{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T17:42:40Z","timestamp":1793295760357,"version":"4.5.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:00:00Z","timestamp":1772841600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002655","name":"Terry Fox Foundation","doi-asserted-by":"publisher","award":["1083"],"award-info":[{"award-number":["1083"]}],"id":[{"id":"10.13039\/501100002655","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002655","name":"Terry Fox Foundation","doi-asserted-by":"publisher","award":["1083"],"award-info":[{"award-number":["1083"]}],"id":[{"id":"10.13039\/501100002655","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012273","name":"Lotte and John Hecht Memorial Foundation","doi-asserted-by":"publisher","award":["1083"],"award-info":[{"award-number":["1083"]}],"id":[{"id":"10.13039\/100012273","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012273","name":"Lotte and John Hecht Memorial Foundation","doi-asserted-by":"publisher","award":["1083"],"award-info":[{"award-number":["1083"]}],"id":[{"id":"10.13039\/100012273","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2024-06265"],"award-info":[{"award-number":["RGPIN-2024-06265"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ontario Ministry of Colleges and Universities","award":["ER19-15-307"],"award-info":[{"award-number":["ER19-15-307"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Quantitative ultrasound (QUS) methods can derive insightful biomarkers from raw radiofrequency (RF) signals for tissue characterization and monitoring, but their clinical adoption is limited by the inaccessibility and storage burden of RF data. This study is the first to investigate the potential of deep generative models in synthesizing RF data from standard B-mode images and evaluate their efficacy in downstream QUS analysis. Three conditional generative adversarial networks (cGAN), namely Pix2Pix, a shallow ViT\u2010based cGAN, and a deep ViT\u2010based cGAN, were adapted and trained on a large paired dataset of RF\/B\u2010mode frames (21,174 training, 3,456 validation, 8,919 test frames) collected from 152 patients (98 patients in the training, 16 in validation, and 38 in the test set) with suspicious breast lesions. The synthesized RF data were assessed using sample-level evaluation metrics, and via a benign-malignant lesion classification task based on the corresponding QUS features. The generative models achieved a structural similarity index measure (SSIM) of 0.82\u2009\u00b1\u20090.05 on the synthetic RF data and an average peak signal-to-noise ratio (PSNR) of about 33 dB on the corresponding B-mode images, confirming strong reconstruction fidelity. In the lesion classification experiments, a classifier trained on a selected subset of six QUS features derived from the original RF data achieved a test accuracy of 82\u2009\u00b1\u20096%. In training and testing the classifier with the same subset of QUS features derived from the synthetic RF data, the deep ViT cGAN matched the original model\u2019s performance (accuracy\u2009=\u200982\u2009\u00b1\u20096%), outperforming the Pix2Pix and shallow ViT cGANs. When the feature selection and classifier training and testing were exclusively performed on the synthetic QUS parameters, the Deep ViT cGAN (accuracy\u2009=\u200981\u2009\u00b1\u20097%) and Pix2Pix cGAN (accuracy\u2009=\u200981\u2009\u00b1\u20096%) demonstrated competitive performance, while the Shallow ViT remained slightly lower (accuracy\u2009=\u200979\u2009\u00b1\u20096%). The promising results obtained in this study demonstrate the feasibility of RF data synthesis from B\u2010mode images, and therefore, is a step forward towards QUS\u2010based tissue characterization without the necessity of direct access to RF data.<\/jats:p>","DOI":"10.1038\/s41598-026-43319-9","type":"journal-article","created":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T15:54:51Z","timestamp":1772898891000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-enabled RF data synthesis for breast ultrasound: efficacy in quantitative ultrasound tissue characterization"],"prefix":"10.1038","volume":"16","author":[{"given":"Nasrin","family":"Sheibani-Asl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurentius O.","family":"Osapoetra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory J.","family":"Czarnota","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Sadeghi-Naini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,7]]},"reference":[{"key":"43319_CR1","unstructured":"Global cancer burden growing. amidst mounting need for services, (n.d.). https:\/\/www.who.int\/news\/item\/01-02-2024-global-cancer-burden-growing--amidst-mounting-need-for-services Accessed June 25, 2024."},{"key":"43319_CR2","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1259\/bjr\/77245199","volume":"84","author":"CF Loughran","year":"2011","unstructured":"Loughran, C. F. & Keeling, C. R. Seeding of tumour cells following breast biopsy: a literature review. Br. J. Radiol. 84, 869\u2013874. https:\/\/doi.org\/10.1259\/bjr\/77245199 (2011).","journal-title":"Br. J. Radiol."},{"key":"43319_CR3","doi-asserted-by":"publisher","first-page":"807","DOI":"10.7863\/jum.2007.26.6.807","volume":"26","author":"H Zhi","year":"2007","unstructured":"Zhi, H. et al. Comparison of Ultrasound Elastography, Mammography, and Sonography in the Diagnosis of Solid Breast Lesions. J. Ultrasound Med. 26, 807\u2013815. https:\/\/doi.org\/10.7863\/jum.2007.26.6.807 (2007).","journal-title":"J. Ultrasound Med."},{"key":"43319_CR4","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.3390\/diagnostics13061105","volume":"13","author":"A Ohashi","year":"2023","unstructured":"Ohashi, A. et al. Comparison of Ultrafast Dynamic Contrast-Enhanced (DCE) MRI with Conventional DCE MRI in the Morphological Assessment of Malignant Breast Lesions. Diagnostics 13, 1105. https:\/\/doi.org\/10.3390\/diagnostics13061105 (2023).","journal-title":"Diagnostics"},{"key":"43319_CR5","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1053\/j.seminoncol.2010.11.006","volume":"38","author":"EJ Feleppa","year":"2011","unstructured":"Feleppa, E. J., Mamou, J., Porter, C. R. & Machi, J. Quantitative Ultrasound in Cancer Imaging. Semin Oncol. 38, 136\u2013150. https:\/\/doi.org\/10.1053\/j.seminoncol.2010.11.006 (2011).","journal-title":"Semin Oncol."},{"key":"43319_CR6","doi-asserted-by":"crossref","unstructured":"Mamou, J. & Oelze, M. L. Quantitative Ultrasound in Soft Tissues (Springer, 2023).","DOI":"10.1007\/978-3-031-21987-0"},{"key":"43319_CR7","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1121\/1.389241","volume":"73","author":"FL Lizzi","year":"1983","unstructured":"Lizzi, F. L., Greenebaum, M., Feleppa, E. J., Elbaum, M. & Coleman, D. J. Theoretical framework for spectrum analysis in ultrasonic tissue characterization. J. Acoust. Soc. Am. 73, 1366\u20131373. https:\/\/doi.org\/10.1121\/1.389241 (1983).","journal-title":"J. Acoust. Soc. Am."},{"key":"43319_CR8","doi-asserted-by":"publisher","unstructured":"Laugier, P. & Ha\u00efat, G. (eds) Bone Quantitative Ultrasound (Springer Netherlands, 2011). https:\/\/doi.org\/10.1007\/978-94-007-0017-8","DOI":"10.1007\/978-94-007-0017-8"},{"key":"43319_CR9","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1359\/jbmr.1997.12.8.1280","volume":"12","author":"C Gl\u00fcer","year":"1997","unstructured":"Gl\u00fcer, C. & Group, I. Q. U. C. Quantitative ultrasound techniques for the assessment of osteoporosis: expert agreement on current status. J. Bone Miner. Res. 12, 1280\u20131288 (1997).","journal-title":"J. Bone Miner. Res."},{"key":"43319_CR10","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/S11547-024-01786-Y","volume":"129","author":"A Rocca","year":"2024","unstructured":"Rocca, A. et al. Quantitative ultrasound (QUS) in the evaluation of liver steatosis: data reliability in different respiratory phases and body positions. Radiol. Med. 129, 549. https:\/\/doi.org\/10.1007\/S11547-024-01786-Y (2024).","journal-title":"Radiol. Med."},{"key":"43319_CR11","first-page":"4437","volume":"13","author":"D Sharma","year":"2021","unstructured":"Sharma, D. et al. Quantitative ultrasound characterization of therapy response in prostate cancer in vivo. Am. J. Transl Res. 13, 4437 (2021).","journal-title":"Am. J. Transl Res."},{"key":"43319_CR12","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1016\/J.ULTRASMEDBIO.2018.02.014","volume":"44","author":"D Rohrbach","year":"2018","unstructured":"Rohrbach, D., Wodlinger, B., Wen, J., Mamou, J. & Feleppa, E. High-Frequency Quantitative Ultrasound for Imaging Prostate Cancer Using a Novel Micro-Ultrasound Scanner. Ultrasound Med. Biol. 44, 1341\u20131354. https:\/\/doi.org\/10.1016\/J.ULTRASMEDBIO.2018.02.014 (2018).","journal-title":"Ultrasound Med. Biol."},{"key":"43319_CR13","doi-asserted-by":"publisher","first-page":"627","DOI":"10.3389\/FENDO.2021.627698\/BIBTEX","volume":"12","author":"PN Goundan","year":"2021","unstructured":"Goundan, P. N. et al. A Preliminary Study of Quantitative Ultrasound for Cancer-Risk Assessment of Thyroid Nodules. Front. Endocrinol. (Lausanne). 12, 627\u2013698. https:\/\/doi.org\/10.3389\/FENDO.2021.627698\/BIBTEX (2021).","journal-title":"Front. Endocrinol. (Lausanne)"},{"key":"43319_CR14","doi-asserted-by":"publisher","first-page":"082901","DOI":"10.1118\/1.4812683","volume":"40","author":"A Sadeghi-Naini","year":"2013","unstructured":"Sadeghi-Naini, A. et al. Low-frequency quantitative ultrasound imaging of cell death in vivo. Med. Phys. 40, 082901. https:\/\/doi.org\/10.1118\/1.4812683 (2013).","journal-title":"Med. Phys."},{"key":"43319_CR15","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1593\/tlo.12385","volume":"6 (3)","author":"A Sadeghi-Naini","year":"2013","unstructured":"Sadeghi-Naini, A. et al. Conventional Frequency Ultrasonic Biomarkers of Cancer Treatment Response In Vivo. Transl Oncol. 6, 234\u2013IN2. https:\/\/doi.org\/10.1593\/tlo.12385 (2013).","journal-title":"Transl Oncol."},{"key":"43319_CR16","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1158\/1078-0432.CCR-12-2965","volume":"19","author":"A Sadeghi-Naini","year":"2013","unstructured":"Sadeghi-Naini, A. et al. Quantitative Ultrasound Evaluation of Tumor Cell Death Response in Locally Advanced Breast Cancer Patients Receiving Chemotherapy. Clin. Cancer Res. 19, 2163\u20132174. https:\/\/doi.org\/10.1158\/1078-0432.CCR-12-2965 (2013).","journal-title":"Clin. Cancer Res."},{"key":"43319_CR17","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.media.2014.11.009","volume":"20","author":"L Sannachi","year":"2015","unstructured":"Sannachi, L. et al. Non-invasive evaluation of breast cancer response to chemotherapy using quantitative ultrasonic backscatter parameters. Med. Image Anal. 20, 224\u2013236 (2015).","journal-title":"Med. Image Anal."},{"key":"43319_CR18","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/s0301-5629(97)00013-6","volume":"23","author":"T Noritomi","year":"1997","unstructured":"Noritomi, T. et al. Carotid plaque typing by multiple-parameter ultrasonic tissue characterization. Ultrasound Med. Biol. 23, 643\u2013650 (1997).","journal-title":"Ultrasound Med. Biol."},{"key":"43319_CR19","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1016\/J.ULTRASMEDBIO.2022.10.018","volume":"49","author":"C Hoerig","year":"2023","unstructured":"Hoerig, C., Wallace, K., Wu, M. & Mamou, J. Classification of Metastatic Lymph Nodes In Vivo Using Quantitative Ultrasound at Clinical Frequencies. Ultrasound Med. Biol. 49, 787\u2013801. https:\/\/doi.org\/10.1016\/J.ULTRASMEDBIO.2022.10.018 (2023).","journal-title":"Ultrasound Med. Biol."},{"key":"43319_CR20","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1109\/tmi.2004.826953","volume":"23","author":"ML Oelze","year":"2004","unstructured":"Oelze, M. L., O\u2019Brien, W. D., Blue, J. P. & Zachary, J. F. Differentiation and characterization of rat mammary fibroadenomas and 4T1 mouse carcinomas using quantitative ultrasound imaging. IEEE Trans. Med. Imaging. 23, 764\u2013771 (2004).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"43319_CR21","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1016\/j.ultrasmedbio.2006.05.006","volume":"32","author":"ML Oelze","year":"2006","unstructured":"Oelze, M. L. & Zachary, J. F. Examination of cancer in mouse models using high-frequency quantitative ultrasound. Ultrasound Med. Biol. 32, 1639\u20131648. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2006.05.006 (2006).","journal-title":"Ultrasound Med. Biol."},{"key":"43319_CR22","doi-asserted-by":"publisher","first-page":"2198","DOI":"10.1118\/1.3566064","volume":"38","author":"Y Liao","year":"2011","unstructured":"Liao, Y. et al. Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature\u2010based and Nakagami\u2010parameter images. Med. Phys. 38, 2198\u20132207. https:\/\/doi.org\/10.1118\/1.3566064 (2011).","journal-title":"Med. Phys."},{"key":"43319_CR23","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.1109\/TMI.2012.2206398","volume":"31","author":"W Gomez","year":"2012","unstructured":"Gomez, W., Pereira, W. C. A. & Infantosi, A. F. C. Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound. IEEE Trans. Med. Imaging. 31, 1889\u20131899. https:\/\/doi.org\/10.1109\/TMI.2012.2206398 (2012).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"43319_CR24","doi-asserted-by":"publisher","unstructured":"Sadeghi-Naini, A. et al. Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps. Sci. Rep. 7, 7963. https:\/\/doi.org\/10.1038\/s41598-017-13977-x (2017).","DOI":"10.1038\/s41598-017-13977-x"},{"key":"43319_CR25","doi-asserted-by":"publisher","unstructured":"Osapoetra, L. O. et al. Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods. Transl Oncol. 13 (10), 100827. https:\/\/doi.org\/10.1016\/j.tranon.2020.100827 (2020).","DOI":"10.1016\/j.tranon.2020.100827"},{"key":"43319_CR26","doi-asserted-by":"publisher","unstructured":"Sadeghi-Naini, A. et al. Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities. Sci. Rep. 7, 10352. https:\/\/doi.org\/10.1038\/s41598-017-09678-0 (2017).","DOI":"10.1038\/s41598-017-09678-0"},{"key":"43319_CR27","doi-asserted-by":"publisher","first-page":"3497","DOI":"10.18632\/oncotarget.1950","volume":"5","author":"A Sadeghi-Naini","year":"2014","unstructured":"Sadeghi-Naini, A. et al. Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture. Oncotarget 5, 3497 (2014).","journal-title":"Oncotarget"},{"key":"43319_CR28","doi-asserted-by":"publisher","unstructured":"Taleghamar, H., Moghadas-Dastjerdi, H., Czarnota, G. J. & Sadeghi-Naini, A. Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment. Sci. Rep. 11, 14865. https:\/\/doi.org\/10.1038\/s41598-021-94004-y (2021).","DOI":"10.1038\/s41598-021-94004-y"},{"key":"43319_CR29","doi-asserted-by":"publisher","first-page":"2244","DOI":"10.1038\/s41598-022-06100-2","volume":"12","author":"H Taleghamar","year":"2022","unstructured":"Taleghamar, H., Jalalifar, S. A., Czarnota, G. J. & Sadeghi-Naini, A. Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy. Sci. Rep. 12, 2244. https:\/\/doi.org\/10.1038\/s41598-022-06100-2 (2022).","journal-title":"Sci. Rep."},{"key":"43319_CR30","unstructured":"Goodfellow, I. J. et al. Generative Adversarial Networks. in Proceedings of the 27th International Conference on Neural Information Processing Systems 2, 2672-2680. https:\/\/dl.acm.org\/doi\/10.5555\/2969033.2969125 (2014)."},{"key":"43319_CR31","doi-asserted-by":"publisher","unstructured":"Kingma, D. P. & Welling, M. An Introduction to Variational Autoencoders. Found. Trends Mach. Learn. 12 (4), 307\u2013392. https:\/\/doi.org\/10.1561\/2200000056 (2019).","DOI":"10.1561\/2200000056"},{"key":"43319_CR32","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. in Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18 234\u2013241 (Springer, 2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"43319_CR33","doi-asserted-by":"publisher","first-page":"3753","DOI":"10.1038\/s41598-020-60520-6","volume":"10","author":"Q Yang","year":"2020","unstructured":"Yang, Q. et al. MRI cross-modality image-to-image translation. Sci. Rep. 10, 3753 (2020).","journal-title":"Sci. Rep."},{"key":"43319_CR34","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1002\/acm2.13121","volume":"22","author":"T Wang","year":"2021","unstructured":"Wang, T. et al. A review on medical imaging synthesis using deep learning and its clinical applications. J. Appl. Clin. Med. Phys. 22, 11\u201336. https:\/\/doi.org\/10.1002\/acm2.13121 (2021).","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"43319_CR35","doi-asserted-by":"publisher","first-page":"2085","DOI":"10.1109\/TMI.2016.2549918","volume":"35","author":"K Bahrami","year":"2016","unstructured":"Bahrami, K. et al. Reconstruction of 7T-Like Images From 3T MRI. IEEE Trans. Med. Imaging. 35, 2085\u20132097. https:\/\/doi.org\/10.1109\/TMI.2016.2549918 (2016).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"43319_CR36","doi-asserted-by":"publisher","first-page":"110247","DOI":"10.1016\/j.mri.2024.110247","volume":"115","author":"M Becker","year":"2025","unstructured":"Becker, M. et al. Deep learning corrects artifacts in RASER MRI profiles. Magn. Reson. Imaging. 115, 110247. https:\/\/doi.org\/10.1016\/j.mri.2024.110247 (2025).","journal-title":"Magn. Reson. Imaging"},{"key":"43319_CR37","doi-asserted-by":"publisher","unstructured":"Shin, H. C. et al. Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks. in Proceedings of Third International Workshop on Simulation and Synthesis in Medical Imaging 1\u201311 (2018). https:\/\/doi.org\/10.1007\/978-3-030-00536-8_1","DOI":"10.1007\/978-3-030-00536-8_1"},{"key":"43319_CR38","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3390\/jimaging9040081","volume":"9","author":"A Kebaili","year":"2023","unstructured":"Kebaili, A., Lapuyade-Lahorgue, J. & Ruan, S. Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review. J. Imaging. 9, 81. https:\/\/doi.org\/10.3390\/jimaging9040081 (2023).","journal-title":"J. Imaging"},{"key":"43319_CR39","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J. Y., Zhou, T. & Efros, A. A. Image-to-image translation with conditional adversarial networks. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1125\u20131134 (2017). https:\/\/doi.ieeecomputersociety.org\/10.1109\/CVPR.2017.632","DOI":"10.1109\/CVPR.2017.632"},{"key":"43319_CR40","doi-asserted-by":"publisher","unstructured":"Zhu, J. Y., Park, T., Isola, P. & Efros, A. A. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. in Proceedings of the IEEE International Conference on Computer Vision 2242-2251 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.244","DOI":"10.1109\/ICCV.2017.244"},{"key":"43319_CR41","doi-asserted-by":"publisher","first-page":"2598","DOI":"10.1109\/TMI.2022.3167808","volume":"41","author":"O Dalmaz","year":"2022","unstructured":"Dalmaz, O., Yurt, M. & Cukur, T. ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis. IEEE Trans. Med. Imaging. 41, 2598\u20132614. https:\/\/doi.org\/10.1109\/TMI.2022.3167808 (2022).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"43319_CR42","doi-asserted-by":"publisher","unstructured":"Kamran, S. A., Hossain, K. F., Tavakkoli, A., Zuckerbrod, S. L. & Baker, S. A. VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers. in Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops 3235-3245 (2021). https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00362","DOI":"10.1109\/ICCVW54120.2021.00362"},{"key":"43319_CR43","doi-asserted-by":"publisher","unstructured":"Wang, X. et al. Conditional Diffusion Model for Abdominal CT Image Synthesis. in 22nd International Symposium on Biomedical Imaging (ISBI) 1\u20135 (IEEE, 2025). https:\/\/doi.org\/10.1109\/ISBI60581.2025.10980773","DOI":"10.1109\/ISBI60581.2025.10980773"},{"key":"43319_CR44","doi-asserted-by":"publisher","first-page":"12098","DOI":"10.1038\/s41598-023-39278-0","volume":"13","author":"G M\u00fcller-Franzes","year":"2023","unstructured":"M\u00fcller-Franzes, G. et al. A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis. Sci. Rep. 13, 12098. https:\/\/doi.org\/10.1038\/s41598-023-39278-0 (2023).","journal-title":"Sci. Rep."},{"key":"43319_CR45","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/s00330-024-11005-x","volume":"35","author":"K Rangarajan","year":"2024","unstructured":"Rangarajan, K. et al. Simulation training in mammography with AI-generated images: a multireader study. Eur. Radiol. 35, 562\u2013571. https:\/\/doi.org\/10.1007\/s00330-024-11005-x (2024).","journal-title":"Eur. Radiol."},{"key":"43319_CR46","unstructured":"Ren, J. From RF signals to B-mode images using deep learning, Master\u2019s degree (KTH Royal Institute of Technology, 2018)."},{"key":"43319_CR47","doi-asserted-by":"publisher","first-page":"2808","DOI":"10.3390\/math12182808","volume":"12","author":"HM Rai","year":"2024","unstructured":"Rai, H. M., Dashkevych, S., Yoo, J. & Diagnostics, N. G. The Impact of Synthetic Data Generation on the Detection of Breast Cancer from Ultrasound Imaging. Mathematics 12, 2808. https:\/\/doi.org\/10.3390\/math12182808 (2024).","journal-title":"Mathematics"},{"key":"43319_CR48","doi-asserted-by":"publisher","first-page":"2308","DOI":"10.1121\/1.1452743","volume":"111","author":"ML Oelze","year":"2002","unstructured":"Oelze, M. L. & O\u2019Brien, W. D. Frequency-dependent attenuation-compensation functions for ultrasonic signals backscattered from random media. J. Acoust. Soc. Am. 111, 2308\u20132319. https:\/\/doi.org\/10.1121\/1.1452743 (2002).","journal-title":"J. Acoust. Soc. Am."},{"key":"43319_CR49","doi-asserted-by":"publisher","first-page":"3232","DOI":"10.1121\/1.3483739","volume":"128","author":"Y Labyed","year":"2010","unstructured":"Labyed, Y. & Bigelow, T. A. Estimating the total ultrasound attenuation along the propagation path by using a reference phantom. J. Acoust. Soc. Am. 128, 3232\u20133238. https:\/\/doi.org\/10.1121\/1.3483739 (2010).","journal-title":"J. Acoust. Soc. Am."},{"key":"43319_CR50","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1177\/016173469001200104","volume":"12","author":"G Berger","year":"1990","unstructured":"Berger, G., Laugier, P., Thalabard, J. C. & Perrin, J. Global Breast Attenuation: Control Group and Benign Breast Diseases. Ultrason. Imaging. 12, 47\u201357. https:\/\/doi.org\/10.1177\/016173469001200104 (1990).","journal-title":"Ultrason. Imaging"},{"key":"43319_CR51","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1118\/1.1897463","volume":"32","author":"N Duric","year":"2005","unstructured":"Duric, N. et al. Development of ultrasound tomography for breast imaging: Technical assessment. Med. Phys. 32, 1375\u20131386. https:\/\/doi.org\/10.1118\/1.1897463 (2005).","journal-title":"Med. Phys."},{"key":"43319_CR52","doi-asserted-by":"publisher","first-page":"45094","DOI":"10.18632\/oncotarget.8862","volume":"7","author":"H Tadayyon","year":"2016","unstructured":"Tadayyon, H. et al. Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach. Oncotarget 7, 45094\u201345111. https:\/\/doi.org\/10.18632\/oncotarget.8862 (2016).","journal-title":"Oncotarget"},{"key":"43319_CR53","doi-asserted-by":"publisher","first-page":"12903","DOI":"10.1118\/1.4852875","volume":"41","author":"H Tadayyon","year":"2014","unstructured":"Tadayyon, H., Sadeghi-Naini, A., Wirtzfeld, L., Wright, F. C. & Czarnota, G. Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties. Med. Phys. 41, 12903\u201312904. https:\/\/doi.org\/10.1118\/1.4852875 (2014).","journal-title":"Med. Phys."},{"key":"43319_CR54","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"SMC-3","author":"RM Haralick","year":"1973","unstructured":"Haralick, R. M., Shanmugam, K. & Dinstein, I. Textural Features for Image Classification. IEEE Trans. Syst. Man. Cybern SMC. -3, 610\u2013621. https:\/\/doi.org\/10.1109\/TSMC.1973.4309314 (1973).","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"43319_CR55","unstructured":"Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12 2825-2830 (2011)."},{"key":"43319_CR56","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","volume":"3","author":"L McInnes","year":"2018","unstructured":"McInnes, L., Healy, J., Saul, N. & Gro\u00dfberger, L. Uniform Manifold Approximation and Projection. J. Open. Source Softw. 3, 861. https:\/\/doi.org\/10.21105\/joss.00861 (2018).","journal-title":"J. Open. Source Softw."},{"key":"43319_CR57","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H. & Hastie, T. Regularization and Variable Selection Via the Elastic Net. J. R Stat. Soc. Ser. B Stat. Methodol. 67, 301\u2013320. https:\/\/doi.org\/10.1111\/j.1467-9868.2005.00503.x (2005).","journal-title":"J. R Stat. Soc. Ser. B Stat. Methodol."},{"key":"43319_CR58","doi-asserted-by":"publisher","unstructured":"Dong, C., Loy, C. C., He, K. & Tang, X. Image Super-Resolution Using Deep Convolutional Networks. IEEE Trans. Pattern Anal. Mach. Intell. 38 (2), 295-307. https:\/\/doi.org\/10.1109\/TPAMI.2015.2439281 (2016).","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"43319_CR59","doi-asserted-by":"publisher","unstructured":"Yang, Q. et al. MRI cross-modality neuroimage-to-neuroimage translation. Sci. Rep. 10, 3753. https:\/\/doi.org\/10.1038\/s41598-020-60520-6 (2018).","DOI":"10.1038\/s41598-020-60520-6"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-43319-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-43319-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-43319-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T19:34:48Z","timestamp":1781638488000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-43319-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,7]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["43319"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-43319-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,7]]},"assertion":[{"value":"25 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"A.S.N. and G.J.C. are inventors of patents on \u201cSystem and method for classifying and characterizing tissues using first-order and second order statistics of quantitative ultrasound parametric maps\u201d, \u201cSystems and methods for prediction of tumour response to chemotherapy using pre-treatment quantitative ultrasound parameters\u201d, \u201cSystems and methods for characterizing intra-tumor regions on quantitative ultrasound parametric images to predict cancer response to chemotherapy at pre-treatment\u201d, and \u201cDeep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy\u201d.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"12544"}}