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Iterative reconstruction is a method or group of algorithms used to reconstruct 2D and 3D images from the projections of an object. The technique differs greatly from the more ubiquitous filtered back projection (FBP) method.
In X-ray computed tomography, this method is not currently in use on medical scanners largely because it is still computationally much slower than FBP.
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Basic concepts
This approach was the one first used by Hounsfield. There are a large variety of algorithms, but each starts with an assumed image, computes projections from the image, compares the original projection data and updates the image based upon the difference between the calculated and the actual projections. Although conceptually this approach is much simpler than FBP, for medical applications it has traditionally lacked the speed of implementation and accuracy. This is due to the slow convergence of the algorithm and high computational demands. For these reasons, it was superseded by the FBP method in the early development of CT.
Advantages
The major advantages of the iterative approach include insensitivity to noise and capability of reconstructing an optimal image in the case of incomplete data. Situations where it is not possible to measure all 180 degrees may be more amenable to solution by this approach. (For example cross-borehole measurements in earth resources imaging). The method has been applied in emission tomography modalities like SPECT and PET, where there is significant attenuation along ray paths and noise statistics are relatively poor.
As another example, it is considered superior when one does not have a large set of projections available, when the projections are not distributed uniformly in angle, or when the projections are sparse or missing at certain orientations. These scenarios may occur in intraoperative CT, in cardiac CT, or when metal artifacts require the exclusion of some portions of the projection data.
See also
References
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- This page was last modified on 5 April 2008, at 21:35.
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