Ultrasound imaging tasks such as calibration, inverse parameter estimation, and acquisition design require models that are physically grounded, efficient, and differentiable with respect to meaningful material and system parameters. While full-wave solvers offer high fidelity, they are often too expensive for iterative optimization, and existing ray-based methods have mostly been limited to forward simulation. In this work, we present a fully differentiable end-to-end ultrasound simulation framework based on full-path Monte Carlo ray tracing. Building on UltraRay, the method propagates gradients from image-space losses back through acoustic transport, beamforming, and post-processing, enabling gradient-based optimization over scene and acquisition parameters. The framework combines differentiable ray transport in Mitsuba 3/Dr.Jit with a custom differentiable bridge through the ultrasound image-formation pipeline. Forward examples reproduce expected geometric image features and capture more complex anatomical structures. In inverse problems, the method recovers known parameters in a simulated-reference setting and identifies effective parameters that improve agreement between simulated and experimental B-mode images in a simulation-to-real setting. Finite-difference comparisons further support the consistency of the computed gradients. Overall, this work provides a practical foundation for differentiable, physics-based ultrasound simulation and optimization.

翻译：超声成像任务如校准、逆参数估计和数据采集设计需要具备物理基础、高效且对有意义材料和系统参数可微的模型。全波求解器虽精度高，但因计算成本过高难以用于迭代优化，而现有基于射线的方法多局限于正向模拟。本研究提出一个基于全路径蒙特卡洛光线追踪的全可微分端到端超声模拟框架。该方法以UltraRay为基础，将图像空间中的损失梯度反向传播至声学传输、波束形成和后处理环节，从而实现对场景与采集参数的梯度优化。该框架将Mitsuba 3/Dr.Jit中的可微射线传输与定制化的可微超声图像生成流程桥接。正向示例复现了预期的几何图像特征，并能捕获更复杂的解剖结构。在逆问题中，该方法在模拟参考场景下可恢复已知参数，并在仿真到现实的对比中识别出有效参数以提升模拟与实验B模式图像的一致性。有限差分验证进一步支持了所计算梯度的准确性。总体而言，本研究为基于可微物理的超声模拟与优化提供了实用基础。