Sastry Karteekeya, Aborahama Yousuf, Luo Yilin, Zhang Yang, Cui Manxiu, Cao Rui, Ku Geng, Wang Lihong V
IEEE Trans Med Imaging. 2025 May;44(5):2068-2078. doi: 10.1109/TMI.2025.3526000. Epub 2025 May 2.
Photoacoustic tomography holds tremendous potential for neuroimaging due to its functional magnetic resonance imaging (fMRI)-like functional contrast and greater specificity, richer contrast, portability, open platform, faster imaging, magnet-free and quieter operation, and lower cost. However, accounting for the skull-induced acoustic distortion remains a long-standing challenge due to the problem size. This is aggravated in functional imaging, where high accuracy is needed to detect minuscule functional changes. Here, we develop an acoustic solver based on the boundary-element method (BEM) to model the skull and de-aberrate the images. BEM uses boundary meshes and compression for superior computational efficiency compared to volumetric discretization-based methods. We demonstrate BEM's higher accuracy and favorable scalability relative to the widely used pseudo-spectral time-domain method (PSTD). In imaging through an ex-vivo adult human skull, BEM outperforms PSTD in several metrics. Our work establishes BEM as a valuable and naturally suited technique in photoacoustic tomography and lays the foundation for BEM-based de-aberration methods.
由于具有类似功能磁共振成像（fMRI）的功能对比度、更高的特异性、更丰富的对比度、便携性、开放平台、更快的成像速度、无磁且运行安静以及成本更低等优势，光声断层扫描在神经成像方面具有巨大潜力。然而，由于问题规模，考虑颅骨引起的声学畸变仍然是一个长期存在的挑战。在功能成像中，这一问题更加突出，因为需要高精度来检测微小的功能变化。在此，我们开发了一种基于边界元法（BEM）的声学求解器，用于对颅骨进行建模并对图像进行去畸变处理。与基于体积离散化的方法相比，BEM使用边界网格和压缩技术以实现更高的计算效率。我们证明，相对于广泛使用的伪谱时域方法（PSTD），BEM具有更高的精度和良好的可扩展性。在通过离体成人人类颅骨进行成像时，BEM在多个指标上优于PSTD。我们的工作确立了BEM作为光声断层扫描中一种有价值且自然适用的技术，并为基于BEM的去畸变方法奠定了基础。
