mlsanityData to 7 Oct 2026

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

arXiv 2112.02896 ↗First posted 6 Dec 2021eess.IV cs.CV cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 50 references that are in the map. First posted on arXiv 6 Dec 2021.

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18 of 50 references.

References · 18

  1. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  2. 2016Least Squares Generative Adversarial Networkscited by 1,000
  3. 2018Computational Optimal Transportcited by 765
  4. 2018Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasoundcited by 43
  5. 2020Unpaired Deep Learning for Accelerated MRI using Optimal Transport Driven CycleGANcited by 28
  6. 2018Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiographycited by 23
  7. 2019Wasserstein Style Transfercited by 21
  8. 2019Adaptive Ultrasound Beamforming using Deep Learningcited by 17
  9. 2019Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasoundcited by 12
  10. 2019CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometrycited by 11
  11. 2017Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learningcited by 8
  12. 2020Continuous Conversion of CT Kernel using Switchable CycleGAN with AdaINcited by 8
6 more
  1. 2017Towards CT-quality Ultrasound Imaging using Deep Learningcited by 7
  2. 2019Normalization of breast MRIs using Cycle-Consistent Generative Adversarial Networkscited by 7
  3. 2020Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Datacited by 7
  4. 2019Deep Learning-based Universal Beamformer for Ultrasound Imagingcited by 4
  5. 2019Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networkscited by 3
  6. 2021Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domaincited by 3

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