Abstract
In general, a theoretical Computerized Tomography (CT) imaging problem can be formulated as a system of linear equations. The discrete inverse problem of reconstructing finite subsets of the n-dimensional integer lattice 𝕫 n that are only accessible via their line sums (discrete x-rays), in a finite set of lattice directions, results into an even more ill-posed problem, from noisy data. Because of background noise in the data, the reconstruction process is more difficult since the system of equations becomes inconsistent easily. Unfortunately, with every different kind of CT, as with many contemporary advanced instrumentation systems, one is always faced with an additional experimental data noise reduction problem. By using Information Geometry (IG) and Geometric Science of Information (GSI) approach, it is possible to extend traditional statistical noise reduction concepts and to develop new algorithm to overcome many previous limitations. On the other end, in the past five decades, trend in Systems Theory, in specialized research area, has shifted from classic single domain information channel transfer function approach (Shannon’s noisy channel) to the more structured ODR Functional Sub-domain Transfer Function Approach (Observation, Description and Representation), according to computational information conservation theory (CICT) Infocentric Worldview model (theoretically, virtually noise-free data). CICT achieves to bringing classical and quantum information theory together in a single framework, by considering information not only on the statistical manifold of model states but also from empirical measures. In fact, to grasp a more reliable representation of experimental reality and to get stronger physical and biological system correlates, researchers and scientists need two intelligently articulated hands: both stochastic and combinatorial approaches synergically articulated by natural coupling. As a matter of fact, traditional rational number system ℚ properties allow to generate an irreducible co-domain for every computational operative domain used. Then, computational information usually lost by using classic LTR computational approach only, based on the traditional noise-affected data model stochastic representation (with high-level perturbation computational model under either additive or multiplicative perturbation hypothesis), can be captured and fully recovered to arbitrary precision, by a corresponding complementary co-domain, step-by-step. In previous paper, we already saw that CICT can supply us with Optimized Exponential Cyclic numeric Sequences (OECS) co-domain perfectly tuned to low-level multiplicative noise source generators, related to experimental high-level overall perturbation. Now, associated OECS co-domain polynomially structured information can be used to evaluate any computed result at arbitrary scale, and to compensate for achieving multi-scale computational information conservation.