Solving Admissibility for the Spatial X-Ray Transform On the Two Element Field

TL;DR

This paper characterizes and enumerates admissible line complexes in finite vector spaces over rac{2}{n} and provides an algorithmic approach to study admissibility in higher dimensions, with applications to discrete tomography.

Contribution

It offers a complete structural description and enumeration of admissible complexes in rac{2}{4} and extends the methodology to higher dimensions, advancing understanding in discrete integral geometry.

Findings

Complete classification of admissible line complexes in rac{2}{4}

Enumeration of all admissible complexes in rac{2}{4} and rac{2}{5}

Algorithmic framework for higher-dimensional admissibility

Abstract

The admissibility problem in integral geometry asks for which collections of affine subspaces the Radon transform remains injective. In the discrete setting, this becomes a purely combinatorial question about recovering a function on a finite vector space from its sums over a prescribed family of affine subspaces. In this paper, we study the spatial X-ray transform (line transform) over the finite vector spaces $\mathbb{Z}_{2}^{n}$ and give a complete structural and enumerative description of admissible line complexes in $\mathbb{Z}_{2}^{4}$. We prove that any admissible line complex in $\mathbb{Z}_{2}^{4}$ can be obtained by taking a disjoint union of one or more odd cycles and attaching trees to the cycle vertices. Using this structural description, we carry out a systematic case-by-case enumeration of all admissible complexes in $\mathbb{Z}_{2}^{4}$ and derive an exact total count. We then generalize our approach to an algorithm that applies to $\mathbb{Z}_{2}^{n}$ for arbitrary $n$, and we then implement it to obtain the total number of admissible complexes in $\mathbb{Z}_{2}^{5}$. Our results extend previous small-dimensional classifications and provide an algorithmic framework for studying admissibility in higher dimensions. Beyond their intrinsic combinatorial interest, these structures model discrete sampling schemes for tomographic imaging, and they suggest further connections between admissibility, incidence matrices, and spectral properties of the associated graphs.