James Smallcombe: And then ice will say, Ruth, please tell us about your work.
Ruth: Okay. And thank you for inviting me here today. James And thanks, everyone, for coming to listen and say like James said I'm going to be presenting some of my groups work on how we can use quantum entanglement to enhance PET imaging
Ruth: So really brief overview. Then, and then I'll start off by talking about what is pet. So feel free to zone out if you're very familiar.
Ruth: Then we're going to talk about what the limitations facing at the moment, I'm going to talk about the effect entanglement has on our past events.
Ruth: And we're going to discuss how we can measure these effects and was immortal them, they'll be a really brief section on entanglement breaking in kind of tech felton's
Ruth: And then lastly, I'm going to show some really simple algorithms, we've been developing on how we can use this entanglement information to enhance our patents.
Ruth: So what is pet and I had to put a pet joking that I'm really sorry.
Ruth: So PET stands for positron emission tomography. It's a technique that we use to image metabolic processes inside the body so you can use it to find cancerous tumors or to monitor brain activity. They've recently shown. You can also use it to track the progress of Alzheimer's disease.
Ruth: It works by injecting something called a review tracer into the bloodstream. This is commonly an isotope of flooring attached to a sugar, it's injected into the bloodstream travels around the body and gets absorbed accumulating and areas with particularly high metabolic processes.
Ruth: When it gets our radio traceable then decay, which produces the positron positron travels couple of millimeters hits the nearby electron within the tissue and the to annihilate and that annihilation produces to perfectly back to back, photons.
Ruth: We detect these two photons by placing the patient inside a big ring of detective when we get to detection hits within the same coincidence window. So think four to 10 seconds kind of scale.
Ruth: Then we can say right YouTube photons. You must have come from the same decay, we draw a line between the two hit positions and then we know somewhere along that line. That's where the sticky took place.
Ruth: When we have enough lines, you can start to build up the picture.
So,
Ruth: This is of course the absolute ideal scenario, the photons exit the body they interact with nothing and then they're instantly detected in our scanner and we call this a true event.
Ruth: Or can actually happen is that one or both of the photons scatters prior to detection. So these photons, they could scatter inside the patient, they could scan it in the air on the table, even at the scanner itself.
Ruth: And now when we draw our line of response between the two hits you can see that it's been offset from whether the key actually to play.
Ruth: The third type of event is a random coincidence.
Ruth: That perhaps we have a very high activity or very bad luck. You might have to the case occur almost exactly the same time.
Ruth: It's possible. Your detect one photon from one day, one vote on some of the other day, if they arrive in the same coincidence window. We're going to falsely assume that came from the same event.
Ruth: And you can see once more, my line of response is now offset from both the actual became
Ruth: To be offset lines of response they create noise and artifact. And our final image.
Ruth: random coincidences are little bit easier to correct for generally forms they kind of uniform distribution of the background scattered event, much more of a problem. So they create noise around your actual regions of interest.
Ruth: Even more of a problem is that in a 3D scan, particularly around the torso where we're biggest these can make up 30 to 50% of all the events and detect which is a Frankie terrifyingly large fraction. So as a result, PET scans are intrinsically noisy, they have poor spatial resolution.
Ruth: So what can we do about this but hospitals will often combine a PET scan with something like a CT or an MRI these crates structural images of the body. They highlight our buttons and our tissues.
Ruth: They also have a really, really good spatial resolution so you can then take your functional PET scan and overlay it with these structural CT and MRI scans and that makes it much easier to localize these pet features within the whole body.
Ruth: There's another slightly less obvious benefits doing that and that's that these structural images they basically highlight all the parts of the body that are particularly attenuated to them.
Ruth: So you basically just made a map of all the scattering sites in this particular patient in this particular kind of format. So that is really useful. You can then use your scatter map that you just made to intuitively scatter correct your pet scan and that works really successfully.
Ruth: The downside of course other it is expensive and it's time consuming and the case, it's dt. We just exposed our patient to even more radiation than we have to begin with.
Ruth: And in the case of MRI. Not everyone can have an MRI scan, perhaps, if I had a pacemaker or some kind of implement that wouldn't be accessible to me.
Ruth: So this is where our work comes in really, we want to see if we can acquire similar information that only using the PET scans. We've all the pet events. We've already detected
Ruth: And we want to do this using quantum entanglement. So I have an annihilation. I have my two annihilation photon.
Ruth: They came from the same event. They are going to be quantum entangle the shareable a function anything happening to one of my photons. That's going to instantly affect the behavior of the other photo.
Ruth: However, if my photons were to scatter
Ruth: That is in effect a measurement of a quantum entangle system. And when we measure a quantum entangle system that we function collapses and entanglement is destroyed.
Ruth: Alternatively, if I had a random coincidence, then my two photons. They never came from the same place to begin with, so they wouldn't be entangled either
Ruth: This is great. This gives us something to discriminate by we need to know when we detect are two photons are they still entangled with each other. They are fantastic. Let's keep it, if not maybe reconsider how we use that data.
Ruth: So then, this leads to the obvious question, how do we detect entanglement.
Ruth: We do it by looking at the way the photons interact when they hit the detector.
Ruth: So I'm going to have my annihilation. The two photons exit the body they hit the detector surface and they're quite likely to Compton scatter at some azimuth angle by and it's the difference between these two angles delta phi. But what we're interested in.
Ruth: So it follows distributions, a bit like this.
Ruth: Is my two photons are on polarized or have random polarization. Then when they hit the detector surface they scatter at a random. And so the difference between the two angles is equally random. We have a flat distribution.
Ruth: If my two photons are perpendicular polarized like annihilation photons are from conservation of momentum. Then they follow the clay Machina distribution and we see a cause to delta phi crop up here.
Ruth: I think quantum entanglement on top of this perpendicular polarization. So the full picture here that purely has the effect of boosting the amplitude of this cause.
Ruth: So in short, it's much more likely that my photons will scatter perpendicular to each other. D 590 the most Catherine the same direction at the five zero.
Ruth: Okay so tiny bit of background here. Cool. And this physics is really old and it was first proposed by price and board in 1947
Ruth: So back in the day, we didn't have the detectors to accurately measure this cruise line. But what we could do is measure what we call the enhancement factor.
Ruth: And that's the ratio of the number of photons scattering perpendicular to each other to those in the same direction.
Ruth: So they would test experimentally using a set up a bit like this. And we're just enough, they had a source. They had a scattering medium on either side and then they had their detectors placed at an angle.
Ruth: Either those detectors are in the same plane for d five zero other and perpendicular planes.
Ruth: So this allows them to measure this enhancement factor and they showed both theoretically and experimentally, but it followed a distribution, a bit like this with Peter
Ruth: So there's a hell of a lot of angles here. So just to be really clear.
Ruth: I have my to Perth and the key polarized photons coming up, back to back, they hit the detector surface and they scatter with a polar and girls teacher and then either moves the lingo. Fine. There's the difference between these two files we care about.
Ruth: IT folks my poster angles are quite small. The mind enhancement factor is also similar with basically means the amplitude of that cause not going to be very big.
Ruth: boost my photon scatter with a theater around at
Ruth: Mine haunting factor is almost three. So that means it's almost three times as likely we see perpendicular scattering to powder scattered
Ruth: So in all of my least upload. If you ever see a fetal heart. This is what's going on with chopping our data around this region here where the effect of entanglement is the biggest we see the biggest amplitude
Ruth: Okay, so let's see if we now have the detectors to measure this cause. So for this we need a pet machine. This is our pet protector. So it's a sodium source police between two cadmium telluride see that T
Ruth: See that he is kind of Next Generation for preclinical PET scans or small animals in terms of like drug trials and stuff.
Ruth: It doesn't quite have the timing resolution for the full body scan, but it's perfectly adequate for this.
Ruth: But it does have is a truly, truly excellent energy resolution and position resolution and this makes it perfect for our purposes, because it's really easy to follow the trajectory of the photons inside of the crystal.
Ruth: That we would want to scan. We look for coincident pairs of photons. So that's when we have an energy deposit in both detectors in the same time window.
Ruth: When we get that we read out one of the detector heads. So this could be one head would have all my pixels here. And clearly, we had an energy deposit here and an energy deposit here with a bit of charge charm.
Ruth: We work out which hit. We think came first. So in this case, we think the photon kits here and then scattered here. And in this case, since it went down. That's going to be a five 180 and we compare that to the other detect ahead, and that gives us delta phi simplify
Ruth: So this was our experimental setup to give it a little bit of meaning. We also simulated it in a software package called gene for that. I'm assuming everybody here knows so models, the path of particles do matter and it's incredibly versatile.
Ruth: But one of the main benefits of it for us is that it allows us to to change the physics by which are photons interact. So we went three different experiments.
Ruth: In our first simulation we had unpoliced victims, so they would hit the detector and scatter at a random angle.
Ruth: And our second simulation we had perpendicular. Right, so they would follow this claim the Shiva and would expect to see a bit of a course.
Ruth: Or third simulation is our quantum entangled simulation.
Ruth: Now, quantum entanglement does not exist in June, for this is something we ourselves working with the developers have input into our program.
Ruth: So we think we're the only people in the world to be running a quantum entangled version of general, it's pretty neat. So we allowed to vote on to communicate with each other and then they follow the quantum entangle double scattering client machine a formula.
Ruth: We take all of our simulated data and we pass it through all the same analysis code as our experimental data and here are our results.
Ruth: So this is a normalized histogram of delta phi
Ruth: There are my three banks here of color. They are three simulations. So at the bottom and green. This is my on polarized data coming out nice and flat like respected.
Ruth: Embed. This is our perpendicular polarized photo, which is showing this close to five, we were looking for. And finally in blue. This is the quantum entanglement simulation.
Ruth: And bang on top of that these black data points are our experimental data. So the really, really nice agreement between this to it, firstly validates our simulation shows, we're doing something right here.
Ruth: And secondly, it makes this the first ever measurement of quantum entanglement in a pet like system.
Ruth: So we've been able to measure this cost before, but we've never been able to directly compare it to quantum entanglement theory.
Ruth: One thing I should possibly point out is that standard gene for us. We all use and download this is represented by the red car to clean it not reproducing the entire picture here, perhaps something we should think about in the future. And the way we simulate asking
Ruth: Okay, but from this was, Oh, I'm now happy that we know how to simulate quantum entanglement to pet events.
Ruth: Then all we have left on random past events and are scattered
Ruth: I don't think anyone will argue with me when I say that random or coincidences, they're going to be pretty well modeled by on polarized data here.
Ruth: We don't know where these photons came from that almost certainly not entangled, and they're probably not politically polarized. So this isn't safe bet.
Ruth: Scattered events. That's a much more interesting question. So scattered events. They were once perpendicular polarized and quantum entangled. They were my blue curve.
Ruth: When one of the photons scattered. We can assume that there's some rotation in the polarization plane. So perhaps the effects we get from polarization would be diminished a bit but not entirely last
Ruth: Secondly, what happens to the quantum entangled information just the way function entirely collapse, do we lose all of that information, always a little bit of it retained. And the simple answer is we don't know. So we run an experiment to try and find that
Ruth: This is a GM for rendering of our experimental setup. So these gray boxes of the side. They are just the key thing about detectives and the little red cubes of the actual sees it crystals.
Ruth: We placed a sodium source between the two. And then this time we put a little block of nylon right in front of this
Ruth: And then we rotated. Our second detector head by about 30 degrees.
Ruth: This meant the only way we could possibly detect coincidence between our two detectors is if one of the photons of scattered in the nylon prior to detection. So in theory, and this setup everything we detect should be a scattered
Ruth: So we ran this experimentally in the lab and we also stimulated it in our quantum entanglement gene for
Ruth: Now because we don't know what happened to the entanglement information, our simulation makes the assumption that all the entanglement informations last
Ruth: You haven't got anything better to go back. So we immediately back to the old paralyzed routines. The moment one photon scatters don't have to see how good of a model that is
Ruth: So these are the results so far. And once more. It's a normalized histogram of demystifying
Ruth: This blue data that's nothing new. That's exactly the same quantum entangled poking to keep organized true events I showed you on previous slide. So laptop Luca
Ruth: The orange curve is now the results of our scattered simulation. So remember this only uses polarization information will be left over here.
Ruth: So you can see that there's a little bit of a peek still remaining we haven't lost all of that total ization information, but it's a lot less defined than it was before.
Ruth: And then these black data points. They are the results of our experimental scattered setup.
Ruth: And this is kind of where covert screwed us over a little bit in that you'll notice they have some quite sizable error bars on that and that is purely due to low statistics takes a really long time to run
Ruth: So I don't think we can happily say we have the statistics to say that are scattered simulation is perfectly modeling are scattered photons, but we do have stats to say that are scattered data is not in agreement with our quantum entangled to
Ruth: This means that we can still use delta phi information to distinguish between are scattered events and our true entangled events. So that's kind of the main purpose there.
Ruth: But aside from its uses and pets. This in itself is a first of its kind. Measurement of quantum entanglement breaking in the MTV regime.
Ruth: This is something that I think has been looked into quite a bit and the optical vision but never have such high energies before so I'm really excited to get back to the lab and press play and see what happened.
Ruth: And I hope it shows the the potential for these detectors to ask some much more fundamental questions about entanglements at these high energy ranges.
Ruth: Okay. But the point of this talk is to talk about pet. So I now know that I know how to model. My entangled photons my random photons and my scattered so it's time to do a PET scan.
Ruth: Of course, we can't afford a PET scanner. So we simulated it and this is my detector here so it has four rings of these Academy and think Telluride crystals. We've been using all along.
Ruth: The object which has to scan is a NEMA mouse Phantom. So a phantom is something designed to mimic the attenuation have a real patient
Ruth: Or in this case IMAX so it's this little cylinder of PMA plastic and it has five votes that one through the middle. And these ones contain a mixture of water and radioactive source.
Ruth: So think of these words like the two men in the patient. This is the thing we're trying to measure.
Ruth: So we would then sell fast can we look for pairs of coincident photons and we record those lines of this month. And we make sure we also record the delta phi angles the photon scattered in the side of the detector.
Ruth: And this is what we see. So you'll notice again, I've got this feature tickets here and this is just dropping in the energy range around equals ATT. So this is the bit where the entanglement effect is the biggest the aptitude is the largest
Ruth: Suddenly first image. I'm going to reconstruct using only lines of response which scattered with a delta phi around there.
Ruth: So these are the lines we think are least likely to be entangled, so they should be most likely to be scattered or random events.
Ruth: If I reconstruct only using this, I get this image. So you can see my fighter periods running through the middle. But the contrast between hot and cold regions, it's not good. And it's quite noisy image.
Ruth: If I do the same thing again. But I reconstruct using lines of response with a slightly higher delta phi, we would expect a higher fraction of those events to now be true.
Ruth: And indeed the image gets a little bit clearer.
Ruth: And we can do the same thing again. It's a different doctor five regions, all the way up to defy of 90 and here you can see how much better the contrast got and white image I flick through it again because my computer's jumping
Ruth: So one thing that I think it's important to point out here is that there are more lines of response around mighty
Ruth: My history and works. But all of these images were reconstructed using the same number of lines of response. So this means the only possible reason we can have the SEEING IMAGE improvement here is that we're using a higher fraction of truth events.
Ruth: So I think that's quite a nice example of how purely just by chopping on this delta phi information we can separate out our data into more and less reliable vision.
Ruth: It picks be fairly obvious question that we have to ask, which is, What happens though if we just used all of the lines of response.
Ruth: And it's really not that bad. So this is a fundamental of pet. There's only so long. The patient can lie on the table. There's only so high, an activity, we can use. It's always always better to just collect as many lines of response as we can.
Ruth: So please don't fret second think we're suggesting that we check out 90% of our data. What we want to do is use this additional delta phi information to enhance the events. We've already got
Ruth: That, how can we do that. And if you flick your mind back to the beginning when I was talking about CP and about why and how you can use those to create scatter
Ruth: We were wondering if we could do something a bit like that.
So,
Ruth: The most theoretical at yes
Ruth: If you can see that this green line is what we measure. These are all the events we detect at the top green line.
Ruth: That is going to be the sum of the delta phi distribution from all our true events which will have some me and some amplitude and the derivative by distribution from Alaska for the events which again has something and some aptitude.
Ruth: Depending on the object. We're trying to scan. Perhaps it's really big and really attenuated than would expect a lot of scattered if it's really small and non there will expect far more truth. So these means they can jump around.
Ruth: To trial and error and looking at our simulation, we found that these attitudes, though, they're ready remaining pretty confident. So you only really vary by about 5% so for the purpose of this really simple demonstration we're going to take those afternoon.
Ruth: This allows us to write an equation. So, uh, pair for the delta phi to detect and 90
Ruth: That will be the some of the amplitude of the truth times. I mean, if the truth and the amplitude of the scatters Tanzania for scattered and we can write something similar for what we measure up there.
Ruth: This that means we can solve these two simultaneously to extract out an equation for the meaning of discounted events.
Ruth: As a function of things that we know. So these attitudes t and t naught and things that we can measure Peanut, peanut and we could do the same thing for our choose
Ruth: Let's see if this works in the image.
Ruth: So we reconstruct an image. And it's exactly the same phantom I had before. So you can see the five Capello is running through the middle.
Ruth: So this first image on reconstruct I'm only going to use those p 90 events so lines of response that are scattered the delta phi around 90 the ones we trust the most
Ruth: I reconstruct using those and then draw a profile through the bottom to propeller is, this is what we see. So he can make out Hillary one get the region.
Ruth: We do the same thing again, but this time we only use lines of response with delta phi around zero my p dot I reconstruct my image with those your profile to the bottom to we get the green line.
Ruth: This is a encouraging start. I mean, the events we think are most likely to be entangled. We're getting much more of them from our capacities and since this is where we put the source. That's a great start.
Ruth: And this equation we worked out on the previous slide, but basically tells me what we did subtraction. Do I need to perform to these two profiles to pull out the effect scatter has had to my profile.
Ruth: If I perform that weighted subtraction. I pull out this red line here. So this is purely chopping on delta phi information from events. We've already detected and used we can pull out a prediction for what scatter has done to the image.
Ruth: We can personally, I have no idea what this means. But fortunately, we're still working in simulation world so I can turn back to the info. I can ask for all the lines of response that we're definitely 100% scattered
Ruth: If I reconstruct only using those you're my profile where those capabilities would be, we get the blue line. So this is a kind of ground truth of what the scatter distribution really means.
Ruth: And we didn't quite a nice job of reconstructing those two edge features and then we are selecting quite a lot. It is a round real scatter perform
Ruth: In fact, if I say a polynomial to our data we're doing quite a nice job of approximating that background.
Ruth: So this is strata. We could also flip it on its head and say, rather than ask me for the contribution of the scatters. We can ask for the contribution the truth has made to this profile.
Ruth: So once more we perform a weighted to traction. Of the two people and we pull up this distribution.
Ruth: So,
Ruth: This is such a nice result that I should play it on our laptop, because you can't have those lines up in bed. That's what we get from extracting after true profile by doing a weighted subtraction and then blue. That's the kind of ground truth that we got from Jn for
Ruth: And there's really, really nice agreement between those two.
Ruth: Services scattered. Let's see if we can do the same thing for choose random events.
Ruth: So I run the scan again with the same phantom, but this time we stimulated a really, really loud random backgrounds to my image because see how much brighter. It's got
Ruth: I go through the same procedure. So I take my lunch at the sponsor delta phi around 90 I reconstruct and I draw profile and we have these two companies.
Ruth: I take my lines of response with though to buy around zero I reconstruct and I do prefer doing this weighted traction, we worked out previously we can pull out the contribution vandals have made to this profile and then comparing it once more to gene for
Ruth: As a really nice agreement between those two. And once again, I can put in a fit.
So,
Ruth: flipping it on its head asking for the contribution of truth. Once more we can pull out a weighted subtraction of these two and I plotted out there. So there's a little bit more oscillation around the baseline, but we've nicely defined those two components.
Ruth: And one thing you'll notice here, these peaks towards the end that's purely a geometrical effect of pet. If you do all the possible lines of response between my detectors, you find that that are more lines of a sponsor on the edge and going through the middle.
Ruth: This means if there's a random background, we always detect more of it at the edges than the center to why you can see these peaks.
Ruth: But purely chopping on this stuff to find the nation. We've managed to pull out the effects. This is having and then correct for is a really nice result that
Ruth: Okay. So in conclusion, we've incorporated quantum entanglement into the leading Monte Carlo software June 4 and then we verified this by comparison to a see that T pets demonstrated system, thereby making the first ever measurement of quantum entanglement in a pet like system.
Ruth: We let them initial investigations into entanglement breaking in the mid range and I hope this demonstrates the potential for these these detectors to prove them much more fundamental questions about entanglement higher energies.
Ruth: Within to get back to pet we simulated some fairly complex sentence and showed how just by chopping on delta fine, we can extract out war and less reliable regions of our data.
Ruth: And then we presented a really simple algorithm to show how using this stuff to find information we can subtract out the contribution of random scattered to it.
Ruth: So I think that's the key takeaway from all of this really is that this information exists in every PET scan we ever take all over the world.
Ruth: And it's not being utilized. So if we could find a simple way to incorporate it into our reconstruction algorithms, then we don't need expensive new detectors, or a new emerging protocol, it's all ready back
Ruth: Okay, thank you so much for listening and thank you to my wonderful team or giving them the hard work.
James Smallcombe: Thank you very much. Ruth was great. So we'd like to open up the floor to any questions. We've got again if you go to the participants menu. There's a little raise hand icon.
James Smallcombe: See a couple of this class is Stuart, please go ahead with your question.
Stuart Christie: Hi, um,
Stuart Christie: Thanks for interesting talk. I feel like a graph. This one rather better and so on previous ones, which is always nice.
Stuart Christie: But I wanted to ask about the the chopping and obviously what you talked about here was, you know, taking, for example, zero to 20 and 80 to 100. Yeah. And what I was wondering is, is there any potential to do something sort of more continuous
Ruth: Yeah.
Ruth: So,
Ruth: This was a very basic example of what we could do with
Ruth: Kind of the bare minimum of the data and using quite a fundamental to to back protection algorithm. So what we'd love to do is use more of the delta phi information. Hopefully the entire calls
Ruth: One of the things we're looking at in the future. Is this was all done using a filter back projection algorithm, whereas a lot of the more advanced we construction techniques are like maximum likelihood methods.
Ruth: They take weeks so you can wait different lines of response, according to different things. So if we could wait our lines of response with this cause function would hopefully be able to use all of the information in that way.
Ruth: Your question.
Stuart Christie: Yeah, thanks.
James Smallcombe: Okay, we've got a question from the chat window where Yang Yang son wants to ask, can you give information about what you have to incorporate into jail for to have entanglement.
Ruth: Yes and no. I will say that
Ruth: We're basically hoping to wait to be published. And then when we are
Ruth: All of this new stuff we've incorporated into into Gen four is going to be released with the next gen for after that. So it should be available freely to everyone. Hopefully quite soon. But basically we have some kind of
Ruth: Them note board for the two photons to
Ruth: Write to so that they know what each other. We're doing
James Smallcombe: Okay, thanks. Right now, you've got a question. Please go ahead. Yes, those nice talk at this is probably a very simple question. So
Ragnar Stroberg: If you have scattering. That should also change the energies of the two photons. Right. So can you use that information as well.
Ruth: I'm in what way do you mean
Ruth: This is the annoying thing with this project.
Ruth: But do you mean scattering in the patient or scattering in the detective.
Ragnar Stroberg: Um, I guess. Either way, the you should have some change in the energy of the photons effect so
Ruth: It's one of the ways
Ruth: We can't
Ruth: Really get rid of scattered
Ruth: Events is by having some kind of an energy window and saying, if my detected energy is not within this range around 511 we're going to cut that event.
Ruth: So this is kind of why it's nice to have such high energy resolution detectives with us. Is it, it is much easier to see which one's deposited all that energy
Ragnar Stroberg: Right.
James Smallcombe: Do we have any other questions.
James Smallcombe: I have a little one in your first comparison of your simulations, your three simulation to your data. Yeah, they seem to be a kink in around 10 degrees delta delta phi, but the king seem to agree very well between the date. Yes.
Ruth: Yeah, so here
James Smallcombe: And about 10 degrees that can't seem to agree with between the data on the simulation is so what's what's causing that.
Ruth: Yep. So it's a combination of two things.
Ruth: One is a billing issue that I found the other day, so that
Ruth: Can get smooth that about, but the other if
Ruth: We think it's due to some acceptance effects. So just because we haven't got infinitely launch detectors some angles are biased towards other than others. So the fact that it's in perfect agreement with the three simulations.
Ruth: Clearly means that we're modeling it well here, even if we don't fully understand where it came from.
James Smallcombe: Right, right. Wilton, would you like to go ahead with your question.
Wilton Catford: Well, I think it
Wilton Catford: So,
Wilton Catford: I probably wasn't paying as much attention as I should have done just at the time when you mentioned it, but
Wilton Catford: I going back to the previous question about the energy information. I guess I was assuming you use the energy information from the CSM team to work out fine by working out, which was the first year. Is that right, yeah. Yeah. Okay, so
Wilton Catford: Just pick up on the point you made at the very end you say all this information is in every past PET scanners that's being taken.
Wilton Catford: It couldn't actually be extracted at the moment. Correct. Because the simulators don't have probably the energy resolution to do that. That's why you need to see that tea. Right.
Ruth: Well, we started with the tea. The tea because we knew this would be the best initial bet, of course, because we have these properties, but I am now doing some initial investigations into doing the same thing in life and
Ruth: In relations, it's going to work just fine. I'm sure a little go to hell. When we get to lab but if
Ruth: You get that
Wilton Catford: If you've got that sort of resolution you presumably also get a good improvement in the image just by taking the, the energy cut you mentioned before, to get rid of scattering to try and get very close to 511
Ruth: Out of course is that would see that t. We have a new or timing with a notion that we can't use such a high activity.
Wilton Catford: Yeah yeah yeah it's timing resolutions, a real sort of challenge in, in fact, okay good. Thanks very much.
Wilton Catford: Okay.
James Smallcombe: We've got another chat question. So Rebecca tryna on fast in your full patch reconstruction, what type of detected crystals do us
Ruth: To get the same thing.
James Smallcombe: And do we have any other questions, I don't
James Smallcombe: This is a problem. I usually ask if you have trouble with the raise hand for whatever reason. You're welcome, just unmute yourself and go ahead
James Smallcombe: But I don't see any
James Smallcombe: So I will say,
James Smallcombe: Thank you again, Ruth through great tool. And thank you to everyone for coming and joining us. If you're interested in these topics.
James Smallcombe: On Wednesday, we've got us students session where we've got Sophia PELs who's going to be talking about validation in Monte Carlo simulation for SPECT imaging so
James Smallcombe: That's that's on Wednesday and students session which you remember that. So different address. So that's in the emails and the website because Manchester hosting student sessions.
James Smallcombe: And do encourage your students to come along and support peers for that. And then we'll be back here in the plenary sessions on Friday for another talk again. All of these are 3pm PST
James Smallcombe: And we've got Manuela Cavallaro who is going to be talking from I NFL who's going to be talking about nuclear reactions to the neutrinos don't beat. OK, so the upcoming tours you thank you once again everyone, for joining us. And thank you very much. Ruth for a great talk.
Thank you. Bye for now.