Luo Yilin, Huang Hsuan-Kai, Sastry Karteekeya, Hu Peng, Tong Xin, Kuo Joseph, Aborahama Yousuf, Na Shuai, Villa Umberto, Anastasio Mark A, Wang Lihong V
IEEE Trans Med Imaging. 2025 Feb;44(2):645-655. doi: 10.1109/TMI.2024.3456595. Epub 2025 Feb 4.
Transcranial photoacoustic computed tomography presents challenges in human brain imaging due to skull-induced acoustic aberration. Existing full-wave image reconstruction methods rely on a unified elastic wave equa- tion for skull shear and longitudinal wave propagation, therefore demanding substantial computational resources. We propose an efficient discrete imaging model based on finite element discretization. The elastic wave equation for solids is solely applied to the hard-tissue skull region, while the soft-tissue or coupling-medium region that dominates the simulation domain is modeled with the simpler acoustic wave equation for liquids. The solid-liquid interfaces are explicitly modeled with elastic-acoustic coupling. Furthermore, finite element discretization allows coarser, irregular meshes to conform to object geometry. These factors significantly reduce the linear system size by 20 times to facilitate accurate whole-brain simulations with improved speed. We derive a matched forward-adjoint operator pair based on the model to enable integration with various optimization algorithms. We validate the reconstruction framework through numerical simulations and phantom experiments.
由于颅骨引起的声学像差，经颅光声计算机断层扫描在人脑成像中面临挑战。现有的全波图像重建方法依赖于用于颅骨剪切波和纵波传播的统一弹性波方程，因此需要大量的计算资源。我们提出了一种基于有限元离散化的高效离散成像模型。固体的弹性波方程仅应用于硬组织颅骨区域，而在模拟域中占主导地位的软组织或耦合介质区域则用更简单的液体声波方程进行建模。固液界面通过弹性 - 声学耦合进行显式建模。此外，有限元离散化允许使用更粗、不规则的网格以符合物体几何形状。这些因素显著将线性系统规模减小了20倍，以便在提高速度的同时促进准确的全脑模拟。我们基于该模型推导了一对匹配的前向 - 伴随算子，以实现与各种优化算法的集成。我们通过数值模拟和体模实验验证了重建框架。
