Physics-informed 4D X-ray image reconstruction from ultra-sparse spatiotemporal data
Journal:
arXiv
Published Date:
Apr 4, 2025
Abstract
The unprecedented X-ray flux density provided by modern X-ray sources offers
new spatiotemporal possibilities for X-ray imaging of fast dynamic processes.
Approaches to exploit such possibilities often result in either i) a limited
number of projections or spatial information due to limited scanning speed, as
in time-resolved tomography, or ii) a limited number of time points, as in
stroboscopic imaging, making the reconstruction problem ill-posed and unlikely
to be solved by classical reconstruction approaches. 4D reconstruction from
such data requires sample priors, which can be included via deep learning (DL).
State-of-the-art 4D reconstruction methods for X-ray imaging combine the power
of AI and the physics of X-ray propagation to tackle the challenge of sparse
views. However, most approaches do not constrain the physics of the studied
process, i.e., a full physical model. Here we present 4D physics-informed
optimized neural implicit X-ray imaging (4D-PIONIX), a novel physics-informed
4D X-ray image reconstruction method combining the full physical model and a
state-of-the-art DL-based reconstruction method for 4D X-ray imaging from
sparse views. We demonstrate and evaluate the potential of our approach by
retrieving 4D information from ultra-sparse spatiotemporal acquisitions of
simulated binary droplet collisions, a relevant fluid dynamic process. We
envision that this work will open new spatiotemporal possibilities for various
4D X-ray imaging modalities, such as time-resolved X-ray tomography and more
novel sparse acquisition approaches like X-ray multi-projection imaging, which
will pave the way for investigations of various rapid 4D dynamics, such as
fluid dynamics and composite testing.
