  Three-dimensional x-ray CT image reconstruction in baggage scanning in
security applications is an important research field. The variety of materials
to be reconstructed is broader than medical x-ray imaging. Presence of high
attenuating materials such as metal may cause artifacts if analytical
reconstruction methods are used. Statistical modeling and the resultant
iterative algorithms are known to reduce these artifacts and present good
quantitative accuracy in estimates of linear attenuation coefficients. However,
iterative algorithms may require computations in order to achieve
quantitatively accurate results. For the case of baggage scanning, in order to
provide fast accurate inspection throughput, they must be accelerated
drastically. There are many approaches proposed in the literature to increase
speed of convergence. This paper presents a new method that estimates the
wavelet coefficients of the images in the discrete wavelet transform domain
instead of the image space itself. Initially, surrogate functions are created
around approximation coefficients only. As the iterations proceed, the wavelet
tree on which the updates are made is expanded based on a criterion and detail
coefficients at each level are updated and the tree is expanded this way. For
example, in the smooth regions of the image the detail coefficients are not
updated while the coefficients that represent the high-frequency component
around edges are being updated, thus saving time by focusing computations where
they are needed. This approach is implemented on real data from a SureScan (TM)
x1000 Explosive Detection System and compared to straightforward implementation
of the unregularized alternating minimization of O'Sullivan and Benac [1].
