Science method

Computed Tomography - Science method

Researchers that are involved in anykind of Computer Tomography Research.
Questions related to Computed Tomography
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As it is known, the potential tube voltage of the CT scanner is equivalent to the maximum energy of the produced photons, but it is not equivalent to the effective energy of the photons beam. Theoretically, effective energy = 1/2 to 1/3 of the maximum energy (or kVp). Furthermore, it varies from one CT modality to another and differs when exposure parameters are changed.
So, how could the effective energy be determined practically and precisely?
Thanks
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Thanks, William David Evans, for sharing your worthwhile paper, I appreciate that.
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Hi! Normally we process our PCR results for fold change in Excel by calculating delta CT, delta delta CT and finally 2^-delta delta CT. My question is, can it be done in Graphpad Prism in a simple way. If yes then how can it be done?
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Yes it can be done in Graph Pad
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So I was running an RT-qPCR, but as you can see from the attachment some of my technical replicate triplets have CT values that vary by 1 (e.g. 26.16, 25.88, 25.14), leading to standard deviation greater than 0.5.
What to do then? Can I still keep the good results and re-run the problem ones on a new plate with the same housekeeping gene? I have made aliquots of the same samples and primers that I can use.
Also, any advice on how to prevent CT value variation in replicates. This is probably a pipetting error, right? As an inexperienced undergrad I spent 3 hours making this plate ;(
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Well, you're probably going to get better data if you repeat your plate.
One way to reduce the well-to-well variation of replicates is to make master mixes. In brief, you make up one tube that you then split between your three wells.
If you really want to be super precise (and like doing math), you make up a mix that has all the common ingredients for all your samples, split that into mixes for each template, then make the mix for each primer set. Be sure to include about 20% excess volume for each mix so you don't run short on the last sample.
Avoiding bubbles does help too. If you have a plate holder for a centrifuge you can give it a brief spin down before running it.
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How can machine and deep learning models be integrated with medical imaging technologies, such as MRI, CT, and PET scans, to improve brain tumor detection and classification?
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Yes, deep learning models particularly Convolutional Neural Network( CNNs) have demonstrated superior performance in many medical image analysis tasks such as detecting brain tumor with different imaging modalities such as MRI, CT scan , PET scan etc.
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I am looking for large data sets of medical imaging, mostly related to neurology. Specifically, a wide array of CT, MRI, diffusion, CTA, PE, and the like. I was wondering if anyone can share some companies they may be aware of that sell such datasets?
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  1. The Cancer Imaging Archive (TCIA): TCIA is a well-known and freely accessible resource that provides a large collection of cancer-related medical images, including CT, MRI, and PET scans.
  2. Radiological Society of North America (RSNA): RSNA offers image datasets, including CT and MRI scans, for research and educational purposes. Some datasets may be available for purchase or access.
  3. Open Access Medical Imaging Repository (OAMIR): OAMIR is a collection of openly accessible medical image datasets that researchers and students can use for various purposes.
  4. Kaggle: Kaggle is a platform that hosts data science competitions, and occasionally, medical image datasets are part of these competitions. You can explore Kaggle's datasets section to find medical imaging datasets.
  5. PhysioNet: While primarily focused on physiological data, PhysioNet also hosts some medical image datasets for research purposes.
  6. DICOM Library: DICOM Library provides a selection of freely available DICOM (Digital Imaging and Communications in Medicine) images for educational purposes.
  7. Medical Image Data Open Resources (MIDORI): MIDORI is an initiative that curates openly accessible medical image datasets.
  8. National Institutes of Health (NIH): NIH and various other research institutions might occasionally release medical image datasets for specific studies or research projects.
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I have some difficulties in finding a good number of DICOM files for SSM...
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Here are some websites where you can find free DICOM images:
  1. The Cancer Imaging Archive (TCIA):Website: https://www.cancerimagingarchive.net/ Description: TCIA provides a large collection of medical images, including DICOM images, related to cancer research. It includes CT, MRI, and other imaging modalities.
  2. Radiological Society of North America (RSNA) Clinical Trials Data:Website: https://www.rsna.org/en/research/clinical-trials-data Description: RSNA provides public access to anonymized imaging data from clinical trials, including DICOM images from CT, MRI, and other modalities.
  3. Open Access Biomedical Imaging Resource (OABR):Website: https://openabm.org/ Description: OABR is an open-access platform that provides biomedical imaging data, including DICOM images from various imaging modalities.
  4. The National Cancer Institute (NCI) Imaging Data Commons:Website: https://portal.gdc.cancer.gov/ Description: NCI's Imaging Data Commons contains a collection of cancer imaging data, including DICOM images from various imaging studies.
  5. MosMedData: Moscow Chest CT Dataset:Website: https://mosmed.ai/en/datasets/covid19 Description: MosMedData provides a large-scale chest CT dataset with over 7,000 anonymized CT scans of patients with pulmonary diseases.
  6. COVID-19 Image Data Collection:Website: https://github.com/ieee8023/covid-chestxray-dataset Description: This dataset contains chest X-ray and CT images of COVID-19 patients, as well as images from other respiratory conditions and healthy individuals.
  7. ImageCLEF - Multimedia Retrieval in CLEF:Website: https://www.imageclef.org/ Description: ImageCLEF offers medical imaging datasets for research, including CT and MRI data.
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Dear CT experts,
i need to estimate the expected equivalent dose to the eye lens for a CT head scan.
The scanner provides me with the CTDI and DLP values, but I couldn't find so far a comprehensive description with approximated conversion formulas to estimate the eye dose. Any help is appreciated.
Thanks in advance, Christoph
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Thank you a lot for this hint. I will definitely try it!
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5-min? 10-min? more?
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Yueh Z Lee yes, for tumor M staging
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I use the 3C_qPCR technique to study the physical interaction between two gene loci. for this I have designated the specific primers for these two regions; first of all, the standard curve must be drawn up using different concentrations diluted to 1/10 on the scale of 1 fmol/ul of DNA synthesized in vitro. For DNA concentrations of 1 fmol and up to 10-4 fmol, I have good CT proportional to the dilution and therefore an E close to 100% whereas from concentrations of 10-5, the CT are no longer in the curve while still DNA is detected which causes me problems at the E level.
I have to go down to 10-5 and 10-6 fmol because my 3C libraries that I want to locate their CT in the curve to determine the SQ give CT higher than those obtained for the range 1 up to 10- 4
my question: is it the problem of primers and how to solve the problem?
and can I determine the SQ of my 3C libraries even if their CT is outside the standard curve?
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The term "primers" typically refers to short nucleotide sequences used in molecular biology to initiate DNA amplification through techniques like PCR (polymerase chain reaction). If you're experiencing problems related to primers, it could indicate issues with primer design, quality, or experimental conditions. Here are some common problems related to primers and potential solutions:
  1. Primer design: Ensure that your primers are designed specifically for your target sequence, avoiding any potential cross-reactivity with unintended templates. Tools like Primer3 can assist in primer design.
  2. Primer quality: Ensure that your primers are of high quality and free from contaminants. If possible, consider ordering primers from reputable suppliers or purifying them before use.
  3. Primer concentration: Optimize the primer concentration in your reaction. Too little or too much primer can affect amplification efficiency. Titrate the primer concentration to find the optimal amount.
  4. Annealing temperature: Ensure that the annealing temperature used in your PCR reaction is appropriate for your primers. Adjust the temperature if necessary to promote specific and efficient primer binding.
  5. Primer degradation: Take precautions to prevent primer degradation by storing them properly (e.g., at -20°C) and avoid repeated freeze-thaw cycles.
Regarding your second question, if the cycle threshold (CT) values of your 3C libraries fall outside the range of your standard curve, it may be challenging to accurately determine the absolute starting quantity (SQ) of your target. Standard curves provide a reference for estimating the initial quantity of your target based on its CT value. If the CT values are beyond the range of your standard curve, it's difficult to make accurate quantifications using that particular curve.
However, you may still be able to make relative comparisons or qualitative assessments using the CT values. For example, you can compare the CT values of different samples to determine the relative abundance of your target sequence. Keep in mind that these comparisons are not absolute quantifications but can still provide valuable information about the relative differences between samples.
To accurately determine the SQ of your 3C libraries outside the standard curve, you may need to consider alternative methods such as digital PCR or qPCR with a new standard curve that covers the expected CT range. Alternatively, you could perform a dilution series of a known sample within the expected range and generate a new standard curve to quantify your samples accurately.
It's recommended to consult with experts in your specific field or molecular biology techniques for further guidance on addressing primer-related issues and quantification challenges.
[1]Rameshwar Gupta*,
Research Scholar,
Department of Lifelong Learning & Extension
CSJM University, Kanpur, U.P. India
Whatsapp/Mobile: +918630831266
[1]Ph.D. Student, Department of Lifelong Learning & Extension, CSJM University, Kanpur, U.P., India, Email id: rameshwargupta775@gmail.com, Mobile: 8630831266, https://orcid.org/0000-0003-3761-3591
*Single author
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I tried doing a qpcr analysis after the whole process I got no Ct value everything, washing negative on the analysis table.
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There could be various reasons for not having a CT value after a qPCR analysis. Here are some possibilities:
No amplification: The qPCR reaction may not have amplified any target DNA/RNA, in which case a CT value would not be produced.
Insufficient starting material: The amount or quality of the starting material (DNA/RNA) may not have been sufficient to produce an amplification signal.
Inhibition: The qPCR reaction may have been inhibited by substances in the sample or from the reaction components, preventing amplification.
Technical issues: There could be issues with the instrument or reagents used for the qPCR, such as incorrect temperature or timing settings or contaminated reagents.
Poor primer/probe design: The primers/probe used for the qPCR reaction may not be specific enough or may not work well with the sample being tested, causing amplification failure.
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Hi All,
I have the results of both qPCR CT values and RNA-seq TPM values. Now that I have 2 sets of data, is it proper to compare expression fold change (2^ of delta delta CT) with log2 of TPM values?
Thanks in advance,
Selim Rozyyev
#qPCR, #RNA-sequence analysis, #TPM.
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qPCR is considered the more sensitive and direct analysis. You use the RNA-seq to get a list of "interesting candidate genes" to then investigate using qPCR. You don't need to do any direct comparison to the original RNAseq data.
You can't know that the high TPM from RNA seq will also have high expression during qPCR. That's why you do the experiment.
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I have been doing some qPCRs on bacterial genomic DNA. I am using 16S as my "housekeeping gene". My sample of interest, however, yield a lower CT value.
I am trying to figure out how to overcome this issue.
Should I redesign 16S primers?
Is there any other "housekeeping gene" for bacteria?
Should I send my samples to genome sequencing?
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There is no issue. Your bacteria contain many molecules of 16S rRNA, what results in low Ct values. That's all. This is not a problem.
There may only be a problem with this when your treatment leads to considerable changes in the amount of protein biosynthesis for which the amount of ribosomes and hence 16S rRNA may be regulated. A regulated housekeeping gene is not valid reference gene. The expression level (-> Ct value) is irrelevant - as long as it is constant across the treatments/samples you like to compare. Note that any gene being constantly exprseed under all the conditions/samples you like to compare would be a valid reference gene (even if it might be not expressed under some other circumstances - so that it would not be a housekeeping gene).
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I am looking for CT datasets for any type of artifacts (Beam hardening, Scatter, Metal, Ring,...) for ( Brain- Head & Neck,...)
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Hello,
I have extracted RNA from the Intestinal Enteroid Cell Line and have measured the expression of multiple genes using RT-qPCR. I tested two housekeeping genes GAPDH and ACTBR both showed good CT values: 18 and 19 *mean values. However, I recently measured their expression using the same primers and dilution factors done in the previous experiment. It was conducted on FH74 cells (Premature infant intestinal cells) However, this time CT values for GAPDH were ~30 and ACTBR were ~28. So it appears that there is expression but not as great as the enteroids. Is it possible that the difference in cell lines would generate that much of a difference for housekeeping genes?
P.S. RNA yield and cDNA yield were higher for the FH74 cells
I plan on rerunning the experiment with higher concentration of primers. To try and trouble shoot the issue. I was hoping to hear your thoughts on the discrepancy of CT values for the house keeping genes.
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Hi Everyone, Can Kiessling Ali Ahmed Alawady Dino Santos Matias John Hildyard Thank you for your insight. I am sorry for the delay in response. However, I wanted to give you all an update. After multiple steps of trouble shooting, the issue was with the conversion of RNA to cDNA. The technique was not clean using the FH74 cells. Once, this was cleaned up the expression was similar.
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How does conservation tillage (CT) improve soil health and effect of CT and crop residue management on soil physical properties and crop productivity?
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The conservation tillage technique based on straw-covered no tillage and sub soiling in the fallow period can effectively reduce soil disturbance of the plow layer, increase the surface cover and soil organic matter content and promote the storage of soil moisture.Residue retention or incorporation increases soil organic carbon, aggregate stability and better regulation of soil hydrothermal regime. Therefore, conservation tillage along with residue management on a long- term basis increases crop yield by improving soil physical properties. Incorporating crop residue with tillage practices have advantage through adding organic matter and carbon to the soil that are preconditions for the better physical, biological as well as for chemical properties. Allowance of crop residue to the soil surface reduces its bulk density and compaction.
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I know that in LighterCycler 480 system, we can use Absolute quantification/fixed points methods and then set a reasonable noise band to estimate the CT values.
My workmate told me to use this analysis method in repeating her work, however, the qPCR machine in our lab is Applied Biosystems ViiA ™ 7 Real-Time PCR System. And I really can not find out how to set the noise band or apply this method.
Thank you so much for your attention and advices!
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Oh I finally figured out it, thank you so much.
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Dear colleague
I evaluate MTF in Matlab through Edge Spread Function and Point Spread Function for computed tomography scanner but there is  a question and I would be grateful if you guided me through this.
How could I convert the x axis from distance (mm) to spatial frequency (1/mm)?
Best Regards
Faeze
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If your x axis has Nx points with a separation of dx=abs(x(2)-x(1));
then L=Nx*dx; %in mm
gives a base spatial frequency:
dfx=1/L; % in 1/mm
your spatial frequency array should be like:
linspace(-(Nx/2)*dfx,(Nx/2)*dfx,Nx);
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The camera trap photos show the ambient temperature in the image, but it looks that the info it's not in the EXIF data
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It may be able to get the so called "maker notes" out from the exif data - if they are there.
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Rectal GIST is relatively rare, and no endoscopy image and CT/MRI shows suspicion for GIST rather than particular characteristic.
We have performed an EUS biopsy, but it doesn't yield a definitive diagnosis. Just spindle cell. And it is not resectable >5cm.
What would your advice be?
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Thank you all for your kind response.
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I am wondering if the protocol of pelvis examination for obese patient should change. instead of them been x-rayed which radiation dose will sometime be equal or more than radiation dose for CT of same with higher details.
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I agree its better to doctors also to See any pelvic problem
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According to my view, even the mere opening of a drawer or a cupboard is already damaging to cultural remains. The shock of opening a drawer, the changing environment between the inside and the outside of a drawer, as well as a sudden light that falls on an artifact that has been in the dark is damaging to any item, especially to parchment, papyrus and paper.
When it comes to 'taking a sample' needed for applying a analytical technique to an artifact, one can only speak of less and more destructive, because destructive it is.
For example in Neutron Activation and Petrography, one needs either an amount of 80 mg of pottery powder or a thin-section before submitting the sample either as powder or as a pellet to a nuclear reactor or to a glass slide to be looked at under a microscope.
I think that the formula "non-destructive sampling technique" was invented by scientists to obtain samples they needed from a curator or conservator. I, therefore, suggest to omit the word "non-destructive" from the Cultural Heritage vocabulary.
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I think we should make a difference between sampling, materials and techniques when talking about non-destructive or non-invasive.
Taking a micro-sample on an artefact is invasive and destructive, while its analysis can be non-invasive and non-destructive. Also, the difference between invasive and destructive should also be taken into account. Invasive means that there is an interaction between the material and the technique, while destructive means the material is destroyed = cannot be re-analysed.
Based on that, some techniques are invasive, but non-destructive.
Also, semi-invasive techniques do not exist (at least in my opinion), while semi-destructive techniques exist. A preparation technique, such as cross-section for example, is semi-destructive. During preparation, part of the sample is polished = destroyed, but the rest of the sample remains whole and could be re-analysed (extraction from resin can be discussed). We can argue about how much invasive this preparation is then, but it largely depends on the method of preparation/protection.
When the cross-section is analysed under an optical microscope, the destructivity depends highly on the material we are observing (exclusion of staining). For most cases (hard substrates, inorganic materials, resins, some organic materials as well), it is non-destructive and non-invasive.
When SEM is used, the same principle can be applied, which means we will have a non-destructive, but invasive analysis. Again, it depends on the material analysed.
In conclusion, the terminology should be carefully explained, and what is determined as non-destructive, semi-destructive, destructive (same for the invasivity) should be carefully described.
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I used Trizol method to extract cellular RNA, and A260/A280 was always around 2.1. The RT reagent of TAKARA and the qPCR reagent of TAKARA were used. The experimental steps were all in accordance with the reagent instructions, and the Ct value of the internal reference gene (GAPDH) of the qPCR was always around 30.
Later, it was suspected that RT was the problem, but the CT value of three batches of RT reagent was still around 30. I also verified that my qPCR reagent and primer were no problem with the cDNA obtained after reverse transcription experiment by others, and the CT value was 15.
So it should be my RT and the previous extraction operation may have problems.
So I changed a very clean experimental environment to extract RNA, and the chloroform,isopropyl alcohol and ethanol used in the extraction process of Trizol were also changed into brand new ones. As a result, the Ct value of the internal reference was changed to about 27, which was still too large.
Could you give me some advice? What else could go wrong, so that the Ct value is more than 10 cycles larger than the normal value? I can be sure that I did not add the wrong reagent in each step, because I have repeatedly checked it.
Please please please help me! ! ! I've been stuck in this RT-qPCR experiment for nearly two months.
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Hey Wenyan Lv it would be of great help if you could share whether your problem has been resolved,if yes how it was solved as i am facing the same problem since few months.
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Hello everyone, I need your expertise for the absolute quantification of resistance genes from river samples.
1. After RT-PCR (Roto Gene, Qiagen) with Syber Green, I am facing a problem in getting a perfect standard curve ( R2 >0.99). I am getting R2 value of 0.95. Therefore, I am getting fluctuations in my results. What is the reason?
2. The CT value between the duplicate was high. Why? Even the same quantity of DNA was distributed.
3. What is the reason behind the substantial % variation in the input (copy number) and the calculated values?
4. How to calculate copy number and prepare a perfect standard curve after getting copy number?
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Thank you so much for your suggestion, @ Can Kiessling
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Hello,
If you are aware of any available codes for material decomposition in Spectral CT, please share them.
Thanks in advance!
U
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Yes, thanks for our response
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Hello everyone,
I am currently doing a medical image processing project. I have a dataset with 300 CT images from each person. I want to determine whether the subject is patient or not. So I need to decide based on all CT images from each person. Which deep learning algorithm can be used for this goal? Is conv-lstm a good choice?
Thanks in advanced
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Convolutional Long Short-Term Memory (Conv-LSTM) networks are a type of recurrent neural network that can be used for image processing tasks. They have the ability to process input sequences with spatial dependencies, making them well-suited for tasks such as image segmentation or prediction.
In general, Conv-LSTM networks can be a good choice for medical image processing tasks, including CT image processing. However, it is important to consider the specific characteristics of your data and the task at hand when selecting an appropriate deep learning algorithm. You may want to consider experimenting with different algorithms and architectures to see which one performs best on your data.
Other deep learning algorithms that may be worth considering for medical image processing tasks include convolutional neural networks (CNNs), fully convolutional networks (FCNs), and U-Net, among others. It may also be useful to consider ensemble methods or hybrid approaches that combine multiple algorithms or architectures.
Ultimately, the choice of algorithm will depend on the specific goals of your project and the resources available to you, and it may be necessary to try out multiple approaches to find the one that works best for your particular dataset and task.
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I read some .dcm meta data file and I think there is no field point out Dual-energy CT or Conventional CT.
Not sure how the value data is convert to material ? Is that using HU convert formulas ?
Thanks
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A single DICOM file is unlikely to contain dual energy data, since it usually only contains one image (or a stack of images usually acquired with one technique). It is likely vendor dependent how they store the dual energy acquisition images. If you have access to the machine giving you the data then you should be able to follow the image trail.
If you are asking about how a dual energy scan converts data to monoenergetic or materials decomposition, then there are a large number of papers out there describing the process. Here as a random example:
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Which is the better option for performing radiomics analysis on CT images? Should radiomics features be extracted directly from CT images with HU values, or should the CT images be converted to ED maps first before extracting features?
The reason I am asking this question is: although there are many great pieces of research looked into the repeatability and reproducibility of radiomics features based on CTs, including test-retest, intra-, and inter- CT scans, however, I found that few of them talked about whether they calculated the features based on HU images or ED images. So I am quite curious, for instance, if we want to calculate the radiomics feature reproducibility between different CT scanners, in order to eliminate the potential difference from the scanner's physical setting (such as energy, phantom setup error, etc..), which method is more convincing and fair?
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I would argue that the information contained in HU is relevant to radiomics training, in contrast to other modalities (x-ray, MRI and US) where the values are somewhat artificial. This does not "fix" the problem the scan protocol differences or lots of other hidden filters that are used in image formation.
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I want to compere fold change for 3gene in 4 groups (control, g1,g2g3) i have CT of 3 genes for 4 groups (control, g1,g2g3) and CT of house keeping gene so i have one dct (dct =CT (target)_ CT (hous keeping gene) could you tell me how can I Computing another dct ?
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You are welcome! and Good Luck
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If this is the gene NDRG4 of Controls range:
Positive 32-38
Negative undertermined or >=39
NTC undertermined or >=39
The results are inconsistent. Which result will you be confident? as this test rely with a ratio to other gene result. the combination result calculated in the system and interpret as "Positive/Negative"
1st result: 37.26 CT (POSITIVE)
*RE-PCR in the same batch with 3 criteria:
2nd result: 41.54 (NEGATIVE)
3rd result: we add duplicate template 39.34 (NEGATIVE)
4th result: 4fold dilution 37.34 (POSITIVE)
*FAM (BLUE) is the NDRG4 gene
Please kindly refer to the photo, Thanks.
For me, I'll release the 1st valid result as negative. Please correct me.
Regards,
Mercy
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Hi @kim ya the graph looks strange as the ct value is higher (lower ct) for the dilution. It all make sense now that my colleague interchange the result.
Thank you for replying. I really appreciate.
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Which software do you recommend for analyzing computed tomography images?
The project is about bird brains so it involves measurements and anatomical reconstructions. I have heard about Avizo, Mimics and SlicerMorph. Which do you suggest and why?
Thank you all in advance.
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I personally prefer 3D Slicer, and I think it provides extensions for the software you mentioned above.
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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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I am measuring the gene expression of my target gene DDX43. I took 2.5 ul of cDNA for Q-PCR. I am using SYBR GREEN for my q-PCR. The concentrations of RNA for most samples were 20-60 ng/ul.
The CT value for housekeeping gene hypoxanthine phosphoribosyl transferase 1 (HPRT1) is around 32-36 and CT value for for DDX43 with all samples were"undetermined". When I search in expression atlas for my target gene. I find it below cutoff in the tissue I used for RNA extraction.
Can I add up to 5ul of my samples in running q-PCR? or this result is consistent with below cutoff expression which means it is undetected so undetermined in qPCR?
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Hi Noha,
Yes you can raise the template amount providing you will do the same for the housekeeping protein.
I would like also to suggest that you check your primer of interest and to make sure that there are no bubbles in platee wells before carrying out PCR.
Good luck
Please give us a feedback.
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I am coming from the background of Computer Science and now I am doing Phd. in eduaction. I want to connect my core subject computer science with the subject eduaction. I want to do a survey study on the topic Computational Thinking (CT)and Digital Competence (DC). Here I want to work on the relationship between CT and DC. I eagerly need your help and your valuable suggestion so that I can forward my work.
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It is more than amazing . Go on . Please send me your survey to comment on more .
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I want to apply neural network on kidney stones images (whether its CT images or ultrasound) to determine whether the kidney has stones or not.
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Please refer "Region Descriptors to Extricate Renal Calculi by Discerning Artifacts from Ultra Sound Images"Research Journal of Applied Sciences, Engineering and Technology 9 (5), 374-379
Have included few ultrasound Kidney stone images
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I am working on rtPCR for different primers and different cDNA samples as a duplicate to confirm the results. However, I am suffering to make the duplicate has the same or close CT value. In general, the difference between the duplicate is more than 3, and sometimes the difference in CT value reaches more than 8. While preparing the primers, diluted cDNA, and master mix the microtubes placed on ice, I couldn't find the reason for the differences between the CTs for wells that have the same primes and the same cDNA sample.
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Typically, any variation between technical replicates is due to your pipetting and/or mixing skills. But there are other possibilities.
Do you see any air bubbles or evaporation in the wells?
Are your pipettes well-calibrated?
Are your qPCR reagents new & not exposed to multiple freeze-thaw cycles?
Is your qPCR machine calibrated?
Are there certain wells that always give errors regardless of the sample type?
I'd suggest scaling down your experiment and starting with doing a positive control and a negative control with 3-4 replicates of each. That will help you narrow down the list of possible issues. And ask other users if they are having similar problems with the qPCR machine.
Good luck!
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I can find very little in the literature about the use of CT for examining slide mounted specimens. This is obviously very important because of the number of slide mounted type specimens out there. Confocal is often not effective for examining slide mounted type specimens because of autofluorescence and bleaching. Is CT simply no good for slide mounted specimens, or this a neglected field of research. I have little idea as my background is largely conventional microscopy and confocal. And if anyone does know of any literature on this topic, can they please point me to it. Thanks very much in advance.
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Are you talking about microscopy mounted slides or actual physical specimens greater than a few millimeters thick? X-ray attenuation will barely exist through something that small and it may be challenging for other technical reasons. As Dr. Thoma mentioned, cost vs. gain of information is also out of balance. If you are asking about "3D imaging", then there are other ways to obtain 3D information from specimens. Relying on electron density differences may not be the best approach for your goal. That being said - go ahead and try it! I'd be happy to learn from you.
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Hi everyone,
I'm looking for a free cardiovascular image dataset of any imaging modality (MRI, CT, OCT, SPECT, ...), I will use it for classification and detection.
Thank you in advance.
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I understand that cycle threshold or CT value is an important determinant for secondary transmission of cases, whereas it is not associated with severity or infection. I need others opinion about it.
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Dear Dr Gajanan, thank you for your response. I understand what you mentioned and logistic issues of RT PCR. But I feel logistic issues can be standardized to have some uniformity. In fact, many of the recent publications are more & more in favor of CT value as an indicator of transmissibility. But there is no concensus about it as an indicator of severity of disease. We may need to wait for more information to be available before coming to a final conclusion
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I am planning to compare CT values of two population set during Delta and Omicron surge. General overview would should a difference but need to apply statistical tool.
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You also need to compare the patient size, can be based on chest AP and lateral diameters. And also consider the CT range size. In terms of statistical analyses use the parametric or non parametric tests (considering the sample characteristics) to compare CTDIvol and DLP values.
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I have to check the radiotherapy plan and dosimetry distribution for a CT image that I have generated and need to compare it with other plan.
Is there any free software to compute the radiotherapy plan using a CT volume?
Please suggest one.
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I am a new researcher in the X-fluorescence Computed Tomography field. I am doing the attenuation correction of the XFCT imaging system and I am confused with something.
As we know that while doing the attenuation correction for emission tomography, we need to convert the attenuation coefficient value for the energy of 511 KeV by using some Bi/Trilinear method! That means :
""Transmission CT energy ( attenuation coefficient value) ==> Emission CT energy ( attenuation coefficient value) "".
As it is known XFCT has two parts: Fluorescent Excitation ( Transmission) and Fluorescent Emission. I have Attenuation co-efficient values for 30KeV energy.
What will be the process for converting the attenuation co-efficient values for XFCT? Would I need to convert the attenuation co-efficient value two times ( for transmission and emission) ?
I am a bit confused about this problem!
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you are right.
There are two attenuation coefficients involved:
a) that of the excitation radiation, which has a photon energy a bit higher than the absorption edge energy of the excited atom, and
b) that of the fluorescence radiation, the photon energy of which is smaller than
and the edge energy.
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I would like to ask anesthesiologists or clinicians who have experienced anesthesia mumps. Was there a case in which the swelling of the parotid gland is associated with a retrograde insufflation of air in the Stensen’s duct and the parotid gland (pneumoparotid)? If you know the literature on anesthesia mumps confirmed by CT as pneumoparotid, I would appreciate it if you could let me know.
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From the following references, I found that "the case introduced by Takelioglu et al. [1] is the first presentation of pneumoparototis as anesthesia mumps [2]". If you have any other literature or experience cases, I would appreciate it if you could let me know.
1. Takelioglu UY, et al. A case of anesthesia mumps after general anesthesia. J Anesth. 2012;26:130-131.
2. Adachi YU, et al. Is it fluid or air causing anesthesia mumps? J Anesth. 2012;26:638-639.
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I am looking for a COVID-19 image (CT or x-ray) dataset that has additional information such as age, gender, and clinical information such as co or previous morbidities of the patients. Any link would be appreciated.
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Am working on femur bone biomechanics.I do have assigned the material properties to the geometry model generated from the CT data in mimics. But couldn't able to do the same for external meshed body(Means the same geometry after initiating the fracture, virtual surgery with implants in some other software). I just want assign the materials of femur for surgery models. Please let me know with possible solution.
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You might use Abaqus for analysis with materials by importing 3D model from MIMICS.
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This happens both in mouse and rats samples. After perfusion with saline we use to perfuse with Microfil. We then store the samples as required and acquire by CT. After reconstruction the vascular tree is usually perfectly represented as a continuum but sometimes we detect some kind of interruptions along the vessel (white vessel profile, then a dark void and then again the white vessel profile). What are these areas of void due to? How could they be overcome considering that perfusion quality is almost the same all the time? Thank you.
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It seems perhaps that your perfusion was sub-optimal. The microfil injection technique is tricky and not 100% reproducible even for the world's most advanced users. The leading article on this topic (ref. ) talks about how to get a more uniform filling of the vessels, however it is not perfect at each try.
There are a plethora of different published perfusion strategies with microfil. From my experience, the learning curve to master this technique is steep.
The target organ to perfuse is another important factor to consider when considering the success of perfusions. Some organs seem harder to perfuse with this technique than others and factors like retrograde or anterograde perfusion method need to be considered depending on the organ of interest.
To get the optimal perfusion, you will definitely want to avoid air bubbles but also consider using a pump that can measure the back-pressure and maintain a constant injection pressure. ref. https://livingsys.com/product/pressureservo-control-wperistaltic-pump/
You would also want to consider using a less viscous alternative to microfil like Vascupaint (ref. https://www.medilumine.com/product/vascupaint/) to get a more consistent filling of the vasculature. Here is video showing an example of a Vascupaint perfusion. ref. https://www.youtube.com/watch?v=Icm1OG-vUl4
Other products like microangiofil (ref. https://www.medilumine.com/product/%ce%bcangiofil/) are based in polyurethane instead of silicone and undergo a slight but rapid viscosity increase after component mixing (unlike Microfil and Vascupaint). They therefore require a mixing tube apparatus for the perfusion. This ensures that the contrast material can enter the organism directly after being mixed so that viscosity increase can occur while the agent is flowing through the non-surviving organism and not in the syringe. This appears to also be giving more consistent results compared to microfil.
There is no literature explaining how slight viscosity increase of a newtonian fluid during a perfusion procedure could actually help with the perfusion and reproducibility of the technique. Clearly, such considerations, as well as potential benefits from non-newtonian aspects of a fluid (if there are some), should be considered. We are currently looking at this in our laboratory to help potential users that desire a more straight forward procedure for perfusing non-surviving organisms with hardening polymers.
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Donor-Bridge-Acceptor (DBA) or Donor-Acceptor (DA) architectures are widely employed protocols to achieve Charge Transfer (CT) in organic semiconductors. Lower Exciton Binding energies (EBE) are required to attain higher efficiencies in Solar Cells.
By exploiting the dipole moment changes we design Donor-Electret-Acceptor moieties which enhances hole and electron distance upon excitation favoring higher CT and CT distance, reducing EBE, plausibly enhancing solar cell efficiencies in the following article.
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This is a good question and prof. Abderrahmane Kadri gave an answer to it.
As said the main problem in organic solar cells is the too high binding energy in the excitons and their short lifetime in the material. This leads to a very short distance to the interface between the donors and acceptors in order to separate the exciton before it recombine. In fact the acceptor material build an attractive electric field on the electrons of the excitons to dispatch them further to the ETL.
So, as you see the separation is affected by the attracting electric field of the acceptor. The exciton diffusion length dictates the distance between the dodnors and acceptors. If there is an electric field agent or conveyor between the two one can further increase the distance between the donors and acceptors. This leads to smaller interface area between the donors and acceptors which is the main cause of recombination losses.
This is really a good idea.
Best wishes
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Hello,
we using the same amount of RNA for cDNA synthesis, and we are sure there are no hand mistakes for doing qPCR. using both TaqMan and SYBER green master mix, the qPCR instrumentt name is Quantstudio3.
reaction size is 20 ul, for information, the instrument is brand new and was calibrated one month before
CT values were 10.45,10.67,10.47 in control and 12.45,12.67,12.47 in Treated gp
the CT values of the reference should be close and the difference should be no more than 0.5, but we see the difference is around 2 CT values.
we repeat the experiment more than 4 times we still face this problem if anyone has a suggestion it will be grateful to give advice
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Dear Osama Sweef,
it cannot be excluded that your treatment conditions have an effect on the expression of the reference gene. Therefore, you should start with a run that only includes different potential reference genes in order to find the one (or more) that shows the lowest differences in Ct values between control and treatments. Afterwards you can choose one (or more) suitable reference gene that was not affected due to your treatment. In addition to 18S, you could check GAPDH, HPRT1, GUSB, ACTB or TUBA1A, for instance.
Good luck. - CL
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Could You please give me a link to a source where I can get (for free) a data set of contrasted CT images of an abdominal aortic aneurism in DICOM (or which?) format manually segmented by a specialist? Even a normal abdominal aorta will fit.
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Hi,
This article may be useful to you
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I have, extinction ratio (ER)=25.6dB ; Crosstalk (CT)= <-18db, Insertion/Excess loss(IL)= 0.51 (0.04)db. How to calculate bandwidth for multiplexer?
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William Thoma You are right at your point 1. But I am trying to use Multiplexer for Optical Phased Array (OPA) for parallel beam steering. It's a new idea and still in theory basis. I am using tapering instead of gate configuration.
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When we perform housekeeping genes (such as GADPH) for baseline comparison control in RT-PCR analysis, the degrees of CT values were found to vary a lot between different animals (in our case, 5 control rats) from 17-22. But we have to choose one CT value for baseline comparison of other genes of interest. Do anyone encounter the same condition and what is the solution?
Thanks a lot in advance!
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Thank you!
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I am looking for a CT dataset of bone fractures in different bones to reconstruct 3D models. I am using these models for developing automated design algorithms. If there is a database of already existing 3D models of bone fractures, that would be useful as well.
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Changing the HU values of the DICOM CBCT image
Hello everyone,
I have a DICOM image from CBCT and a DICOM image from CT. The two images describe the same object, but each image has different gray values (HU values).
The goal is to overlay the CBCT image with CT image. I.e. plannings CT image is subtracted with CBCT to see if the two images are completely transferred.
The range of HU values for CBCT image is between 0 and 1000.
The range of HU values for the Plannings CT image is between 0 and 800.
Now I wanted to reduce HU values of CBCT image by 200 HU values so that HU values of CBCT correspond to HU values of Plannings CT.
My question is the following, Is it allowed to change or manpulate the HU values of DICIOM.
As far as I know the HU values describe physical cause.
I've been stuck with this for a week, is there any other way to make the two CTs have the same HU values?
Thank you for any answer
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Thresholding of the CT and CBCT scan can be done in the same software. The images if inherently different may lead to some differences in the measurements.
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We have many cases with normal D-dimer with the normal CT pulmonary angiography.
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If you have elevated d Dimer along with clinical features suggestive of pulmonary embolism and / or echo features of pulmonary hypertension but at the same time normal pulmonary CT, you should ask for pulmonary ventilation Perfusion scan (VQ scan). Because some of CTEPH are only detected by VQ scan.
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I am working on developing a real-time PCR assay but I don't know how to set up and/or define the range for CT values which could help me in lower and upper CT values for determining the positive and negative samples of the experiment.
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The lower Ct is defined only by the need to have some cycles "in the background" so that one can identify the background signal and recognize the beginning of the exponential part of the amlification curve. This will be difficult ifthe Ct is less than about 10 or so.
The upper Ct is usually at around 35, but this may depend on the assay. To get better estimates of the upper bounnd you may generate a standard (could be a PCR product amplified with primers outside the qPCR primers), purify and quantify this product and measure it in qPCR in a dilution series. You would actually need only the dilution containing 0.5 copy (on average).You can expect 39% of reactions give a positive result, and among those 77% will contain only a single template molecule (the rest will contain 2 or more copies). That should give you a good idea what Ct you can expect if there is just a single molecule in the PCR.
A different question is that for the dynamic range of reliable quantification. Here you must define what "reliable" means. Particulrly at the lower concentrations, Poisson variation limits the achievable precision.
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For my thesis I'm in need of finding information on incorrect fitting patient specific surgical guides due to the partial volume effect in the segmentation of the taken CT data for a osteotomy procedure. Can anyone help me out? All tips are welcome!
Thanks and best regards,
Richard
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I have done this specific training in DQBITO https://www.dqbito.com/.
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Hello,
Can someone with experience in CT image analysis help me to understand the elements of the pictures. It is a plate thermoformed from waste like ABS and PMMA polymers and fiberglass. I need to know what black dots represent, white particles, and also if fiberglass is visible.
Thank you!
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Dear Mihai, I can see that you are using our Software from Volume Graphics. The best would be to go through the training materials first and get an understanding of CT data. What you see is always the different absorption of X-Rays of different materials. The darker the "dots", the closer the absorptin is to the back groud material. In fact it can also be porosity of it is very similar. The "lighter" the dots are, the more absorption there is for X-Rays. It does not necessarily match the houndsfield scale at all, because industrial CT's do not get calibrated for medical purposes and then water is not having a houndsfield value of zero and air of 1000 what a doctor may expect. The grey values may reach to several thousands. If you can see fibers actually depends on the resulution you get from your setup. Check for the voxel size (properties of the dataset, rightclick in the scene tree). The voxel size needs to get very close to the fiber size, otherwise it won't show up as it cannot be resolved. Best get some infos from our support center. I think we can help with some basics in the physics and a training in the software.
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Hello Dear Colleagues!
We are trying to determine CTDIvol and DLP from CT scans using CT Expo v 2.7.
However, we need to enter the scan length (in cm) into CT Expo. Does anyone know how to calculate the scan length using the information in the DICOM header?
The equipment is old, we only have the following tags:
Single Collimation Width (0018,9306)
SliceLocation (0020,1041)
SliceThickness (0018,0050)
And in addition there is no information in the
SpacingBetweenSlices (0018,0088) tag.
Can anyone help us?
Best regards
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You can also use RadiAnt (for not commercial purposes), it is very friendly.
Open your sequence, click the icon "A" ( Annotations), and select Show Dicom Tags. You will be able to explore all official Dicom tags, private TAGs could be harder to guess.
Alternatively, you can select "Multiplanar Reconstructions", select a Coronal or Sagittal and measure the extension of the volume in the longitudinal axis.
RadiAnt can be downloaded here: https://www.radiantviewer.com/
As a last resource, you will be forced to use SliceLocation assuming that ImageOrientationPatient is orthogonal to the Gantry plane.
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Need CT image database for Normal Pneumonia.
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Hi Vivek,
Take a look in our database in our new free medical imaging question and answer forum ( www.imagingQA.com ). Search here :
Feel free to sign up too, and join the growing community.
If you can't find what you're looking for, please feel free to open a new topic at the link below :
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I knew tools for dicom format but I want for nifti, is there?
thanks
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Evgeny Lipovetsky thanks Evgeny for your answer. I already have the images in dicom format.
James O'Callaghan thanks for your recommendation. appreciated.
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I don't have enough cDNA, so I need to dilute 1: 3. The problem is that I can't figure out what calculations I should do when obtaining the CT to calculate the DDCt. Anyone who can help me with this? Thank you.
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Hi!
You do not need correct for dilution in calculations, if your objective is to compare the transcript levels of the interest gene between different treatments. Just adjust your qPCR to amplify between cycles 15-25 where your machine is more precise.
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I am looking for CT image dataset for COVID-19 that have annotated the infection areas with severity score.
Please, can you help to find such dataset.?
Thanks in advance
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Hi. I hope the following article could help you:
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In this regard, I'm looking for a CT/MRI dataset required to train a CNN model for medical image segmentation.
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If the existence of a pathology does not matter you can use TCIA .
You can find any kind of data set in there and I assume it is free
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Currently, I'm working on a paper that deals with CT image classification. I am using two open-source datasets among which, I can't find any relevant paper on the Harvard Dataverse CT image dataset (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.7910%2FDVN%2FSZDUQX) to compare with the results of our paper.
Can you please suggest a few published papers?
Thanks :)
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Question about expanding the linear range for my standard curve dilution in Taqman probe qPCR? I am doing a 4-fold serial dilution using the Taqman probe assay to generate a standard curve dilution, and it was a multiplex qPCR. And I am trying to use the reagent and primers concentration from a paper, and the amount of concentration for the primers and reagents I used is the same as the paper. The sample I used Nanodrop result is shown attached. I am using 4-fold dilution (3ng-0.01ng), 1800 copies of DNA, and 2 copies. The paper uses plasmid 3.8kb length(10pg-1fg) 5-point dilution 10^6 copies DNA to 10^1 copies. And They are using 10-fold dilution. By looking at their standard dilution plot, in the high end (low CT, high input), and compared their plot and my plot, their slope looks steep in the high end, my plot looks flatten. The slope is about -3.7, and the qPCR amplification efficacy En is 88%. How to improve my standard curve in the high end makes the plot steep, improving my qPCR amplification efficacy? How to expand my linear range for my curve? I only can use the lower end (High CT, low initial input DNA amount) for now.
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Dear Delong Zhang ,
To remove PCR inhibitors, you may need to select a good DNA extraction kit for your samples, some times manual extraction works better to remove PCR inhibitors specially phenol -chloroform extraction. the following link can help you to select kits
you also need to increase the number of your standard curve points as much dilution in your case is better,
PCR inhibitors and DNA concentration will be reduced but still DNA is detectable.
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There are reference values for different lung volumes based on age, weight, BMI, BSA, etc., e.g., Garcia-Rio F et al (2009), doi:10.1164/rccm.200901-0127oc. Are there equivalent methods that would allow CT-based estimates of reference values? For example, a CT would allow determining the gas volume of a patient at an end-expiratory breath-hold. But, that volume is different from FRC the patient would have if healthy and in an upright position. It would be ideal if the CT could be used for an anatomical measurement that would allow estimating the reference volume, e.g., FRC for that patient and the deviation of gas volume in the CT from the reference value.
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Yes, you describe the problem very accurately: the gas volume at the time of imaging is not necessarily at a well-defined status, e.g., the functional residual capacity (FRC) at passive equilibrium between alveolar and atmospheric pressure. Additionally, body position affects regional end-expiratory gas volume in the lungs. So, I'm wondering if the FRC (in a standard upright position) as a measure of 'lung size' is strongly correlated with an anatomical feature related to lung size, perhaps, an anatomical parameter of the rips or the sternum so that this parameter could provide a reliable reference for normalization.
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Infected aortic aneurysms are diagnosed on the basis of a positive bacterial blood culture, clinical evidence of inflammation, and morphologic findings on computed tomography (CT). However, preoperative diagnosis is often difficult because blood cultures are frequently negative and patients can be asymptomatic. Because therapeutic approaches differ significantly, it is vital to determine whether an aortic aneurysm is infected prior to surgery.
There are some literatures which have shown that fluorodeoxyglucose positron emission tomography-computed tomography (F-FDG-PET/CT) examination is useful in the diagnosis of infected aortic aneurysms.
However, I wonder whether or not FDG-PET/CT takes place of the conventional imaging diagnosis methods?
【References】
* Clin Nucl Med. 2008 Jul;33(7):492-5.
F-18 FDG PET/CT in the management of infected abdominal aortic aneurysm due to Salmonella.
* Ann Vasc Surg. 2014 Apr;28(3):575-8.
Fluorine-18-fluorodeoxyglucose positron emission tomography-computed tomography for diagnosis of infected aortic aneurysms.
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The diagnosis of infected (= mycotic) AAA is at times difficult. The current European Guidelines (Wanhainen et at, Eur J Vasc Endovasc Surg (2019) 57, 8e93) do not discuss PET in the diagnosis. In single case reports (LR Leon . 2010 Jan;44(1):5-13.Vasc Endovasc Surg doi: 10.1177/1538574409344225) FDG-PET was considered helpful.
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How can I assess the SUVmean, max and ratio in a PET-CT scan using Horos?
I know how to create a ROI and read the hounsfield units/Bqml for CT or PETCT scans but I don't know how I can see what the SUVmean for that ROI is.
Is it also possible to select an ROI in a CT-scan and connect this scan to the PET-images, thereby automatically selecting the same ROI in the PET scan and thus known both HU/BQML and SUV for that region?
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Hi Anouk,
I have not used Horos, so hopefully, someone with specific experience will come along and answer your question.
Most analysis software should allow you to save your ROI drawn on the CT and to then upload it on the PET scan. You should ensure that both PET and CT are co-registered and are handled similarly (for example, do not crop then differently)
If the software does not give the SUV values itself, then it can be calculated as Activity concentration in the tissue/Activity concentration in the whole body. Activity concentration in the whole body is injected dose/body weight. Make sure to use the same units for both tissue concentration and body concentration.
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Are there colleagues who would like to work on ground-glass opacity ("verre dépoli") evaluation using computed tomography (CT) imaging of the lungs ? The work can be extended to crazy paving (combining frosted glass, inter-lobular and intra-lobular cross-linking) evaluation. More details can be given later.
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Hi,
I would like to highlight a free, submillimetric, dataset made of 62 covid-positive CTs.
The dataset is provided alongside automatic labels (as well as the description of the strategy used to generate those labels).
The article is the following:
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By looking at a medical image, how can i identify whether it is an ultrasound or MRI or CT image?
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Dear all,
I would like to measure the total lung SUV on Horos. Is it possible to segment lungs on CT and warp the segmented volume on the PET images to measure the total lung SUV?
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thank you , I will give it a try
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I have nanoparticles in dried powder form. I am looking to suspend them in appropriate medium and image on CT and MRI machine.
What is the appropriate solvent for zirconia nanoparticles ?
How much concentration of nanoparticles in mg/ml in a glass vial?
Simply image a glass vial on both machines ?
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Hi Obaid,
Ax T1 Propeller is the name of an imaging sequence. I assume this particular sequence was configured for a particular clinical scan, and will require modification for you to image your vial of solution (slice thickness, averaging etc.).
You will probably also need to optimize your sequence (and thus the image contrast) for TR and TE to provide sufficient T1 or T2 weighting (depending on what you are trying to achieve). You might want to determine the T1 of the solution using an inversion recovery sequence (or other available method) which would help to guide you further. You can also calculate the T2 using one of 2 techniques; either run the same spin echo sequence many times with a change in the TE, or run one multi-echo sequence one with multiple TEs.
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Dear fellow researchers,
I am looking for a freely available 3D-anatomcial scan of a rat's head (encompassing not only the brain but ranging from neck to snout).
CT or T1w MRI would both work and it does not necessarily have to be a standard template as long as the (adult) rat (preferably Wistar) was of average size and there are no obvious pathologies.
Can you reccomend any resources for that?
Thank you for your help!
Kind regards,
Peter
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Thank you for your input. Unfortunately, though, I could not find any non-human atlases other than macaques’ in the MNI’s resources
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I use the contour integral method in ABAQUS to calculate the J-integral of CT specimen(2D). The constitutive model is Ramberg-Osgood model, which is the deformation plasticity in ABAQUS. When the Nlgoem is Off, the J-integral is correct. However, the mesh deformation in the crack tip region is very large. When the Nlgoem is On, the analysis will terminate because of too many attempts made for this increment.
I will be appreciate that if you can help me to solve this problem.
PS:
The CT model, the the mesh deformation in the crack tip region, and the results of J-integral are shown below.