Questions related to X-ray Computed Tomography
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?
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
Call for Book Chapters:
Intelligent Diagnosis of Lung Cancer and Respiratory Diseases
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
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.
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.
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
3. Viral and bacterial pneumonias
5. Emergent imaging techniques
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
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.
• 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
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 - email@example.com.
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?
I have an unknown sample to characterize using different techniques (mainly pore size distribution):
-X-ray computed tomography
-Mercury intrusion 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.
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 !
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?
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.
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.
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.
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?
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?
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
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.
What is the best method to assess the absorbed dose by the target organs during an X-ray computed tomography?
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?
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?
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 :-( )
I have 2 CT images with different resolution. I would like to spatially align them.
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.
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?
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?
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
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:
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?
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.
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.
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.
Improve the resolution by destructive or non-destructive means in 3D computed tomography.
imgRec is software developed for processing tomography scan data (cleaning it up).