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This paper is in the following e-collection/theme issue:

Medical imaging (205) Theoretical Frameworks and Concepts (594) Advanced Data Analytics in eHealth (384) Imaging Informatics (511) Computer-Aided Diagnosis (445) AI Applications in Biomedical Engineering (44)

Published on in Vol 9 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58911, first published .
Doctor performing abdominal ultrasound on a senior man

Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss

Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss

Authors of this article:

Shreeram Athreya1 Author Orcid Image ;   Ashwath Radhachandran2 Author Orcid Image ;   Vedrana Ivezić3 Author Orcid Image ;   Vivek R Sant4 Author Orcid Image ;   Corey W Arnold1, 2, 3, 5, 6 Author Orcid Image ;   William Speier2, 3, 5 Author Orcid Image
Article Authors Cited by (14) Tweetations (2) Metrics

Journals

  1. Zuo W, Bai J, Wei J, Long C. Fishbone ingestion is a non-negligible cause of intestinal perforation. Frontiers in Medicine 2025;12 View
  2. Du S, Shen M, Liu Y, Wei F, Lei Y. Development and evaluation of a novel organ-specific biomechanics-guided contrast-enhanced CT volume synthesis model*. Physics in Medicine & Biology 2025;70(21):215018 View
  3. Liu L, Chen L, Wang A. Innovative paths of CycleGAN loss functions and evolution of cross-domain applications. ITM Web of Conferences 2025;80:01014 View
  4. Malagila Raphael Y, Monkam P, Qi S, Liu S, He D, Qian W. Deep generative models in echocardiography: a scoping review of trends and future perspectives. Measurement Science and Technology 2026;37(4):042001 View
  5. Giourga M, Petropoulos I, Stavros S, Potiris A, Goula K, Moustakli E, Papahliou A, Daskalaki M, Segou M, Rodolakis A, Daskalakis G, Domali E. A Deep Learning Approach for Classifying Benign, Malignant, and Borderline Ovarian Tumors Using Convolutional Neural Networks and Generative Adversarial Networks. Medical Sciences 2026;14(1):89 View
  6. Kawattikul K, Hamamoto K. Toward Realistic Synthetic Ultrasound Images With Pose Vectors. IEEE Access 2026;14:42132 View
  7. Muñoz B, Pezoa R, Gutierrez H. HistoXplaining in Style: Counterfactual Explanations for Histopathology Images using StyleGAN2-ADA. Information Systems Frontiers 2026 View
  8. Hayashi C, Mori S, Mori Y, Taehyeung L, Suyari H, Ishikawa H. Development of an unpaired deep neural network for synthesizing X-ray fluoroscopic images from digitally reconstructed tomography in image guided radiotherapy. British Journal of Radiology 2026 View
  9. Dietrich C, Blaivas M, Cantisani V, Cui X, Dong Y, Kaffas A, Goudie A, Graumann O, Herzog M, Karlas T, Merkel D, Möller K, Nielsen M, Nolsoe C, Snelling P, Wastl D, Jenssen C. Handheld Ultrasound: Technical Review and International Expert Consensus Recommendations. Diagnostics 2026;16(19):3215 View
  10. Mitarai S, Taniguchi M, Tateuchi H, Nakao M. Focus Depth Conversion of Muscle Ultrasound Images via Diffusion Models With Pseudo-Paired Training. IEEE Access 2026;14:149672 View

Books/Policy Documents

  1. Ge G, Pressman B, Dawkins A, Zhang J. Quality and Patient Safety in Medical Imaging. View
  2. Maji C, Kumar R, Gupta U, Jadon A, Gugnani P. Proceedings of International Conference on Artificial Intelligence and Networks. View

Conference Proceedings

  1. Spairani E, Bosco E, Podda F, Ravera A, Damiano A, Magenes G, Matrone G. 2026 IEEE International Symposium on Artificial Intelligence for Instrumentation and Measurement (AI4IM). CycleGAN-based cross-domain adaptation for echocardiographic segmentation across ultrasound systems View
  2. Fernandes L, Santos J, Lau N. 2026 IEEE 31st International Conference on Emerging Technologies and Factory Automation (ETFA). Evaluation of Unpaired Image-to-Image Translation for Rare Defect Augmentation in Industrial Visual Inspection View