Space Experiment, and it's done with the light source jogging from 35 by 35 positions, it's all done with mirrors, but the light laser intensity was one wavelength and DC. If we did this with AC, I think we'd get some improvement, also. That's on the list of things to do.
	I did want to tell you before we're done, about another area. This is a new area that I suggested the group get into, and we started last summer. It's called Speckle Imaging, and it has to do with pre-clinical, not on patients that are sick, but more or less our laboratory animals. Pre-clinical study of ischemic stroke. You'd think very briefly and quickly ... you probably know the mechanism for ischemic stroke is similar to the mechanism for heart attack. Namely, an artery or blood vessel or an artery gets occluded, and the tissue around it dies, infarcts is the famous ... and the same thing is happening in the heart attack, so much so that doctors were starting to call ischemic stroke a brain attack. 
	There is actually a difference. What's dying in a heart attack is the peripheral myocardial muscle. What's dying is muscle in a heart attack. What's dying in a stroke is neurons, it's brain. Turns out there's difference. The muscle cells die pretty morbidly. The neurons in the core of the stroke, in the umbral region die pretty irreversibly, but there's a penumbral region that can be resurrected, if you will. It's called a periinfarct depolarization. 
	The question is we'll be able to get information on that using something called laser speckle imaging. I'll cue up the laser speckle image just here briefly. You see that on the wall? If you look at that spot, carefully and slowly, you'll see it has sort of speckles in it. If fact, that can also serve as a test of your visual acuity, but I'll explain that later.
	Why is that speckled? It's speckled because laser light is coming in, of course, columnated, the rays are coming in parallel to each other, but then they're reflected from this irregular surface in irregular directions, and they take different path lengths to get to your eye. They take different path lengths to the eye, they were in sync before they got here. Laser light is called spatially coherent. They're all in phase. When they're reflected, they're out of phase, and that means they can ... any two waves could add up anywhere between zero and maximum of four times the intensity. That's what that speckle image is for, it's because the wall is rough, and the statistics of that speckle, namely how the contrast is ... statistics of that speckle can tell you something about the roughness of the wall.
	In tissue, what would happen is the following. Light comes in the tissue, gets scattered, these two rays are parallel to each other and in sync, they're taking different paths when they come out ... if they come out parallel, or even if they didn't, and they focus by your eye, they would be out of sync. The red ray and black ray could interfere, and give you zero destructive or totally constructive interference. 
	What if these scattering centers were moving? If the scattering centers were moving, then I'm going to have a random type fluctuation of all the speckles in it. The temporal scale of these fluctuations, in fancy language the autocorrelation function. Basically the time scale of these fluctuations will tell me something about the velocity of those scattering centers. 
	That's the experiment, to look at live brain under those conditions. We started it last summer. A very simple situation. Very simple. You shine the laser light ... this is the laser light in a lens. This is the camera. The subject lays here in this head vice called a stereotactic vice. The animal is a laboratory animal. It's a de-cranialized ... the animal technician resects the scalp, and the animal technician very carefully takes out a part of skull ... this is skull here, this is an anatomical features whose name I forget. That's live brain, and the animal is, as I said, sedated of course and alive, and is breathing any sort of combination of gas we control. 
	The idea is to image that, if you ... this is the raw speckle pattern of the light shined on that little brain section, and measured by the camera. You might notice that the contrast is lower here than over there. There's a difference in contrast, and when you reconstruct that, the raw data is the statistics of the contrast, when you reconstruct it, you actually see the blood vessels in that brain gray scaled by the amount of velocity. The volume of blood traveling through it. 
	The only issue here is that this is patently not a tomographic image, it's not slices like a possible ... with CT or with the breast diffusive optic tomography. This is just a two dimensional picture of a three dimensional situation, like a regular camera would do. 
	The idea, the breakthrough would be to do it and get depth information as well. All you have to do, in principle, is change the intensity of the laser to not be uniform, but to have a spatial structure. Stripes, if you will. It's called spatially modulated. Why? Well, here is a structured amount of light shined on a target, a phantom, which is similar to human tissue. I took this picture last summer. 
	If you look at the top, I took a picture of the top, you can see that that pattern ... and I should not stand in front of this ... but you can see that that pattern persists to some depth. Can you see that? It persists to some depth, and then it disappears. That depth penetration is a function of the spatial frequency, of how tight they are. If the spatial frequency were lower, and I unfortunately should have taken that picture as well, if the spatial frequency were lower, this pattern would proceed deeper. If the spatial frequency were higher, it proceeds less deep.
	Well, if you do the whole experiment that I showed you before using spatially modulated light, you are getting depth information. The hope is to do that image with perhaps tomographic in slices. I hope that's clear. We have no results for this, this is very short, and I'm nearly done. I am done. 
	I want to promise you I'll tell you more, my sabbatical is coming up next Spring. We'll get results from this, I hope. This is just basically a little one tiny area of physics and medicine. Many other areas exist. PET scanning, great bit of physics, is the gold standard in detecting metastasis. Cancer in the whole body. Ultrasound is very effective in, extremely effective in measuring the contrast between tissue and macroscopic amounts of water. If you have a cyst, ultrasound will ... MRI, of course, is the gold standard of resolution. You can see things sub, even sub millimeter with MRI. 
	As I mentioned before, user friendly co-registration is pretty hot today. That is mixing all these techniques together. Look at this wonderful ... this is a co-registrated image of PET scan and CT at the same time. You're seeing this basically almost this entire body in axial, sagittal, and coronal. The cancers have lit up, and this patient has multiple cancers. You can see that cancer in addition to all of the rest of the anatomy. This is a combined CT and PET scan. 
	It's interesting, we're talking about physics contribution outside of physics and medicine, and in fact one of the earliest contribution physics made to medicine is also the first Nobel prize. Wilhelm Roentgen took this picture of his wife's hand, I think some of you have seen it, the ring seems to be on the wrong hand, but I don't ... The first Nobel prize, I think 18, no I think 1900. There's a long history of physics and medicine. That's our title of our freshman seminar, Physics and Modern Medicine. Pretty much all I need to tell you about it.
	Except that this is an emerging multi-disciplinary field. You have people who are experts on applied math and the inverse problem, of course computer coders, because ... very interesting story about the computer situation. You have the MD, PhD types. There needs to be a clinical staff. It's not just physicists and engineers, but people who interface with the patients. A physicist can't interface with a patient in a lab. Physicists can't in a hospital, can't interface much with anybody. You can't go into a hospital and tell a woman, "Please take your top off, I want to shine red light."
	Of course there've been about five students ... one, two, three, four, five, six students from Swarthmore. Alex [Shang 00:51:17] did a thesis on the project, I think he graduated with high, or highest honors, I forgot, and is at Harvard now. We nominated him for an award, also. Jessica is sitting right there. Did you get patient certification? Yes, she's allowed to go in with the patients, I'm not. I have nothing else to tell you, my friends. Thank you very much for coming. [inaudible 00:51:43]
	Is there time for questions?