SlingBAG Pro: Accelerating point cloud-based iterative reconstruction for 3D photoacoustic imaging with arbitrary array geometries

2026-01-02

arXiv.org

AI Summary

This work addresses the high computational complexity, substantial memory consumption, and slow reconstruction speed of conventional iterative algorithms when applied to photoacoustic imaging with irregular transducer array geometries. To overcome these limitations, we propose a fast three-dimensional photoacoustic reconstruction method based on a point-cloud iterative framework. For the first time, this framework is extended to arbitrary array geometries and enhanced with zero-gradient filtering and a progressive temporal sampling strategy. These innovations, combined with hierarchical optimization, significantly accelerate convergence and reduce redundant computations. Experimental results demonstrate that the proposed method achieves up to a 2.2-fold speedup over the SlingBAG algorithm while maintaining high image quality and reducing the required number of transducers.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

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Abstract

High-quality three-dimensional (3D) photoacoustic imaging (PAI) is gaining increasing attention in clinical applications. To address the challenges of limited space and high costs, irregular geometric transducer arrays that conform to specific imaging regions are promising for achieving high-quality 3D PAI with fewer transducers. However, traditional iterative reconstruction algorithms struggle with irregular array configurations, suffering from high computational complexity, substantial memory requirements, and lengthy reconstruction times. In this work, we introduce SlingBAG Pro, an advanced reconstruction algorithm based on the point cloud iteration concept of the Sliding ball adaptive growth (SlingBAG) method, while extending its compatibility to arbitrary array geometries. SlingBAG Pro maintains high reconstruction quality, reduces the number of required transducers, and employs a hierarchical optimization strategy that combines zero-gradient filtering with progressively increased temporal sampling rates during iteration. This strategy rapidly removes redundant spatial point clouds, accelerates convergence, and significantly shortens overall reconstruction time. Compared to the original SlingBAG algorithm, SlingBAG Pro achieves up to a 2.2-fold speed improvement in point cloud-based 3D PA reconstruction under irregular array geometries. The proposed method is validated through both simulation and in vivo mouse experiments, and the source code is publicly available at https://github.com/JaegerCQ/SlingBAG_Pro.

Problem

• photoacoustic imaging
• iterative reconstruction
• irregular array geometries
• point cloud
• 3D imaging

Innovation

• point cloud iteration
• arbitrary array geometries
• hierarchical optimization
• photoacoustic imaging
• iterative reconstruction