Realistic Ultrasound Simulation of Complex Surface Models Using Interactive Monte‐Carlo Path Tracing

Abstract

Ray‐based simulations have been shown to generate impressively realistic ultrasound images in interactive frame rates. Recent efforts used GPU‐based surface raytracing to simulate complex ultrasound interactions such as multiple reflections and refractions. These methods are restricted to perfectly specular reflections (i.e. following only a single reflective/refractive ray), whereas real tissue exhibits roughness of varying degree at tissue interfaces, causing partly diffuse reflections and refractions. Such surface interactions are significantly more complex and can in general not be handled by conventional deterministic raytracing approaches. However, these can be efficiently computed by Monte‐Carlo sampling techniques, where many ray paths are generated with respect to a probability distribution. In this paper, we introduce Monte‐Carlo raytracing for ultrasound simulation. This enables the realistic simulation of ultrasound‐tissue interactions such as soft shadows and fuzzy reflections. We discuss how to properly weight the contribution of each ray path in order to simulate the behaviour of a beamformed ultrasound signal. Tracing many individual rays per transducer element is easily parallelizable on modern GPUs, as opposed to previous approaches based on recursive binary raytracing. We further propose a significant performance optimization based on adaptive sampling.

Scatgan for Reconstruction of Ultrasound Scatterers Using Generative Adversarial Networks

“…It performs well on synthetic data, and generalizes to in vivo data with relative ease, while remaining orders of magnitude faster at inference time than the state-of-the-art optimization-based alternative [6]. Our fast runtime would make an extension to 3D feasible, which will then enable the determination of realistic input scatter maps for ray-based training simulations [3].…”

“…a spatially-varying Point-Spread Function (PSF), the convolution of which, with a particular tissue representation, then yields the desired image. Ray-based methods [3] can, in addition, compute the incident acoustic power by tracing the complex reflections and refractions which the wavefront undergoes, during large-scale interactions with geometrical boundaries of anatomical structures.…”

Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation

“…Nevertheless, it may be possible to separate the direction-dependent image content prior to capturing only scattering effects. One can then simulate such directional wave interactions at a later time using ray tracing techniques [7], [8]. For instance, reflection boundaries could be detected and removed using a simple phase symmetry (PS) algorithm [38], which is designed to estimate directional reflections at tissue boundaries such as bone surfaces.…”

“…Restoration of this granular pattern is not only important for visual realism in a simulation scenario, but also for preserving structural and diagnostic information about the tissue. Ray-based methods [7], [8] simulate the propagation of ultrasonic wavefront as rays using computed graphics techniques, which allows to simulate interactions such as refractions and reflections, while simulating speckle using a PSF convolution with a texture representing scatterers. Using stochastic and sophisticated interaction models with Monte-Carlo ray sampling, this was shown in [8] to lead to impressively realistic US images even at real-time framerates.…”

“…A simple Gaussian-parametrized model was fit to the inverse-problem reconstructed scatterers in [19]. This method was demonstrated for homogeneous tissues that other instances of the same tissue can be obtained with the found model for simulating new images, which were however reported as lack of visual variety of real tissues in [8].…”