Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning

TL;DR

This paper introduces a deep generative adversarial network (GAN) framework for fast, patho-realistic ultrasound image simulation, specifically applied to intravascular ultrasound, improving realism and computational efficiency.

Contribution

The novel multi-stage GAN approach significantly enhances ultrasound image realism and speed compared to traditional wave space simulation methods.

Findings

Simulated images are indistinguishable from real in visual Turing tests.

The method accurately replicates tissue-specific speckle profiles.

The approach is computationally efficient for practical use.

Abstract

Ultrasound imaging makes use of backscattering of waves during their interaction with scatterers present in biological tissues. Simulation of synthetic ultrasound images is a challenging problem on account of inability to accurately model various factors of which some include intra-/inter scanline interference, transducer to surface coupling, artifacts on transducer elements, inhomogeneous shadowing and nonlinear attenuation. Current approaches typically solve wave space equations making them computationally expensive and slow to operate. We propose a generative adversarial network (GAN) inspired approach for fast simulation of patho-realistic ultrasound images. We apply the framework to intravascular ultrasound (IVUS) simulation. A stage 0 simulation performed using pseudo B-mode ultrasound image simulator yields speckle mapping of a digitally defined phantom. The stage I GAN subsequently refines them to preserve tissue specific speckle intensities. The stage II GAN further refines them to generate high resolution images with patho-realistic speckle profiles. We evaluate patho-realism of simulated images with a visual Turing test indicating an equivocal confusion in discriminating simulated from real. We also quantify the shift in tissue specific intensity distributions of the real and simulated images to prove their similarity.