  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.
