| dc.description.abstract | X-ray computed tomography (CT) is a medical imaging framework. It takes measured
projections of X-rays through two-dimensional cross-sections of an object from
multiple angles and incorporates algorithms in building a sequence of two-dimensional
reconstructions of the interior structure. This thesis comprises a review of the different
types of algebraic algorithms used in X-ray CT. Using simulated test data, I
evaluate the viability of algorithmic alternatives that could potentially reduce overexposure
to radiation, as this is seen as a major health concern and the limiting
factor in the advancement of CT [36, 34]. Most of the current evaluations in the
literature [31, 39, 11] deal with low-resolution reconstructions and the results are
impressive, however, modern CT applications demand very high-resolution imaging.
Consequently, I selected ve of the fundamental algebraic reconstruction algorithms
(ART, SART, Cimmino's Method, CAV, DROP) for extensive testing and the results
are reported in this thesis. The quantitative numerical results obtained in this study,
con rm the qualitative suggestion that algebraic techniques are not yet adequate
for practical use. However, as algebraic techniques can actually produce an image
from corrupt and/or missing data, I conclude that further re nement of algebraic
techniques may ultimately lead to a breakthrough in CT. | en |
