The Transverse Myelitis Association advocates for people who have Acute Disseminated Encephalomyelitis (ADEM), Neuromyelitis Optica Spectrum Disorder (NMOSD), Optic. Intracranial Vessel Wall MRI: Principles and Expert Consensus Recommendations of the American Society of Neuroradiology.

Transcript: January 2018 Podcast David A. Bluemke, MD, PhD I’m Dr. David Bluemke, Editor of the Journal Radiology and I’m here today with Dr. Aleksander Kosmala from University Hospital in Wurzburg, Germany. Did I pronounce that right? Aleksander Kosmala, MD Yes, Wurzburg. And we’re going to be discussing his paper that’s going to be appearing in the January issue of Radiology, “Multiple Myeloma and Dual-Energy CT: Diagnostic Accuracy of Virtual Noncalcium Technique for Detection of Bone Marrow Infiltration of the Spine and Pelvis”.

So this was a very interesting paper and we’re gonna go through a couple steps and have it explain it to us today. As we start here, could you first tell us what was the motivation to do this paper?

Why did you choose this topic and how did you come to eventually study multiple myeloma? So we had our first experiences with bone marrow imaging and dual-energy CT with traumatic bone marrow changes and then we also saw that every patient with multiple myeloma or we suspected multiple myeloma sooner or later will need to have an assessment of bone status and this is routinely done by whole body computed tomography.

So this computed tomography is in the axial skeleton only to detect osteolytic changes. So let me ask you this question. You started applying this technique to traumatic bone lesions. And that would seem to be a harder problem than tumor perhaps. How do you compare the challenge of using dual-energy CT for trauma with edema detection versus what you were trying to achieve with tumor detection?

So in theory tumor detection should be even easier than detection of traumatic bone marrow edema. Why do you say that? Because edema should have attenuation of around zero Hounsfield units. Around water? Around water, yes. You’re trying to find that, okay. So whereas malignant infiltration of the bone marrow has potentially much higher attenuation numbers.

Okay so you have a cellular infiltrate in the marrow that you’re trying to detect with metastatic disease. And you mentioned also the use of CT. Some sites are using MRI for evaluation of bone marrow especially in multiple myeloma, but also CT is becoming first in your institution because you’re doing body evaluation?

I think at most institutions CT comes first. It’s more available, it’s faster, and it doesn’t take so much time and there are not so many contraindications compared to whole body MRI. MRI certainly has a place for specific patients groups.

For example patients with asymptomatic myeloma it has been shown that the detection of bone marrow infiltration, the number and the size of bone marrow lesions have certain prognostic and therapeutic consequences. So MRI certainly has a place and a role, but most commonly I think whole body computed tomography is used. So the idea from the start was if you can do the body screening with CT and improve the quality of the CT in some cases, the MRI may not be necessary. Is that the idea? That was the basic idea.

We thought to at the pros of MRI to be able to detect even non-osteolytic lesions through the standard CT which up to now in the axial skeleton can only detect osteolytic lesions. That’s a good point because CT we’re looking for lytic lesions in the spine, the bone marrow there’s not trabeculation, but in other areas of the spine the trabeculation is preserved. And there’s marrow infiltration. MRI currently does a better job at that and we’re accessing the new technology. Alright, good.

Well so if you were to summarize the primary purpose of this study then was to evaluate dual-energy CT for this purpose? Yes the diagnostic performance of dual-energy CT for the detection of bone marrow infiltration. So tell us how you designed this study and what methods you took to assess that purpose?

So we consecutively evaluated the patients that were referred to us for a standard routine staging for computed tomography. We used our dual-energy exam for them and then we tried to acquire them for the MRI. We took great care that there is no time between MRI and CT scan so that no anti myeloma treatment is delivered. And you choose patients with multiple myeloma just to be specific about the diagnosis or you had a lot of those patients coming to CT anyway? Is that the reason? So they got referred to us from the internal medicine department with the diagnosis or with the suspected diagnosis of multiple myeloma.

There were 28 follow-ups and I think 6 primary diagnosis. So the idea is to look at the essentially the performance or accuracy of dual-energy CT and your standard of reference was an MRI?

That was MRI right. Okay so everybody had both exams. What was the time difference between the two exams was it pretty close? In most patients we tried to do it on the same day. So most patients we managed to do this, but some patients they were not in-patients but they came back and in some cases I think the biggest data was around 60 days.

So in order to use dual-energy CT to detect bone marrow infiltration by myeloma what sort of techniques did you use with dual-energy CT? How did you do it? In order to – the contrast of computed tomography images result from differences in photon attenuation. Mainly based on the photoelectric effect. I think the attenuation of a given tissue depends on one hand on the material of which it consists, and on the other hand of the photon energies that you use to examine it with. So if you have a given photo energy for example created by a tube of 120 kV and use and you examine different materials than you get different attenuation values.

But if you have a constant specific material and use different photon energies then you also receive a set of different attenuation values. And this is characteristic for different materials. Okay so what energies did you use for the dual-energy CT scan or what energy levels did you use? We choose the 90 kV for the low kV tube and 150 kV with a tin filter for the high kV tube. Okay a tin filter, what’s the purpose of that tin filter? The tin filter hardens the high kV spectrum so it filters the low energy parts of the spectrum and that creates a better spectral separation between the low kV tube and the high kV tube.

So you have a more pure high energy versus the low energy because there’s still overlap between this? Yes, but it minimizes the overlap.

So you have two energies and those photons are gonna through different materials. They’re gonna go through fat in normal marrow primarily fat and then if you have tumor you’re looking for multiple myeloma those photons are going to be going through tumor.

And so what are you expecting if there’s two energies and perhaps you’re going to take the high energy versus the low energy, the photons are going through tumor tissue, you’re gonna have different Hounsfield unit measurements right? Okay and how different are those energies at let’s say the 90 and the 150? Are the Hounsfield units similar or do they start to get quite different for those? So what we didn’t measure in the 90 kV and the 150 kV data sets, but what we received were Hounsfield measurements that were already from the virtual noncalcium data set. So the Hounsfield units let’s say one type of tissue if it were all fat the 90 kV is a little higher Hounsfield units than the 150 kV. But I believe that at the 150 high energy there’s a larger difference for the tumor tissue right, the myeloma? Okay so there’s a broader differentiation between the 90 kV which represents its signature and a dual-energy scan.

So also the unique aspect of this paper and new innovation is three material separation right? Could you tell us a little about that because you had to separate calcium, fat which is benign tissue, and then tumor tissue which is higher density. So there is this three material decomposition algorithm that has been used before and it differentiates as you said calcium, fat and red marrow or soft tissue or tumorous tissue which are assigned specific attenuation numbers and then those materials can be quantified and consequently virtually removed. Okay, so as you’re going into it you realize then already I suppose the fat Hounsfield units probably relatively specific for fat?

Okay but what about the tumor? The tumor we know soft tissues are soft tissues, some overlap with other tissues perhaps you have to understand going in maybe other types of red marrow might overlap? Yes this was the tumor Hounsfield units were non-specific so it was not obviously a tumor, but it was elevated when you compare it to the normal background of healthy, fatty bone marrow that you would expect in those populations. So we have this understanding that fat, you’re gonna get a signal that’s pretty characteristic and then you’re gonna get another signal and you still have to make an interpretation of whether that signal you’re getting is actually tumor or maybe there’s different patterns that might be – you want to make sure it’s not hemopoietic marrow or edema perhaps or something like that. Yes, yes, yes. So those are the methods, tell us about the main results of the study.

How well did it work? It worked actually quite well.

We did a two-step analysis. The first part of the analysis was the visual analysis so we just looked at the patient based results, whether we could classify a patient as having bone marrow infiltration or not. And compared to standard CT in five cases we were able to detect bone marrow infiltration that was not visible on standard CT. This was one of the most important pieces. So it just helped you with the basic CT and that alone might be a win I think right?

So you did visual analysis, you found some patients you mentioned five patients that worked better than conventional CT and that was five of out of about how many patients? Five out of I think 23 patients that had infiltration. Yeah so that’s a good percentage. That may be helpful in those patients.

So in addition to looking at a per patient analysis you also measured a lot of regions of interest. So what was the purpose of that? We wanted to show that not only in visual analysis and on a per patient base, but also on a per lesion base and with a quantitative approach it’s possible to differentiate healthy marrow from infiltrated marrow.

So we measured, guided by MRI, we measured the attenuation numbers of lesions and of controlled regions. Okay so MRI is your reference and say this area is really normal by MRI what are the Hounsfield unit measurements are what are the signals that we’re getting and how do they compare? That also gives you a lot more data potentially some overlap but you get a lot more data points you get a little bit better statistics that way I suppose. Yep so then you have was it almost hundreds of data points with regional interest of A.K. We had 245 points of interest. Lots of areas normal versus abnormal and how did the quantitative results turn out compared to the visual results.

What were the quantitative results? The numbers were actually quite similar. So when it comes to diagnostic performance we had sensitivities and specificities a little bit above 90% so it was comparable. Okay so that sensitivity and specificity I noticed they were pretty consistent.

Sometimes some of our tests are very highly sensitive, but have a low specificity or vice versa. It’s kind of helpful that we’re not over calling or under calling in that sense. So that was pretty encouraging and almost kind of reassuring that the quantitative numbers corresponded to the visual assessment of the per patient analysis. So that was really quite nice. And any other results that came to mind? How did the images look? How did you like the image quality as a radiologist?

The images – so you don’t only get the 90 kV image and the 150 kV image but a third data set of 120 kV “like” images created and those look, in my opinion, comparable to what you’re used to. And to detect the tumor, the myeloma let’s say in the infiltrated marrow, you were using a virtual non-calcium image. I think we’re mostly familiar with virtual non-contrast iodine image, so how does that differ the virtual non-calcium? How do you do that or what is that? So basically it all comes back to the three material decomposition algorithm that is used to quantify in our case calcium and if you can quantify it and you can also virtually subtract it from the image information.

Okay so if I understand correctly you create the calcium map and you subtract it out of the other material leaving let’s say the fat and the water image primarily? The fat and the soft tissue or red marrow images. And then that was a color overlay then where you are looking at that and I looked at some of the images, the viewers can look at the images in the manuscript and you color coded the images. It seemed like there was a lot of variation in the color maps. What’s your assessment as a radiologist because it seemed like the variation would be hard to interpret. Do you have to look at a lot of those? Yes I think in my opinion it’s not – in some cases it is self-explanatory, but sometimes you really have to look at the dual-energy overlay at the CT image and really think whether it is an artifact or whether this can be real.

Cool Jazz Font Apk For Galaxy Y there. Okay so there’s still some interpretation involved but it’s potentially an interpretative tool to help you with that diagnosis of marrow abnormalities. Okay, got it. Okay so you’ve done the study, you have sensitivity and specificity and a smaller study right now, a preliminary study of a little over 30 patients, what would you say are the potential limitations with the technique? Where – you had a couple false positives and a false negative, what happens in those cases? Yes I think the false negatives those were patients that had very small lesions on the MRI imaging around 6 or 7 mm solitary lesions, and the first problem is to actually see them on the dual-energy CT images and the second step is to if you see them then you really have to think whether this is real, whether this can be a solitary tiny lesion or whether this is just an artifact. And I think at this first stage we were not confident enough to call it a solitary 5 mm lesion and just didn’t really count this patient as infiltrated.

Okay, got it. So this is how I would explain the false negatives. The false positives on the other hand I think they are mainly because the elevation in attenuation numbers is not specific to multiple myeloma. That can happen due to degenerative cases (inaudible).

So it’s not specific and I think this is how you can explain the false positives. Some patients showed artifacts and one patient even the artifacts looked like focal infiltrations so both me and the other reader we both classified the same patient as positively infiltrated and he was not. One or two other questions then just on your results and how this may work, you looked at the spine and the pelvis, does the technique work better in one area or another or is it quite similar in either area, any difference? It’s important that there is lots of background of healthy fatty marrow and when you go from cranial to caudal in the body you get more fatty bone marrow. So it’s physiological that in the lumbar spine there is more fatty marrow than in the thoracic spine and in the pelvis there is even more so it works best if there is lots of fatty background. So if a patient’s anemic perhaps and they have a lot of hemopoietic marrow may be a challenge to do that? Now the other thing that comes to mind, MRI for a diagnosis of myeloma is pretty straightforward.

There’s a lot of contrast-to-noise ratio, the abnormal lesions are really bright and inversion recovery sequence. So that’s clear and that probably works pretty well on CT as well. Just reading your technology and looking at the images, but sometimes myeloma has a more difficult pattern maybe a salt and pepper pattern?

How did you deal with that? This was one important limitation of our paper that we had only patients that were normal and patients with focal lesions and we completely missed patients with diffuse infiltration. For example this is really an important pattern that has to also be diagnosed. And would that pattern be amenable to further technological refinement of the technique or is that going to be a limitation of this method in general? We kept on examining those patients so now we have patients with this infiltration of combined diffuse and focal infiltration and it also works.

It’s possible to detect it. Good so it’s a learning curve and maybe getting the technology improvement as well is that right? I think it’s a learning curve for the examiner. So really you have to look at some of those pictures and really have to in some cases have the MRI to learn what it looks like. So having the MRI to guide you and interpret the images because the images were the first time, essentially almost the first time done and so it’s hard to know what is normal and abnormal the first time you do the study.

So just to conclude then, what would be the next steps for the technique? Are you continuing to use it? Are more patients examined? Where is it going? Yes we are continuing to use it. We want to examine different patterns of myelomatosis infiltration.

So for MRI the patterns are established and for CT it’s only possible to detect lytic lesions so we want to try to establish maybe a different CT patterns comparable to MRI patterns as well for dual-energy. And I think an important point to look into is how treatment related changes look in dual-energy computed tomography. So if we get one patient and primary diagnosis and then look at therapeutic monitoring with dual-energy this will be certainly important for the future. And with CT we always have that concern about radiation exposure. With the dual-energy technique how’s the radiation exposure going to compare to a standard CT acquisition?

So there are different papers out there actually. Some of them tell us that in some cases the radiation exposure is higher; some tell us that it’s lower.

For example we did pulmonary embolism with dual-energy CT and we were able to lower the radiation exposure. So this is an important point to look into and we’ll certainly do it. But comparing to our previous generation scanners, using dual-energy CT we’re still below what we used to have at the for example 64 (inaudible) scanner. Okay so that’s encouraging. So your experience now in radiation is under control.

Maybe we’ll get lower than earlier generations. Good to know. Okay, so any other final thoughts on the success of dual-energy? You did a study on trauma; you did a study on tumors, any other studies with bone marrow and bone with dual-energy that you’re thinking about? We are continuing to do the multiple myeloma patients to look into the patterns and the therapy and use changes and we’ll certainly keep looking.

Aleksander, thank you very much. Thank you very much. Very nice paper. Transcript: December 2017 Podcast Deborah Levine, MD Hi. I’m Debbie Levine. I’m the Senior Deputy Editor of Radiology and I’m here today doing a podcast for our December issue.

We’re talking to Dr. Emily Conant who is a professor of radiology at the hospital of the University of Pennsylvania about a study that her group published that’s got the title of “Comparison of Use of BI-RADS 3 Probably Benign Category after Recall from Screening before and after Implementation of Digital Breast Tomosynthesis.” So welcome Dr. Conant, MD Thank you.

So can you tell us a little bit about what you did and what you found? So we began with digital breast tomosynthesis with our screening patients in one day.

We went whole hog and converted so we’ve been watching our population screening patients for a while now and we published on this group before but we were particularly interested in the subgroup of women with category 3 which is sometimes a very difficult category. I think most radiologist don’t like category 3s because it adds to a patients anxiety and increases the amount of imaging that she obtains over time. So we had seen some reports of the category 3 usage going up with digital tomosynthesis so the analysis was in part to address that in sort of a natural experiment with our group totally converting to tomosynthesis. Did any of your results surprise you? Well not really.

I think we now, most people believe, that tomosynthesis is the better mammogram so you know we again showed that our recalls, our callbacks from screening, decreased and our cancer detection rate did go up and we found that there was really no difference in the use of the category 3 in the tomosynthesis population, but because there was an overall decrease in recall if you looked at it by the patient level, we actually ended up putting 2.4 per thousand women screened less in the category 3 category. Now that was not statistically significant compared to the 2D screening world, but when you think about the impact, the economic impact, you know what improvements that we can offer with tomosynthesis, I think that’s important. Certainly if you’re one of the women who doesn’t have to go through a category 3, that’s a good thing. Like you just mentioned, you had this number 2.4 per thousand, but when we look at the 95% confidence interval, so we’ve getting to the statistics here, it did cross zero which means that we don’t know if this would necessarily be true for another population. How confident can you be in your results? Well I think this is – you know we’ve continued to monitor our outcomes with tomosynthesis and we see similar results over time.

As a matter of fact, when we looked at our subgroups based on first round versus second round, again there’s a reduction as one would expect when a new technology is implemented. There’s a learning curve and as we look out in time there’s a reduction over time, a trend towards that. So I think that’s good news. Some of the earlier reports on implementation of tomo had shown a really large increase in the use of category 3s so we were very happy with this and we continue to see this. Well that’s interesting. And then another interesting finding was that the distribution of your recalled findings type differed.

So for example architectural distortions and masses went up but you had fewer recalls for asymmetries I’m wondering if you can comment on this and particularly use of architectural distortion in BI-RADS 3. So this has been shown people have looked at different recall buckets so to speak and others have shown various results, but it does kind of intuitively make sense that as we improve the conspicuity of different lesions types and sort of unmask them, we see some subgroups more. For example we unmask masses so we see more with tomosynthesis. Architectural distortion is always an issue for the radiologist.

It has a very high predictive value of being cancer so we’re certainly seeing more of it as we have the quasi 3D format of tomosynthesis, so it wasn’t surprising that it went up. And you know calcium really is a 2D sort of task so we saw no change in our recall for calcium. And we were very happy to see less technical recalls which others had not looked at yet. We were happy to see that. It’s horrible to call a woman back when we haven’t adequately examined her because of positioning or motion or something like that. So we’re happy to see that one decrease. Getting back to the architectural distortion, we did have more recalls for architectural distortions as others have shown and we did have some architectural distortions put in category 3.

However, in the tomosynthesis group mostly the ones that were put into category 3 at diagnostic imaging another lesion type was seen that was then put into category 3. So we do not advocate putting architectural distortion in general into a category 3. I don’t want people to think that’s appropriate. So frequently when we did do that it was because something else was seen over time you know as the diagnostic workup progressed. So like you mentioned earlier, your practice basically switched overnight to tomo and then you started collecting the results in this paper two weeks later.

Do you think if you had had a longer period of time for your imagers to adjust to digital breast tomosynthesis that your results might have differed? So that’s a very interesting question. We’ve actually looked at that.

We haven’t published that and there’s certainly a learning curve to tomosynthesis. It’s – you know we sort of read in a group so that when one person sees something that has been impressive on tomosynthesis we call others so that it’s sort of a group learning process. And I do think it’s important to look at data over time as more people are converting to tomosynthesis to see if the cancer detection rate persists, if the recall rate continues to go down and I think that data is evolving now but we certainly are looking at that in our group.

And so what advice would you give to a group, a breast imaging group, that has decided to move to tomosynthesis and so they’re starting fresh but they have other published papers that they could go back to. Is there any advice you could give a center that’s thinking about embarking on this transition? Well I’m excited for them that they’ll be using this because I think that it really does increase the confidence of the reader in evaluating, characterizing different patient types. I really believe that tomosynthesis has the largest impact on the screening population and so that starting with screening where you can see a wide variety of normal breasts and also problem solve with things like asymmetries which are certainly better work through with tomosynthesis than 2D imaging. I think that will get people acquainted to it. We’re now converting of course to synthetic imaging.

Many people ask about that conversion. Important to put the synthetic image in front of the 2D dose digital mammogram and have an overlap period as one implements synthetic 2D imaging in your tomosynthesis screening.

We did that for a four month period so our readers were comfortable and could toggle back and forth between synthetic and 2D. And I think really too whenever possible share successes and even things where you could have done better with your group so that everyone can learn together with this new, better mammogram. One slightly critical issue but I think it’s important to talk about is when you looked back in time and your historical cohort was the digital mammography BI-RADS 3 cohort, you ended up finding four cancers and that ended up being 2.4% BI-RADS 3 had cancer which is of course higher than the 2% that we expect by the ACR criteria and here we are in 2017 with performance metrics you know 7 years ago and your group obviously had to do a lot of work to come up with these results. Did you know at the time that your cancer rate was higher than recommended for use of BI-RADS 3?

So we do monitor that and we do have sections where we look at false negatives as a group and so we were certainly not happy with that slightly over the benchmark of 2% for BI-RADS category 3. The cases when we reviewed them though were really quite small, however that’s still significant for an individual woman when that happens.

I’m excited to say that it’s improved as you can see from our data from the tomosynthesis world. And I think this kind of publication and sharing of this kind of data is very, very important to prompt every practice to really go through all their metrics not just you know the simple ones like recall rate and even cancer detection. But really look, category 3s are a very, very important group because they cause such anxiety to patients. I think often they prompt over imaging, but we certainly don’t want to miss cancers clinically significant cancers.

So yes, we continue to monitor and try to learn from our cases like these. How should groups go about getting these cancer results? You link to a state database; is that the thing that you think is best? Yeah so I think the strength of our data is that we have been able to link to a state cancer databases because that’s more complete. For example if a woman comes to our site to be screened but may continue at another site for her diagnostic follow-up over time, we may lose her in our local network cancer registry.

So it’s great if you can get the state. It would be even better if we could get a tristate cancer registry. I think most audits are done based on the local institutional registry. So if possible state linkage should be done for a more complete picture of cancers and false negatives. And what’s your group working on now? What’s next for you?

Thank you for asking. We’re really interested in the outcomes over time. Now we’re entering our 7th year of screening our entire population. We’ve been very fortunate because of donations from grateful patients at our cancer hospital, our cancer center, to be able to offer this without additional charge to the patients. So really all of our population has been screened now going on 7 years and we want to look if we maintain these outcomes how we learn and improve hopefully over time, and really, really importantly the biology of the cancers that we’re finding with tomosynthesis as well as those that we’re not finding with tomosynthesis. Because in the end it really boils down to the biology of the cancer and that is what drives prognosis for women.

Intel Nh82801gb Motherboard Drivers For Windows 7 here. Hopefully you’ll hear some more exciting data coming out shortly. Well that sounds great. Well thank you so much for your time. I really appreciate the time and work that all of your group has put into these wonderful publications. Well thank you very much for your time.

Transcript: December 2017 Podcast Alexander A. Bankier Hello. My name is Alex Bankier.

I’m Deputy Editor of the journal Radiology responsible for thoracic imaging. For today’s podcast we welcome Dr. Symons and Dr. Pourmorteza from the NIH in Bethesda and we are discussing today their article the technical development of Feasibility of Dose-reduced Chest CT with Photon-counting Detectors: Initial Human Results. Symons let’s start with you. Could you give to our readers a brief explanation, description of this technical innovation and explain in what this (inaudible) from previous technologies.

Rolf Symons, MD Certainly. So photon-counting detectors use semiconductors to directly convert incoming x-ray photons into an electric signal and the pulse height of that electrical signal is proportional to the photon energy. High speed applications and specific circuits then integrate the signal, they count all the pulses and measure their energy and they can then divide the multiple x-ray photons into different bins based on their energy. This approach can then effectively eliminate electronic noise which is as we all know a very important contributor to noise in low dose studies such as dose reduced chest CT. All currently available commercially available CT scanners use energy integrating detector where the x-ray photon indirectly is converted into an electric signal which leads to a combination of multiple photons into one intensity value and a loss of this spectral information and a retention of electronic noise in the signal. Pourmorteza you show that this new technology has the ability to reduce dose to reduce noise are there other practical implications of this technical innovation?

Amir Pourmorteza, PhD First of all thanks for inviting us. It’s a pleasure to be here. And yes, so the way we when we think about photon-counting detectors as Dr. Symons just mentioned, one of the aspects is the rejection of electronic noise, but there are also other advantages. Specifically in this paper we focused on the rejection of electronic noise, but there’s also the spectral information or the energy information of photons that could be used specifically to separate or discriminate different materials and also another aspect of photon-counting detectors is very important there is no scintillating crystal so the steps where we go from detecting the x-ray into light photons into an electric signal. The light photons are removed. There’s no intermediate step so that adds two important things.

Number one is that because we don’t have that intermediate step, some photon statistic is now better preserved so not only electronic noise but the actual (inaudible) or quantum noise of the system is better preserved. There are lots of studies based on cascaded system modeling of photon-counting detectors, so that’s one aspect. So if we choice to do model-based reconstructions we will have a better behaving signal compared to energy-integrating detectors. And then number two is that because we don’t have these scintillating crystals, we can make the pixel sizes of the detector much, much smaller. The size of the detector pixels have been limited to about a millimeter and with photon-counting detectors we can reduce that. There will be some (inaudible) and other effects that are produced but we can reduce the size to a quarter or half of a milliliter. So this whole interface and interaction between resolution noise and so forth is of course of particular interest for thoracic images, for lung images, you have applied this new technology to preliminary nodules which are by definition high contrast structures, do you anticipate that the technique will provide equally good results with more diffused changes in the lung parenchyma that are (inaudible) of lower contrast?

So I’ll start with something and then I will let Dr. Symons answer the rest of it. But before this paper we actually did an extensive phantom study and the phantom study was focused on more diffuse type of nodules, ground-glass nodules, and actually the performance that we observed was better for photon-counting CT even better than what we saw in the clinical studies. Of course it was a phantom and you know a well-shaped phantom so we expected the results to be better, but that initial study made us confident that we could move forward to do these human studies with similar results, but Rolf would you like to add something to that? I completely agree in that phantom study we saw that also for low contrast structures like ground-glass there was a clear advantage of using photon-counting technology. Another advantage of the photon-counting technology is that we see that the Hounsfield stability is better preserved probably related to the lower electronic noise. So repetitive follow-up scans and measurements of Hounsfield units which is important to follow-up of certain diseases such as lung fibrosis are more reliable at low dose with photon-counting detector technology.

So I think that also in that regard for low contrast changes or discrete changes in Hounsfield units photon-counting technology has certain potential. Yeah I agree. I think this study issue of Hounsfield units attenuation measurements is of big importance. Notably with the rapidly evolving iterative reconstruction technologies that are currently used, you call your paper initial human results, obviously we are quite early in this development, what are the next steps that are needed to evaluate this technology and my second question would be what is the time course, what is the path in the future until this technology will become part of routine imaging. Now as we know this is a prototype system. The purpose of using the scanner is to answer this main question that do we even want to move from energy integrating detectors or the current technology to the next generation. It brings to my mind the time where we were going from analog cameras into digital cameras and small webcams, so the first challenge right now is a mass production of these photon-counting detectors.

These detectors have been around for a very long time. They were developed at CERN and the challenge that we have in CT diagnostic CT is that the photon count rate is so high so we had to wait for certain advances in electronics for better you know faster electrical circuits and now we’re at a step that we have very viable prototypes and very stable prototypes that can operate in room temperature and does not have a lot of the (inaudible) or problems of older detectors. I think in terms of is this a viable technology, that was the purpose of this paper and a couple of other papers I’ve been working on, and I do believe the answer to the initial question that at least with photon-counting detectors we’re not losin.

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