Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN

Abstract

In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US imaging system can visualize a volume along three axial planes. This allows for a full view of the anatomy, which is useful for gynecological (GYN) and obstetrical (OB) applications. Unfortunately, the 3-D US has an inherent limitation in resolution compared to the 2-D US. In the case of 3-D US with a 3-D mechanical probe, for example, the image quality is comparable along the beam direction, but significant deterioration in image quality is often observed in the other two axial image planes. To address this, here we propose a novel unsupervised deep learning approach to improve 3-D US image quality. In particular, using unmatched high-quality 2-D US images as a reference, we trained a recently proposed switchable CycleGAN architecture so that every mapping plane in 3-D US can learn the image quality of 2-D US images. Thanks to the switchable architecture, our network can also provide real-time control of image enhancement level based on user preference, which is ideal for a user-centric scanner setup. Extensive experiments with clinical evaluation confirm that our method offers significantly improved image quality as well user-friendly flexibility.

Ultrasound Signal Processing: From Models to Deep Learning

“…Gasse et al (2017) explore mapping a single PW image to an image that was compounded using multiple PWs. Zhou 2021) present a self-supervised method based on the cycle-GAN architecture, originally developed for unpaired (cycle-consistent) style transfer (Huh et al, 2021). This approach aims at transferring the features of a high-quality target distribution of images to a given low-quality image, which the authors leverage to improve elevational image quality in 3D ultrasound.…”