4D SlingBAG: spatial-temporal coupled Gaussian ball for large-scale dynamic 3D photoacoustic iterative reconstruction

TL;DR

The paper tackles the challenge of efficient, high-quality dynamic 3D photoacoustic imaging with sparse sensor geometries by extending the SlingBAG framework to 4D SlingBAG. It introduces a Gaussian-deformed Gaussian ball (GGBall) model that couples spatial and temporal deformation for each Gaussian PA source, allowing joint learning of baseline attributes and time-varying dynamics via a differentiable forward model. The approach yields over an order-unity speedup (more than 8x) for dynamic reconstructions with memory comparable to single-frame SlingBAG, demonstrated on simple and complex phantoms under sparse detectors. This work enables fast, large-scale dynamic PA imaging with potential clinical impact in hemodynamic and perfusion studies, while noting limitations related to acoustic inhomogeneities and attenuation to be addressed via spherical-harmonic speed modeling.

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}.

Figures (12)

• Figure 1: The Gaussian-deformed Gaussian ball model (GGBall).
• Figure 2: The overview framework of 4D SlingBAG pipeline.
• Figure 3: The dynamic 3D photoacoustic scene.
• Figure 4: The Top-View-MAP of the dynamic 3D photoacoustic reconstruction results using 4D SlingBAG.
• Figure 5: The reconstruction results of the dynamic 3D photoacoustic scene in the 1st frame. (a) XY Plane-MAP, YZ Plane-MAP, XZ Plane-MAP of the ground truth acoustic source. (b) XY Plane-MAP, YZ Plane-MAP, XZ Plane-MAP of the UBP reconstruction results using 1,024 sensor signals. (c) XY Plane-MAP, YZ Plane-MAP, XZ Plane-MAP of the 4D SlingBAG reconstruction results using 1,024 sensor signals. (Scale: 2.0 mm.)