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

arXiv: https://arxiv.org/abs/2112.02896 · HTML: https://mlsanity.com/paper/2112.02896/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 18 of 18)

- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound](https://mlsanity.com/paper/1811.08252/) (2018)
- [Unpaired Deep Learning for Accelerated MRI using Optimal Transport Driven CycleGAN](https://mlsanity.com/paper/2008.12967/) (2020)
- [Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography](https://mlsanity.com/paper/1806.09748/) (2018)
- [Wasserstein Style Transfer](https://mlsanity.com/paper/1905.12828/) (2019)
- [Adaptive Ultrasound Beamforming using Deep Learning](https://mlsanity.com/paper/1909.10342/) (2019)
- [Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound](https://mlsanity.com/paper/1907.10257/) (2019)
- [CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry](https://mlsanity.com/paper/1908.09414/) (2019)
- [Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning](https://mlsanity.com/paper/1712.06096/) (2017)
- [Continuous Conversion of CT Kernel using Switchable CycleGAN with AdaIN](https://mlsanity.com/paper/2011.13150/) (2020)
- [Towards CT-quality Ultrasound Imaging using Deep Learning](https://mlsanity.com/paper/1710.06304/) (2017)
- [Normalization of breast MRIs using Cycle-Consistent Generative Adversarial Networks](https://mlsanity.com/paper/1912.08061/) (2019)
- [Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data](https://mlsanity.com/paper/2008.01362/) (2020)
- [Deep Learning-based Universal Beamformer for Ultrasound Imaging](https://mlsanity.com/paper/1904.02843/) (2019)
- [Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks](https://mlsanity.com/paper/1904.09974/) (2019)
- [Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain](https://mlsanity.com/paper/2103.09022/) (2021)

## Abstract (arXiv)

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.