PA-SFM：无需跟踪器的可微声辐射用于自由手3D光声成像
PA-SFM: Tracker-free differentiable acoustic radiation for freehand 3D photoacoustic imaging
AI总结：
本文提出PA-SFM，通过可微声辐射建模实现无需外部传感器的自由手3D光声成像，实现了高精度3D重建和低成本解决方案。
摘要：
三维（3D）手持光声断层成像通常依赖于笨重且昂贵的外部定位传感器来校正运动伪影，这严重限制了其临床灵活性和可及性。为解决这一挑战，我们提出了PA-SFM，一种无需跟踪器的框架，仅利用单模态光声数据进行传感器姿态恢复和高保真3D重建。与基于视觉特征的传统结构从运动（SFM）方法不同，PA-SFM将声波方程整合到可微编程管道中。通过利用高性能的GPU加速声辐射核，该框架通过梯度下降同时优化3D光声源分布和传感器阵列姿态。为确保自由手场景下的稳健收敛，我们引入了粗到细的优化策略，结合几何一致性检查和刚体约束以消除运动异常值。通过数值模拟和活体大鼠实验验证了所提方法。结果表明，PA-SFM实现了亚毫米定位精度，并恢复了与地面真实基准相当的高分辨率3D血管结构，提供了一种低成本、软件定义的临床自由手光声成像解决方案。源代码可在https://github.com/JaegerCQ/PA-SFM公开获取。
英文摘要：
Three-dimensional (3D) handheld photoacoustic tomography typically relies on bulky and expensive external positioning sensors to correct motion artifacts, which severely limits its clinical flexibility and accessibility. To address this challenge, we present PA-SFM, a tracker-free framework that leverages exclusively single-modality photoacoustic data for both sensor pose recovery and high-fidelity 3D reconstruction via differentiable acoustic radiation modeling. Unlike traditional structure-from-motion (SFM) methods based on visual features, PA-SFM integrates the acoustic wave equation into a differentiable programming pipeline. By leveraging a high-performance, GPU-accelerated acoustic radiation kernel, the framework simultaneously optimizes the 3D photoacoustic source distribution and the sensor array pose via gradient descent. To ensure robust convergence in freehand scenarios, we introduce a coarse-to-fine optimization strategy that incorporates geometric consistency checks and rigid-body constraints to eliminate motion outliers. We validated the proposed method through both numerical simulations and in-vivo rat experiments. The results demonstrate that PA-SFM achieves sub-millimeter positioning accuracy and restores high-resolution 3D vascular structures comparable to ground-truth benchmarks, offering a low-cost, software-defined solution for clinical freehand photoacoustic imaging. The source code is publicly available at \href{https://github.com/JaegerCQ/PA-SFM}{https://github.com/JaegerCQ/PA-SFM}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
