  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.
