4D SlingBAG: spatial-temporal coupled Gaussian ball for large-scale dynamic 3D photoacoustic iterative reconstruction
Journal:
arXiv
Published Date:
Dec 5, 2024
Abstract
Large-scale dynamic three-dimensional (3D) photoacoustic imaging (PAI) is
significantly important in clinical applications. In practical implementations,
large-scale 3D real-time PAI systems typically utilize sparse two-dimensional
(2D) sensor arrays with certain angular deficiencies, necessitating advanced
iterative reconstruction (IR) algorithms to achieve quantitative PAI and reduce
reconstruction artifacts. However, for existing IR algorithms, multi-frame 3D
reconstruction leads to extremely high memory consumption and prolonged
computation time, with limited consideration of the spatial-temporal continuity
between data frames. Here, we propose a novel method, named the 4D sliding
Gaussian ball adaptive growth (4D SlingBAG) algorithm, based on the current
point cloud-based IR algorithm sliding Gaussian ball adaptive growth
(SlingBAG), which has minimal memory consumption among IR methods. Our 4D
SlingBAG method applies spatial-temporal coupled deformation functions to each
Gaussian sphere in point cloud, thus explicitly learning the deformations
features of the dynamic 3D PA scene. This allows for the efficient
representation of various physiological processes (such as pulsation) or
external pressures (e.g., blood perfusion experiments) contributing to changes
in vessel morphology and blood flow during dynamic 3D PAI, enabling highly
efficient IR for dynamic 3D PAI. Simulation experiments demonstrate that 4D
SlingBAG achieves high-quality dynamic 3D PA reconstruction. Compared to
performing reconstructions by using SlingBAG algorithm individually for each
frame, our method significantly reduces computational time and keeps a
extremely low memory consumption. The project for 4D SlingBAG can be found in
the following GitHub repository:
\href{https://github.com/JaegerCQ/4D-SlingBAG}{https://github.com/JaegerCQ/4D-SlingBAG}.
