GPAIR: Gaussian-Kernel-Based Ultrafast 3D Photoacoustic Iterative Reconstruction

TL;DR

GPAIR introduces a GPU-accelerated, Gaussian-kernel-based iterative reconstruction method that achieves near-real-time 3D photoacoustic imaging, significantly reducing computation time for large-scale applications.

Contribution

The paper presents a novel Gaussian-kernel-based IR method with analytical pressure wave expressions and GPU acceleration, enabling ultrafast 3D PA reconstruction.

Findings

Achieves sub-second reconstruction speed for 8.4 million voxels

Demonstrates orders-of-magnitude acceleration over traditional IR methods

Enables near-real-time large-scale 3D photoacoustic imaging

Abstract

Although the iterative reconstruction (IR) algorithm can substantially correct reconstruction artifacts in photoacoustic (PA) computed tomography (PACT), it suffers from long reconstruction times, especially for large-scale three-dimensional (3D) imaging in which IR takes hundreds of seconds to hours. The computing burden severely limits the practical applicability of IR algorithms. In this work, we proposed an ultrafast IR method for 3D PACT, called Gaussian-kernel-based Ultrafast 3D Photoacoustic Iterative Reconstruction (GPAIR), which achieves orders-of-magnitude acceleration in computing. GPAIR transforms traditional spatial grids with continuous isotropic Gaussian kernels. By deriving analytical closed-form expression for pressure waves and implementing powerful GPU-accelerated differentiable Triton operators, GPAIR demonstrates extraordinary ultrafast sub-second reconstruction speed for 3D targets containing 8.4 million voxels in animal experiments. This revolutionary ultrafast image reconstruction enables near-real-time large-scale 3D PA reconstruction, significantly advancing 3D PACT toward clinical applications.