Ultrasound Image Enhancement using CycleGAN and Perceptual Loss
Shreeram Athreya Ashwath Radhachandran Vedrana Ivezić Vivek Sant Corey W. Arnold William Speier
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.