Synthesizing Real-Time Ultrasound Images of Muscle Based on Biomechanical Simulation and Conditional Diffusion Network

Abstract

Quantitative muscle function analysis based on ultrasound imaging, has been used for various applications, particularly with recent development of deep learning methods. The nature of speckle noises in ultrasound images poses challenges to accurate and reliable data annotation for supervised learning algorithms. To obtain a large and reliable dataset without manual scanning and labeling, we proposed a synthesizing pipeline to provide synthetic ultrasound datasets of muscle movement with an accurate ground truth, allowing augmenting, training, and evaluating models for different tasks. Our pipeline contained biomechanical simulation using a finite-element method (FEM), an algorithm for reconstructing sparse fascicles, and a diffusion network for ultrasound image generation. With the adjustment of a few parameters, the proposed pipeline can generate a large dataset of real-time ultrasound images with diversity in morphology and pattern. With 3030 ultrasound images generated, we qualitatively and quantitatively verified that the synthetic images closely matched with the in vivo images. In addition, we applied the synthetic dataset to different tasks of muscle analysis. Compared to trained on an unaugmented dataset, a model trained on synthetic one had better cross-dataset performance, which demonstrates the feasibility of synthesizing pipeline to augment model training and avoid overfitting. The results of the regression task show potentials under the conditions that the number of datasets or the accurate label is limited. The proposed synthesizing pipeline can not only be used for muscle-related study but also for other similar study and model development, where sequential images are needed for training.