Ultrasound Image Enhancement using CycleGAN and Perceptual Loss

TL;DR

This paper introduces a CycleGAN-based framework with perceptual loss for enhancing ultrasound images, especially from portable devices, effectively improving image quality and structural preservation without requiring registered image pairs.

Contribution

The work presents a novel CycleGAN model with perceptual loss tailored for ultrasound enhancement, capable of handling non-registered image pairs and improving image quality across multiple organ systems.

Findings

Achieved a SSI score of 0.722 indicating good structural similarity.

Attained a LNCC score of 0.902 demonstrating high correlation.

Reached a PSNR of 28.802 showing improved image fidelity.

Abstract

Purpose: The objective of this work is to introduce an advanced framework designed to enhance ultrasound images, especially those captured by portable hand-held devices, which often produce lower quality images due to hardware constraints. Additionally, this framework is uniquely capable of effectively handling non-registered input ultrasound image pairs, addressing a common challenge in medical imaging. Materials and Methods: In this retrospective study, we utilized an enhanced generative adversarial network (CycleGAN) model for ultrasound image enhancement across five organ systems. Perceptual loss, derived from deep features of pretrained neural networks, is applied to ensure the human-perceptual quality of the enhanced images. These images are compared with paired images acquired from high resolution devices to demonstrate the model's ability to generate realistic high-quality images across organ systems. Results: Preliminary validation of the framework reveals promising performance metrics. The model generates images that result in a Structural Similarity Index (SSI) score of 0.722, Locally Normalized Cross-Correlation (LNCC) score of 0.902 and 28.802 for the Peak Signal-to-Noise Ratio (PSNR) metric. Conclusion: This work presents a significant advancement in medical imaging through the development of a CycleGAN model enhanced with Perceptual Loss (PL), effectively bridging the quality gap between ultrasound images from varied devices. By training on paired images, the model not only improves image quality but also ensures the preservation of vital anatomic structural content. This approach may improve equity in access to healthcare by enhancing portable device capabilities, although further validation and optimizations are necessary for broader clinical application.