Toward Smart Ultrasound Image Augmentation to Advance Tumor Treatment Monitoring: Exploring the Potential of Diffusion Generative Model

Abstract

Medical imaging is a crucial tool in clinics to monitor tumor treatment progress. In practice, many imaging tools (such as magnetic resonance imaging (MRI) and computed tomography (CT) scans) are in general costly and may also expose patients to radiation, leading to potential side effects. Recent studies have demonstrated that ultrasound imaging, which is safe, low-cost, and easy to access, can monitor the drug delivery progress in solid tumors. However, the noisy nature of ultrasound images and the high-level uncertainty of cancer disease progression are still challenging in ultrasound-based tumor treatment monitoring. To overcome these barriers, this work presents a comparative study to explore the potential advantages of the emerging diffusion generative models against the commonly applied state-of-the-art generative models. Namely, the denoising diffusion models (DDMs), against the generative adversarial networks (GAN), and variational auto-encoders (VAE), are used for analyzing the ultrasound images through image augmentation. These models are evaluated based on their capacity to augment ultrasound images for exploring the potential variations of tumor treatment monitoring. The results across different cases indicate that the denoising diffusion implicit models (DDIM)/kernel inception distance (KID)-inception score (IS) model leveraged in this work outperforms the other models in the study in terms of similarity, diversity, and predictive accuracy. Therefore, further investigation of such diffusion generative models could be considered as they can potentially serve as a great predictive tool for ultrasound image-enabled tumor treatment monitoring in the future.