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The mathematical basis for '''tomographic imaging''' was laid down by [[Johann Radon]]. It is applied in [[Computed Tomography]] to obtain cross-sectional images of patients. This article applies in general to '''tomographic reconstruction''' for all kinds of [[tomography]], but some of the terms and physical descriptions refer directly to X-ray computed tomography.


[[Image:Tomographic fig1.png|thumb|Figure 1: Parallel beam geometry. Each projection is made up of the set of line integrals through the object.]]


The projection of an object at a given angle <math>\theta</math> is made up of a set of ''line integrals''. In X-ray CT, the line integral represents the total attenuation of the beam of [[x-rays]] as it
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travels in a straight line through the object. As mentioned above, the resulting image is a 2D (or 3D) model of the [[attenuation coefficient]]. That is, we wish to find the image <math>\mu(x,y)</math>. The simplest and easiest way to visualise method of scanning is the system of [[parallel projection]], as used in the first scanners. For this discussion we consider the data to be collected as a series of parallel rays, at position <math>r</math>, across a projection at angle <math>\theta</math>. This is repeated for various angles. [[Attenuation]] occurs [[exponential decay|exponentially]] in tissue:
 
:<math>I = I_0\exp\left({-\int\mu(x,y)\,ds}\right)</math>
 
where <math>\mu(x)</math> is the attenuation coefficient at position <math>x</math> along the ray path. Therefore generally the total attenuation <math>p</math> of a ray at position <math>r</math>, on the projection at angle <math>\theta</math>, is given by the line integral:
 
:<math>p(r,\theta) = \ln (I/I_0) = -\int\mu(x,y)\,ds</math>
 
[[Image:Ct skull.jpg|thumb|right|250px|Projected X-rays are clearly visible on this slice taken with a CT-scan]]
 
Using the coordinate system of Figure 1, the value of <math>r</math> onto which the point <math>(x,y)</math> will be projected at angle <math>\theta</math> is given by:
 
:<math>x\cos\theta + y\sin\theta = r\ </math>
 
So the equation above can be rewritten as
 
:<math>p(r,\theta)=\int^\infty_{-\infty}\int^\infty_{-\infty}f(x,y)\delta(x\cos\theta+y\sin\theta-r)\,dx\,dy</math>
 
where <math>f(x,y)</math> represents <math>\mu(x,y)</math>. This function is known as the [[Radon transform]] (or ''sinogram'') of the 2D object. The [[projection-slice theorem]] tells us that if we had an infinite number of one-dimensional projections of an object taken at an infinite number of angles, we could perfectly reconstruct the original object, <math>f(x,y)</math>. So to get <math>f(x,y)</math> back, from the above equation means finding the inverse Radon transform. It is possible to find an explicit formula for the inverse Radon transform. However, the inverse Radon transform proves to be extremely unstable with respect to noisy data. In practice, a stabilized and discretized version of the inverse Radon transform is used, known as the [[filtered back projection]] algorithm. Recent developments have seen the Radon transform and its inverse used for tasks related to realistic object insertion required for testing and evaluating [[Computed Tomography]] use in [[Airport Security]].<ref name="megherbi13radon">{{cite book| author=Megherbi, N., Breckon, T.P., Flitton, G.T., Mouton, A.| chapter=Radon Transform based Metal Artefacts Generation in 3D Threat Image Projection| title=Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence|date=October 2013| volume=8901| number=B| pages=1–7| publisher=SPIE| doi=10.1117/12.2028506| url=http://breckon.eu/toby/publications/papers/megherbi13radon.pdf| accessdate=5 November 2013}}</ref>
 
== References ==
<references />
 
== Further reading ==
*[[Avinash Kak]] & Malcolm Slaney (1988), Principles of Computerized Tomographic Imaging, IEEE Press, ISBN 0-87942-198-3.
 
== External links ==
*http://www.slaney.org/pct/
*http://www.itk.org/ Insight ToolKit; open source tomographic support software
*http://www.visielab.ua.ac.be/software ASTRA toolbox; very flexible, fast and open source tomographic software
*http://niftyrec.scienceontheweb.net/ NiftyRec; comprehensive open source tomographic reconstruction software; Matlab and Python scriptable
 
[[Category:Radiology]]
[[Category:Medical imaging]]
[[Category:Inverse problems]]

Latest revision as of 15:24, 7 October 2014


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