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X-ray Computed Tomography - Science method

Explore the latest questions and answers in X-ray Computed Tomography, and find X-ray Computed Tomography experts.
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Hello,
I'm currently looking to segment some CT data with the Dragonfly software. However, the reconstructed data I received is in the .vol file format, which I cannot import/open with all the segmentation programs I use. I am now looking for a program to slice up the .vol files into .TIFF files and start my segmentation from there. Does anyone know a way to convert .vol files like that? Or is anyone aware of freeware to segment .vol files?
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Most likely, the .vol file is just a binary array. For correctly interpreting it, you would need some meta-information, e.g. dimensions in x, y, z, the voxel-size, the data-type (e.g. 16bit-unsigned-int, 32bit-float, ...), and the "endianess", i.e. "little endian" or "big endian".
The CT reconstruction-software should provide a separate file containing that information, or even a wrapper for direct import.
In many applications you can import the .vol-file as "raw" image, where you have to specify the necessary meta-information.
For conversion from raw to TIFF you could, for instance, use Fiji/ImageJ.
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Consider the following questions when you construct your response:
• What type of radiation is typically exploited in most nuclear medicine procedures?
• How are patients prepared for nuclear medicine procedures?
• What are the advantages and limitations of nuclear medicine?
• What ailments are typically diagnosed and treated via nuclear medicine procedures?
• Evaluate a minimum of three applications of nuclear medicine relating to any of the following topics: Positron Emission Tomography (PET) scans Gallium scans Indium white blood cell scans Iobenguane scans (MIBG) Octreotide scans Hybrid scanning techniques employing X-ray computed tomography (CT) or magnetic resonance imaging (MRI)
• Nuclear medicine therapy using radiopharmaceuticals
Support
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Hello Dear,
I hope this link helps you, get your answers,
nuclear-medicine-for-diagnosis-and-treatment.pdf (iaea.org)
Best regards
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Call for Book Chapters:
Intelligent Diagnosis of Lung Cancer and Respiratory Diseases
Editors:
Wellington Pinheiro dos Santos, Federal University of Pernambuco, Brazil
Juliana Carneiro Gomes, Polytechnique School of The University of Pernambuco, Brazil
Maíra Araújo de Santana, Polytechnique School of The University of Pernambuco, Brazil
Valter Augusto de Freitas Barbosa, Federal University of Pernambuco, Brazil
Introduction
The series of books Intelligent Systems in Radiology aims to present the principles and advances of diagnostic techniques in Radiology based on Artificial Intelligence, from the perspective of the advent of Digital Health. The series consists of three books. Each of them is divided into two parts: one dedicated to theoretical foundations and the other to radiological applications in the real world. This call for chapters is dedicated to the first volume.
The first book, Intelligent Diagnosis of Lung Cancer and Respiratory Diseases, is dedicated to the diagnosis of diseases of the respiratory tract or those that seriously affect the respiratory system. In the first part, the physiological foundations of the respiratory system and the formation of radiographic images and x-ray computed tomography are presented. Principles of respiratory diseases are also presented, including lung cancer, viral and bacterial pneumonia, tuberculosis, and Covid-19. In addition, the principles of pattern recognition and machine learning and the main theoretical and practical tools are also briefly presented, and libraries in the programming languages ​​Python, Java and Matlab are also commented. The second part presents innovative works and systematic reviews of intelligent applications in the diagnosis of lung cancer, tuberculosis, viral and bacterial pneumonias, and Covid-19.
No publication fee will be demanded from the authors of the accepted chapters.
The Objective of the Book
This book series is intended for readers interested in intelligent systems to support diagnosis in Radiology. The series is composed by three books. The first one, Intelligent Diagnosis of Lung Cancer and Respiratory Diseases, the focus of this call, is dedicated to diagnosis of respiratory diseases. The second book covers the diagnosis and treatment of neurodegenerative diseases. The last book is dedicated to Neuroscience applications, from clinical to affective computing applications. All books present comprehensible theoretical fundamentals both from clinical and computer engineering perspectives.
Target Audience
This book is intended to everyone who needs to understand how radiological images, neuroscience and artificial intelligence could work together to generate solutions in the context of intelligent diagnosis support and applied neuroscience and how intelligent systems could process and analyze images to improve early diagnosis and, consequently, prognosis of diseases.
Recommended Topics
Contributors may submit proposals on topics that include, but are not limited to, those listed below. The chapters may take various forms.
Part I: Fundamentals
1. Physiology of the respiratory system
2. Fundamentals of x-ray images and computerized tomography
3. Principles of lung cancer and respiratory diseases
4. Principles of pattern recognition and machine learning
5. Principles of image processing
6. Computer-aided image diagnosis
7. Computational tools and tutorials on Python, Java and Matlab
Part II: Applications
1. Lung cancer
2. Tuberculosis
3. Viral and bacterial pneumonias
4. Covid-19
5. Emergent imaging techniques
Submission process
Potential contributors are invited to submit, on or before January 31, 2021, an abstract of 300 – 400 words proposal (excluding references) that presents the intended contributions of their chapter, intended approach and methodology.
In addition, authors should provide the following:
· Proposed titles of their chapters
· The theme (see above) of their intended chapters
· Full names
· E-mail addresses and
· Affiliations
Chapters submitted must not have been published, accepted for publication, or under consideration for publication anywhere else.
Proposals and full chapters should be submitted via EasyChair according to the following link:
By February 15, 2021, potential authors will be notified about the status of their proposed chapters. When accepted, the authors will receive further information regarding the submission process, including the formatting guidelines.
Full chapters should be submitted on or before April 16, 2021 in a single attached Word or LaTeX file with the Copyright Letter. References should follow IEEE standards. The authors should follow the formatting rules in this link:
Final submissions should be approximately 4,000-5,000 words in length, excluding references, figures, tables, and appendices. All chapters will be peer-reviewed. No fees will be demanded from the authors at any stage.
Full chapters are expected to be at least 25 pages in length, font size of 10pt for the abstract, 12pt for the body text, and single-spaced paragraphs.
Key deadlines
• January 31, 2021 - Proposal submission deadline (300-400 words)
• February 15, 2021 - Notification of acceptance of proposal
• April 16, 2021 - First draft of full chapter submission
• April 30, 2021 - Revision submission
• May 14, 2021 - Final acceptance notification
• December 2021 - Publication
Publisher
The book will be published by Bentham Science Publisher until December 2021.
Please address any questions you may have to Prof. Wellington Pinheiro dos Santos - wellington.santos@ufpe.br.
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Dear Wellington Pinheiro Dos Santos, I am now dealing with a book chapter dealing polymeric nanoparticles (PN) for solid cancer drug delivery. During my bibliographic processing I found appreciable bibliographic material on the same PN used in cancer diagnosis for image contrast mainly. However, in the recommended parts of your Book no one is concerned by this aspect. So, what do you think, if you think this is feasable I Can be in charge of that. Kindest Regards
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Hello,,
Dose anyone know the internal pressure of x-ray vacuum insert used in CT scan machines?
Thank you
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Dear Dr. Pierluigi Traverso
Thank you very much for your attention and your time.
Best regards,
A A F
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I need to find the cracks in the samples, which would be easier with higher contrast between crack and plastic. could you please suggest different methods to increase the contrast in X-Ray computed tomography?
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when dealing with low atomic number material you have to go to low kVp as possible. Attenuation coefficient goes with 1/E³.
E = x-ray photon energy
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I have an unknown sample to characterize using different techniques (mainly pore size distribution):
-X-ray computed tomography
-Mercury intrusion porosimetry
-Gas porosimetry
-BET tests for the surface area.
how would you proceed to make a perfect analysis (taking into consideration the time/money/precision factor)?
Any explanation, recommendation and/or articles for comparative studies would be a perfect help. Thank you in advance.
Moustafa
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For unknown materials, a BET surface area analysis (by gas adsorption) is a good place to start... this at least will reveal how much gas accessible surface there is and one can make certain inferences about the extent of meso and microporosity by combing the BET calculation with the t-plot method. A complete description of (gas accessible) micropore space requires low relative pressure gas adsorption isotherms and appropriate mathematical models (DFT, not BJH for micropores). Remember that gas molecular/atomic size plays a role in accessing the smallest of micropores; CO2 at 273K can access smaller pores than can N2 at 77K for example. Mesopore and small macropore size distribution can be done by gas adsorption up to a few hundred nm pore diameter. Gas adsorption isotherm hysteresis can reveal something about the connectivity of pores. Larger macropores are quickly evaluated using mercury intrusion porosimetry... up to ~1mm in diameter. The value of these fluid-based methods (gas adsorption and mercury intrusion) is that pore volume can be known directly, unlike diffraction and microscopy techniques. They still have a role to play of course to understand the underlying solid structure that is giving rise to the porosity. CAT scans can be useful in the meso/macro scale but cannot easily give quantitative values and of course rely on good X-ray contrast. Small angle scattering techniques really come into their own for amorphous and soft materials.
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I am looking for a contrast agent specific of plant cell-wall's lignin suited for Scaning Electron Microscopy or X-Ray computed tomography.
Many thanks !
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A traditional stain with affinity for wood lignins in EM is KMnO4, potassium permanganate. This has been used in EM since the 1930s. Attached is a link covering some of the available stains for wood in EM.
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Can anyone help me on how to proceed with preserved structure soil sampling for X-ray computed tomography analysis when working with soil wetting and drying cycles and addition of hydrochloride polymer (hydrogel)?
Would it be feasible to place sampler rings inside a vessel and apply the cycles?
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If your vessel is made of low atomic number elements such as polymers you can do that. Such a material is transparent enough so that the x-rays of your CT machine can penetrate the vessel walls.
You know that CT is a three dimensional imaging technique. So you are able to 'look' inside the vessel and informations ( e.g. Hounsfiled values) from the voxels inside the vessel are indenpendent from the material properties of the vessel walls.
This also holds for the sample rings. If they are sufficient transparent for the x-rays you can preceed with your experiments.
You can see unsufficient transparency of your components when unexpected streak artifacts show up; they are due to beam hardening caused by the sample rings.
But here you can play around with further filtration of your x-rays and changing (e.g. increasing) the tube voltage.
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Hi,
I wrote a program in Python which simulates a CT scan taking into account the Poisson distribution of recorded photons. Taking 128 projections over 180 degrees results in the following filtered backprojection image.
What could be the reason for these vertical lines? I tried removing the "duplicate projections" for 0 and 180 deg, and checked that the phantom does not attenuate too much at the edges.
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The spikes appear to be an alignment issue in the reconstruction. This can be visualized as a shift in the image +/- Pi rotation. The 0-180 degre projections should align if they don't a cusp artifact will form in the direction of the mis-alignment. In this case you may want to check the counting math in your projections so when you select a 0-degree projection and an 180-degree projection they would be maximally aligned.
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I like to have a good number of CT images for the research work. I this regard, i like to know the different possible option available for the collection of CT images. Thank you.
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Dear Jagalingam P,
please have a look at my recent answer:
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Interested in any available unclassified XCT data scans from the industrial or military research communities compatible for use with Volume Graphics StudioMax 3.2 software.
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Dear Joe,
you may have a look at:
Here four data sets of CT images of luggage items are available on request.
They may meet to your expectations.
Good luck
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Dear All, I want to know standard oprating procedure for reconstruction of a material by using X-Ray Computed Tomography(xradia)microscope for to do exact analysis.
Please help me?
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Xradia system already have 3D Viewer software module in its control PC to visualize volume-rendered 3D image of datasets.
If you want segmentation of 3D image to extract quantitative info (such as porosity, volume, etc), then a wide plethora of software's are available (few are free while others are commercial) such as ImageJ, Dragonfly Pro, GeoDict, Drishti, Volume Graphics, Avizo Fire, Simpleware, etc.
Hope this helps.
- Arun
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Does anyone work on the X-ray tomography of particulate suspensions? I am looking for some work and literature on the same. Kindly share your work related experience or some good papers related to the subject.
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Please send your proposal on ashishka@rrcat.gov.in.
We are doing X-ray micro-tomography using synchrotron radiation at Indore India
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GGO can be seen in lungs cancer by CT. I want to know how to take biopsy of that GGO? and can we analyse that GGO or not?
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In our experience, a large number of persistent ground-glass opacities (GGOs) on follow-up images performed after 6-8 weeks may represent a malignant lesion [mostly bronchoalveolar carcinoma (BAC) and lymphoma]. We perform CT-guided core needle biopsy of ground-glass nodules larger than 1 cm in diameter safely utilizing a 20-gauge Tru-Cut needle. The complications are uncommon and usually mild with a small amount of self-limiting pneumothorax being the most common. In addition, we need to repeat the biopsy only on few occasions.
It is important to take the sample on your first attempt since the possible localized hemorrhage at the site of biopsy needle may obscure margin of the small nodules.
Interestingly, we have noticed that majority of GGOs, which are detectable on both lung and mediastinal window/level settings are malignant in nature; however, the vice versa is not true.
You may also need to read more about the accuracy of CT-guided core biopsy of GGOs by Kim et al. on “American Journal of Radiology” (the link is attached).
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This are CT images of an Aluminum 7010 strained sample having some cracks.I have attached 5 out of 800 images taken during full scan.
There is a separate image attached for scan parameters.
Sample size : 1x1x10 mm
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I can do it. message me anton2@sun.ac.za
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We are conducting pioneer X-ray and compute tomography studies on chosen species of elasmobranches. Research so far included rough shark, Oxynotus centrina (L.) (Squaliformes: Oxynotidae), Dasyatis pastinaca (L.) (Myliobatiformes: Dasyatiidae), Torpero marmorata Risso, 1810 (Torpediniformes: Torpedinidaae) and Raja miraletus L. (Rajiformes: Rajidae). Any help in analyzing the scans is much appreciated.
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than you very much for attached link, every help is more than useful at this point !
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What is the best method to assess the absorbed dose by the target organs during an X-ray computed tomography?
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Dear Slimane,
During an X-ray computed tomography exposure, the most common parameter
used to estimate and minimize patient dose is the CT dose index or CTDIw (Computerised Tomography Dose  Index). 
CTDI is measured with a pencil ionization chamber ( 10cm) and  2 cylindrical phantoms (  the measurements are only an approximation of the patient dose). 
These phantoms refer, respectively, the head examinations (with a diameter of 16 cm) and body examinations  (with a diameter of 32 cm ).
After measuring CTDIw = CTDIw = (1/3)CTDI100,center + (2/3)CTDI100,peripheral
>>>>>>> CTDIvol = CTDIw/Pitch
One approach (actually an approximation):
E= DLP * k
where :
* E = Effective Dose in mSv
* DLP is Dose Length Product given by: CTDIvol* length of scan [in mGy*cm] and 
* k= .0023 for head exams , k =0.015 for abdomen.
we can have easily average absorbed dose to tissue if we have: 
  • wT= tissue weighting factor (next page)
  • wR= radiation weighting coefficient
* CTDI underestimates dose from contiguous scans (helical) by not capturing scatter tails.
* CTDI overestimates dose from axial scan with no table motion because scatter tails included.
Best regards.
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In X-ray computed tomography, the defects come with probability value attached with them. Is there any particular cutoff value of probability to differentiate between real defects and artefacts?  
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Do you have access to such CTs?
If yes, please try (or better let try) and see what happens or what further questions/settings are asked. 
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There is need to optimize the resin flow and hence glass fiber orientation in a sheet molding process. The glass fiber length is between 1" and 2" and the average filament diameter is around 12 microns. Who has the ability to map the glass fiber orientation in 2D or 3D space?
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Interesting project. What is your e-mail ?
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Hello everyone,
I routinely do tomography on 90nm resin sections (HM20 lowicryl) of embedded Arabidopsis root tips collected on forever filmed slot grids.
With one block in particular I am having the following issue : 
When I acquire images, the image seems distorted in one direction. Astigmatism comes to mind at first, but their is none. It's only when I decrease the integration time of the camera (4k x 4k eagle bottom mounted camera) that the image seems to go back to normal. Unfortunately the electron dose is also decreased thus embedding on the quality of the image (which I can compensate by focusing the beam more).
My second suspicion was that the sample was drifting but it appears not to be the case.
Have any of you already experienced something similar ? I am thinking of maybe excess charges accumulating on my grid. If you do, how have you fixed it, other than switching blocks (only have one in this condition and the preservation is sadly very good :-( )
Thanks,
Regards
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Hi William,
have you had this problem only with one particular block. If you cut different sections of the same block do you get the same? 
I suppose that you have tried to redo the reference image on your camera. Perhaps it needs to be repeated with more counts?
Are your sections on slot-grids? 
If it is charging perhaps you can try to cover the sections with an additional carbon layer.
Does increasing settling time (illumination time) prior acquisition helps?
It would be helpful to see the distorted image.
Best wishes
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I have 2 CT images with different resolution. I would like to spatially align them.  
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I would suggest the 3D slicer and Analyze by Mayo Clinic. Analyze is the best in my opinion. You can download a trial version.
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I want to smooth X-ray tomography data to reduce noise but preserve edges as much as possible. I know of proprietary software that does this, but I need freeware. My volumes are quite large (sometimes as large as 1000x1000x1000 voxels^3) so parallelized or GPU-enabled software is preferred. 
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If your xray tomography data can be converted to an electron tomography format (like .mrc) then you can do anisotropic diffusion with the software package IMOD (http://bio3d.colorado.edu/imod/) with the 'nad_eed_3d' command or by using the 'etomo' gui which will then launch 'nad_eed_3d'. Easy software to install, parallelized.
Regards, Greg
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Improvements in x-Ray computed tomography capabilities have occurred over the past decade making this non-invasive damage characterization technology more efficient. Now, how extensive is this modality being used in current research for ballistic impact damage characterization?
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I cannot speak for the laboratory-based systems, looking in the literature I collected in the past years I have found only one article: DOI: 10.1016/j.ijimpeng.2015.05.012 . At the synchrotron light sources I cannot remember having seen such a request, indeed / of course I also do not know all activities. More common is that people start to exploit MHz-framerate radioscopy at synchrotron light sources to study impact dynamics "in situ".
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We are sintering samples and recording 3D tomographs in situ. One of the processing steps we'd like to try is to measure how the curvature evolves inside the closing pore spaces. Any ideas or expertise?
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Thank you all for such thorough feedback! Much appreciated and very helpful. 
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I am working on meshing Aorta STL file by CT scan i don't know how to extract sharp features to fit blocking into geometry any help/recommendations?
ICEM Meshing , Aortic Arch biomedical 
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Hi, I suggest the free software Lsdyna LS-PrePost at http://www.lstc.com/products/ls-prepost.
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Do you know any protocols? This is especially for dental biofilm.
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We tried it once without a contrast agent. We couldn't see anything; so definitely try it with a contrasting agent.
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Using the method of Laplace Inverse Transform, proposed by B.R. Archer et al. (1982-1988), we can get an x-ray spectrum from the Transmission Data where we need to use the values of mass attenuation coefficients. Not only this method but other methods also involve the use of the mass attenuation coefficients for the beam attenuating materials and these values are very sensitive to get an accurate x-ray spectrum back. If we use the mass attenuation values from NIST (that includes scattering, photoelectric absorption and pair production), can we also use the same values in the backward calculation? By backward calculation I mean the calculation of the Transmission Data using the X-ray spectrum in the following way:
T1(x1)=F1(E1)*exp(-mu1(E1)*x1)+F2(E2)*exp(-mu2(E2)*x1)+...+Fn(En)*exp(-mun(En)*x1)
T2(x2)=F1(E1)*exp(-mu1(E1)*x2)+F2(E2)*exp(-mu2(E2)*x2)+...+Fn(En)*exp(-mun(En)*x2)
....
Tm(xm)=F1(E1)*exp(-mu1(E1)*xm)+F2(E2)*exp(-mu2(E2)*xm)+...+Fn(En)*exp(-mun(En)*xm)
Or any other factors related to the geometry of the x-ray machine (that contribute the secondary x-rays passing through the attenuators and/or detectors) also need to be considered?
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You can try using FFAST tabulated data (http://www.nist.gov/pml/data/ffast/) of x-ray mass attenuation coefficients, which is the most accurate tabulation (I found) compared with experiment. Experimental geometry is an issue, where x-rays are attenuated by background matters (in between the monitor and the detector), detector gas and detector windows, that can be modelled to remove their effects; the efficiency of a detector can be estimated using attenuation data measured using that particular detector. Of course, theoretical data of x-ray mass attenuation coefficients do not provide information about the oscillatory part (XAFS) around an absorption edge; it just provides the background comparable for regions away from the structural parts! 
 
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In adults  with CT scan evidence of  liver injury (due to BAT) ,is there a corresponding alteration in hepatic transaminases and how long do they remain altered.
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I am not sure about the relationship between the severity of the liver damage and the level of the increase of the transaminases.Is there any evidence of that?
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I am working with X-ray CT Skyscan 1172 and its accompanied software CT-An. Are you experienced with this device? Can we share some analysing techniques, especially in diffusion mapping and porosity analysis.
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Additional porosity measurements can be made by using ImageJ. Christopher Liner has a nice little video showing this sort of measurement in rock section at http://seismosblog.blogspot.com/2013/07/thin-section-porosity-estimation-using.html . Also the bonej plugin (http://bonej.org) has a nice particle analysis section that can be used to track porosity in 3D.
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I have recently performed X-Ray computed tomography on a (single) melt inclusion within an olivine phenocryst from lava from Mount Etna volcano. I wish to prepare a methods/proof of concept paper, in which I will show the resolutions obtainable (specifically in determining the volume of the melt inclusion) using three difference XRT set-ups, and outline the potential applications to chemical volcanology.
I would be grateful if anyone could recommend a publication route, or refer me to any similar studies already available.
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Hi, Robin! I hope this artical will be interesting for you:
Smith, E.M., Kopylova, M.G., Dubrovinsky, L., Navon, O., Ryder, J., Tomlinson, E.L., 2011. Transmission X-ray diffraction as a new tool for diamond fluid inclusion studies. Mineralogical Magazine 75, 2657-2675. http://minmag.geoscienceworld.org/content/75/5/2657.abstract
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Improve the resolution by destructive or non-destructive means in 3D computed tomography.
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In general, dried insects with well defined chitinous exoskeletons will have greater contrast for the skeletal structures. This is due to the low absorptivity of air relative to the skeleton or tissue. The problem with dried specimens is the distortion of normal anatomy. X-ray CT have little native soft tissue contrast and discerning soft tissue structures without any contrast producing additives is difficult. Low energy scans can give better internal structure contrast (20 - 40 kV) but at the cost of very long scan times. Remember that kV sets the contrast level for the amount of tissue and the integral of the I, current, and Exposure,s, sets the image SNR level.
If possible is can be helpful to introduce an external contrast agent to produce additional absorption in image structures of interest. We have used a variety of materials to do this. This can be fed to live insects in the form of lead sugar, Lead(II) acetate. Which concentrates in metabolically active tissues. Post-morteum labeling agents like phosphotungstic acid will label fibrin, collagen and connective tissues. Other agents such as osmium tetroxide will stain lipids.
The use of agents allows a more complete approach to discerning soft tissue structures in specimens such as insects.
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imgRec is software developed for processing tomography scan data (cleaning it up).
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Here is another attempt at sending a manual.