Foam-like phantoms for comparing tomography algorithms

Abstract

Tomographic algorithms are often compared by evaluating them on certain benchmark datasets. For fair comparison, these datasets should ideally (i) be challenging to reconstruct, (ii) be representative of typical tomographic experiments, (iii) be flexible to allow for different acquisition modes, and (iv) include enough samples to allow for comparison of data-driven algorithms. Current approaches often satisfy only some of these requirements, but not all. For example, real-world datasets are typically challenging and representative of a category of experimental examples, but are restricted to the acquisition mode that was used in the experiment and are often limited in the number of samples. Mathematical phantoms are often flexible and can sometimes produce enough samples for data-driven approaches, but can be relatively easy to reconstruct and are often not representative of typical scanned objects. In this paper, we present a family of foam-like mathematical phantoms that aims to satisfy all four requirements simultaneously. The phantoms consist of foam-like structures with more than 100000 features, making them challenging to reconstruct and representative of common tomography samples. Because the phantoms are computer-generated, varying acquisition modes and experimental conditions can be simulated. An effectively unlimited number of random variations of the phantoms can be generated, making them suitable for data-driven approaches. We give a formal mathematical definition of the foam-like phantoms, and explain how they can be generated and used in virtual tomographic experiments in a computationally efficient way. In addition, several 4D extensions of the 3D phantoms are given, enabling comparisons of algorithms for dynamic tomography. Finally, example phantoms and tomographic datasets are given, showing that the phantoms can be effectively used to make fair and informative comparisons between tomography algorithms.

tomoCAM: fast model-based iterative reconstruction via GPU acceleration and non-uniform fast Fourier transforms

“…In the first experiment, we compare the reconstruction of a foam phantom from all three codes. A foam phantom and its projection data of size (128 Â 16 Â 2048) was generated using the foam_ct_phantom package (Pelt et al, 2022). A full slice from the phantom in Fig.…”

Is it feasible to identify outputs of an arbitrary process at run time without excessively slowing down workflows?

“…3. Another Python script that runs a scientific analysis similar to a known study [28]. It processes 15 .npy data sets of size 1.1 MB, yielding a JupyterNotebook for each.…”