This study proposes a Hessian-inversion-free ray-born inversion approach for biomedical ultrasound tomography. The proposed approach is a more efficient version of the ray-born inversion approach proposed in [3]. Using these approaches, the propagation of acoustic waves are modelled using a ray approximation to heterogeneous Green's function. The inverse problem is solved in the frequency domain by iteratively linearisation and minimisation of the objective function from low to high frequencies. In [3], the linear subproblem associated with each frequency interval is solved by an implicit and iterative inversion of the Hessian matrix (inner iterations). Instead, this study applies a preconditioning approach on each linear subproblem so that the Hessian matrix becomes diagonalised, and can thus be inverted in a single step. Using the proposed preconditioning approach, the computational cost of solving each linear subproblem of the proposed ray-Born inversion approach becomes almost the same as solving one linear subproblem associated with a radon-type time-of-flight-based approach using bent rays. More importantly, the smoothness assumptions made for diagonalising the Hessian matrix make the image reconstruction more stable than the inversion approach in [3] to noise.
翻译:本研究提出了一种声学生物医学成像中无Hessian矩阵求逆的射线Borne反演方法。该方法是[3]中提出的射线Borne反演方法的更高效版本。通过将声波传播建模为杂波绿函数的射线逼近,该方法通过从低到高频逐次线性化目标函数,并最小化来解决频率域内的逆问题。在[3]中,与每个频率间隔相关的线性子问题通过隐式和迭代求解Hessian矩阵(内部迭代)来解决。相反,本研究对每个线性子问题应用一种预处理方法,使得Hessian矩阵变成了对角线矩阵,因此可以在一步中得到求逆。使用所提出的预处理方法,解决所提出的射线Borne反演方法的每个线性子问题的计算成本几乎与使用弯曲射线的Radon型飞行时间方法中与线性子问题相关的一个线性子问题的解决成本相同。更重要的是,为对角化Hessian矩阵所做的平滑性假设使图像重建比[3]中的反演方法对噪声更加稳定。