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

TL;DR

This paper introduces an unsupervised deep learning method using switchable CycleGAN to enhance 3-D ultrasound image quality, providing real-time adjustable improvements based on user preferences, validated through clinical experiments.

Contribution

The paper presents a novel unsupervised deep learning approach with switchable CycleGAN for real-time, user-controlled enhancement of 3-D ultrasound images using unmatched 2-D US references.

Findings

Significant improvement in 3-D US image quality.

Real-time control of image enhancement level.

Validated effectiveness through clinical evaluation.

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 {\em 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.