Ultrasound is well-established as an imaging modality for diagnostic and
interventional purposes. However, the image quality varies with operator skills
as acquiring and interpreting ultrasound images requires extensive training due
to the imaging artefacts, the range of acquisition parameters and the
variability of patient anatomies. Automating the image acquisition task could
improve acquisition reproducibility and quality but training such an algorithm
requires large amounts of navigation data, not saved in routine examinations.
Thus, we propose a method to generate large amounts of ultrasound images from
other modalities and from arbitrary positions, such that this pipeline can
later be used by learning algorithms for navigation. We present a novel
simulation pipeline which uses segmentations from other modalities, an
optimized volumetric data representation and GPU-accelerated Monte Carlo path
tracing to generate view-dependent and patient-specific ultrasound images. We
extensively validate the correctness of our pipeline with a phantom experiment,
where structures' sizes, contrast and speckle noise properties are assessed.
Furthermore, we demonstrate its usability to train neural networks for
navigation in an echocardiography view classification experiment by generating
synthetic images from more than 1000 patients. Networks pre-trained with our
simulations achieve significantly superior performance in settings where large
real datasets are not available, especially for under-represented classes. The
proposed approach allows for fast and accurate patient-specific ultrasound
image generation, and its usability for training networks for
navigation-related tasks is demonstrated.

通过模拟管道生成大量的超声图像，以供导航相关的网络训练使用，从而实现快速准确的患者特异性超声图像生成并展示该方法在网络导航相关任务中的可用性。

自主超声导航的心脏超声模拟

Cardiac ultrasound simulation for autonomous ultrasound navigation

Gradient-based algorithms are crucial to modern computer-vision and graphics
applications, enabling learning-based optimization and inverse problems. For
example, photorealistic differentiable rendering pipelines for color images
have been proven highly valuable to applications aiming to map 2D and 3D
domains. However, to the best of our knowledge, no effort has been made so far
towards extending these gradient-based methods to the generation of depth
(2.5D) images, as simulating structured-light depth sensors implies solving
complex light transport and stereo-matching problems. In this paper, we
introduce a novel end-to-end differentiable simulation pipeline for the
generation of realistic 2.5D scans, built on physics-based 3D rendering and
custom block-matching algorithms. Each module can be differentiated w.r.t
sensor and scene parameters; e.g., to automatically tune the simulation for new
devices over some provided scans or to leverage the pipeline as a 3D-to-2.5D
transformer within larger computer-vision applications. Applied to the training
of deep-learning methods for various depth-based recognition tasks
(classification, pose estimation, semantic segmentation), our simulation
greatly improves the performance of the resulting models on real scans, thereby
demonstrating the fidelity and value of its synthetic depth data compared to
previous static simulations and learning-based domain adaptation schemes.

本论文提出了一种基于可微仿真管道和方块匹配算法的端到端可微深度图像生成模型，可以用于各种深度感知的任务，如分类、姿态估计和语义分割，可比之前使用的静态仿真和学习方法提高模型性能，赋予所生成图像更高的真实感。