用物理模型生成逼真超声图像，助力术中导航与术前影像融合
DiffUS: Differentiable Ultrasound Rendering from Volumetric Imaging
- 基于深度学习将MRI转为声阻抗体积，再用射线追踪模拟波传播
- 生成带斑点噪声和深度衰减的超声图像，与真实数据高度一致
- 全程可微分，适合用于图像配准与三维重建等下游任务
术中超声成像能实时指导手术，但受噪声、伪影及与高分辨率术前MRI/CT对齐困难影响。为弥合术前规划与术中引导的差距，本文提出DiffUS，一种基于物理的可微分超声渲染器，可从体素化影像合成真实的B模式超声图像。DiffUS首先通过机器学习将3D MRI扫描转换为声阻抗体积；随后利用带有耦合反射-透射方程的射线追踪模拟超声波传播，将波传播建模为稀疏线性系统以捕捉多重内部反射；最后通过扇形采集几何下的深度分辨回波提取重建B模式图像，并包含真实存在的斑点噪声与深度相关退化。DiffUS完全在PyTorch中实现为可微张量运算，支持梯度优化，适用于切片-体积分组和体积分重建等下游应用。在ReMIND数据集上的评估表明，DiffUS能够从脑部MRI数据生成解剖准确的超声图像。
原文摘要 · Abstract (English)
Intraoperative ultrasound imaging provides real-time guidance during numerous surgical procedures, but its interpretation is complicated by noise, artifacts, and poor alignment with high-resolution preoperative MRI/CT scans. To bridge the gap between reoperative planning and intraoperative guidance, we present DiffUS, a physics-based, differentiable ultrasound renderer that synthesizes realistic B-mode images from volumetric imaging. DiffUS first converts MRI 3D scans into acoustic impedance volumes using a machine learning approach. Next, we simulate ultrasound beam propagation using ray tracing with coupled reflection-transmission equations. DiffUS formulates wave propagation as a sparse linear system that captures multiple internal reflections. Finally, we reconstruct B-mode images via depth-resolved echo extraction across fan-shaped acquisition geometry, incorporating realistic artifacts including speckle noise and depth-dependent degradation. DiffUS is entirely implemented as differentiable tensor operations in PyTorch, enabling gradient-based optimization for downstream applications such as slice-to-volume registration and volumetric reconstruction. Evaluation on the ReMIND dataset demonstrates DiffUS's ability to generate anatomically accurate ultrasound images from brain MRI data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。