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.
Highlights
In tomographic imaging, an image of the interior of a scanned object is obtained by combining measurements of some form of penetrating wave passing through the object
Tomographic datasets can be computed for the proposed phantoms for a wide variety of experimental conditions and acquisition modes and with data sizes that are common in real-world experiments, making the approach both flexible and representative
We introduced a family of foam-like phantoms for comparing the performance of tomography algorithms
Summary
An image of the interior of a scanned object is obtained by combining measurements of some form of penetrating wave passing through the object. One advantage of using such mathematical phantoms is that the true object is readily available, allowing one to compute accuracy metrics with respect to an objective ground truth Another advantage is that this approach is flexible: since the tomographic experiment is performed virtually, different acquisition modes and experimental conditions can be simulated. Tomographic datasets can be computed for the proposed phantoms for a wide variety of experimental conditions and acquisition modes and with data sizes that are common in real-world experiments, making the approach both flexible and representative.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have