{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:27:33Z","timestamp":1775071653734,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010905","name":"National Science Foundation of China | Major Research Plan","doi-asserted-by":"publisher","award":["2021ZD01111104"],"award-info":[{"award-number":["2021ZD01111104"]}],"id":[{"id":"10.13039\/501100010905","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010905","name":"National Science Foundation of China | Major Research Plan","doi-asserted-by":"publisher","award":["2021ZD01111104"],"award-info":[{"award-number":["2021ZD01111104"]}],"id":[{"id":"10.13039\/501100010905","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National High Level Hospital Clinical Research Funding of China"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our multicenter study develops a deep learning synthetic model with Convolutional-Transformer hybrid encoder-decoder architecture for generating thin-slice CT from thick-slice CT on a single center (1576 participants) and access the synthetic CT on three cross-regional centers (1228 participants). The qualitative image quality of synthetic and real thin-slice CT is comparable (<jats:italic>p<\/jats:italic>\u2009=\u20090.16). Four radiologists\u2019 accuracy in diagnosing community-acquired pneumonia using synthetic thin-slice CT surpasses thick-slice CT (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05), and matches real thin-slice CT (<jats:italic>p<\/jats:italic>\u2009&gt;\u20090.99). For lung nodule detection, sensitivity with thin-slice CT outperforms thick-slice CT (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) and comparable to real thin-slice CT (<jats:italic>p<\/jats:italic>\u2009&gt;\u20090.05). These findings indicate the potential of our model to generate high-quality synthetic thin-slice CT as a practical alternative when real thin-slice CT is preferred but unavailable.<\/jats:p>","DOI":"10.1038\/s41746-024-01338-8","type":"journal-article","created":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T19:17:46Z","timestamp":1732389466000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT"],"prefix":"10.1038","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4065-0377","authenticated-orcid":false,"given":"Pengxin","family":"Yu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9412-7584","authenticated-orcid":false,"given":"Haoyue","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dawei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Rongguo","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Mei","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Haoyu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiaoxu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Andrea S.","family":"Oh","sequence":"additional","affiliation":[]},{"given":"Fereidoun G.","family":"Abtin","sequence":"additional","affiliation":[]},{"given":"Ashley E.","family":"Prosper","sequence":"additional","affiliation":[]},{"given":"Kathleen","family":"Ruchalski","sequence":"additional","affiliation":[]},{"given":"Nana","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Huairong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ye","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xinna","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Shaohong","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Dasheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"John M.","family":"Hoffman","sequence":"additional","affiliation":[]},{"given":"Denise R.","family":"Aberle","sequence":"additional","affiliation":[]},{"given":"Chaoyang","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Shouliang","family":"Qi","sequence":"additional","affiliation":[]},{"given":"Corey","family":"Arnold","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,23]]},"reference":[{"key":"1338_CR1","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.2214\/ajr.181.4.1811101","volume":"181","author":"F Kodama","year":"2003","unstructured":"Kodama, F., Fultz, P. J. & Wandtke, J. C. Comparing thin-section and thick-section CT of pericardial sinuses and recesses. Am. J. Roentgenol. 181, 1101\u20131108 (2003).","journal-title":"Am. J. Roentgenol."},{"key":"1338_CR2","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.acra.2009.08.007","volume":"17","author":"DS Gierada","year":"2010","unstructured":"Gierada, D. S. et al. Effects of CT section thickness and reconstruction kernel on emphysema quantification: relationship to the magnitude of the CT emphysema index. Academic Radiol. 17, 146\u2013156 (2010).","journal-title":"Academic Radiol."},{"key":"1338_CR3","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s00068-018-1021-9","volume":"46","author":"L Guchlerner","year":"2020","unstructured":"Guchlerner, L. et al. Comparison of thick-and thin-slice images in thoracoabdominal trauma CT: a retrospective analysis. Eur. J. Trauma Emerg. Surg. 46, 187\u2013195 (2020).","journal-title":"Eur. J. Trauma Emerg. Surg."},{"key":"1338_CR4","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1148\/radiol.2017161659","volume":"284","author":"H MacMahon","year":"2017","unstructured":"MacMahon, H. et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology 284, 228\u2013243 (2017).","journal-title":"Radiology"},{"key":"1338_CR5","doi-asserted-by":"publisher","first-page":"101034","DOI":"10.1016\/j.eclinm.2021.101034","volume":"38","author":"G Frija","year":"2021","unstructured":"Frija, G. et al. How to improve access to medical imaging in low-and middle-income countries? EClinicalMedicine 38, 101034 (2021).","journal-title":"EClinicalMedicine"},{"key":"1338_CR6","doi-asserted-by":"publisher","first-page":"e136","DOI":"10.1016\/S1470-2045(20)30751-8","volume":"22","author":"H Hricak","year":"2021","unstructured":"Hricak, H. et al. Medical imaging and nuclear medicine: a Lancet Oncology Commission. Lancet Oncol. 22, e136\u2013e172 (2021).","journal-title":"Lancet Oncol."},{"key":"1338_CR7","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.118.010110","volume":"8","author":"JL Christensen","year":"2019","unstructured":"Christensen, J. L. et al. Impact of slice thickness on the predictive value of lung cancer screening computed tomography in the evaluation of coronary artery calcification. J. Am. Heart Assoc. 8, e010110 (2019).","journal-title":"J. Am. Heart Assoc."},{"key":"1338_CR8","doi-asserted-by":"publisher","first-page":"102444","DOI":"10.1016\/j.media.2022.102444","volume":"79","author":"X Chen","year":"2022","unstructured":"Chen, X. et al. Recent advances and clinical applications of deep learning in medical image analysis. Med. Image Anal. 79, 102444 (2022).","journal-title":"Med. Image Anal."},{"key":"1338_CR9","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1038\/d41586-023-03302-0","volume":"622","author":"M Lenharo","year":"2023","unstructured":"Lenharo, M. An AI revolution is brewing in medicine. What will it look like? Nature 622, 686\u2013688 (2023).","journal-title":"Nature"},{"key":"1338_CR10","first-page":"1030","volume":"37","author":"B Chen","year":"2024","unstructured":"Chen, B. & Wang, Y. Innovation in artificial intelligence medical regulatory and governance: thoughts on breaking through the current normative framework. Chin. Med. Ethics 37, 1030\u20131036 (2024).","journal-title":"Chin. Med. Ethics"},{"key":"1338_CR11","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1148\/radiol.2021203387","volume":"299","author":"S Park","year":"2021","unstructured":"Park, S. et al. Computer-aided detection of subsolid nodules at chest CT: improved performance with deep learning\u2013based CT section thickness reduction. Radiology 299, 211\u2013219 (2021).","journal-title":"Radiology"},{"key":"1338_CR12","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1136\/neurintsurg-2021-017842","volume":"14","author":"SPR Luijten","year":"2022","unstructured":"Luijten, S. P. R. et al. Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography. J. Neurointerventional Surg. 14, 794\u2013798 (2022).","journal-title":"J. Neurointerventional Surg."},{"key":"1338_CR13","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.clinimag.2023.05.019","volume":"101","author":"R Salman","year":"2023","unstructured":"Salman, R. et al. Diagnostic performance of artificial intelligence for pediatric pulmonary nodule detection in computed tomography of the chest. Clin. Imaging 101, 50\u201355 (2023).","journal-title":"Clin. Imaging"},{"key":"1338_CR14","doi-asserted-by":"publisher","first-page":"107290","DOI":"10.1016\/j.cmpb.2022.107290","volume":"229","author":"Q Guo","year":"2023","unstructured":"Guo, Q. et al. The gap in the thickness: estimating effectiveness of pulmonary nodule detection in thick-and thin-section CT images with 3D deep neural networks. Computer Methods Prog. Biomedicine 229, 107290 (2023).","journal-title":"Computer Methods Prog. Biomedicine"},{"key":"1338_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01119-3","volume":"7","author":"V Bellemo","year":"2024","unstructured":"Bellemo, V. et al. Optical coherence tomography choroidal enhancement using generative deep learning. NPJ Digital Med. 7, 115 (2024).","journal-title":"NPJ Digital Med."},{"key":"1338_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01018-7","volume":"7","author":"R Chen","year":"2024","unstructured":"Chen, R. et al. Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening. NPJ Digital Med. 7, 34 (2024).","journal-title":"NPJ Digital Med."},{"key":"1338_CR17","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230681","volume":"309","author":"J Lyu","year":"2023","unstructured":"Lyu, J. et al. Generative adversarial network\u2013based noncontrast CT angiography for aorta and carotid arteries. Radiology 309, e230681 (2023).","journal-title":"Radiology"},{"key":"1338_CR18","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230427","volume":"308","author":"LM Bischoff","year":"2023","unstructured":"Bischoff, L. M. et al. Deep learning super-resolution reconstruction for fast and motion-robust T2-weighted prostate MRI. Radiology 308, e230427 (2023).","journal-title":"Radiology"},{"key":"1338_CR19","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1148\/radiol.210551","volume":"303","author":"B Jiang","year":"2022","unstructured":"Jiang, B. et al. Deep learning reconstruction shows better lung nodule detection for ultra\u2013low-dose chest CT. Radiology 303, 202\u2013212 (2022).","journal-title":"Radiology"},{"key":"1338_CR20","doi-asserted-by":"publisher","first-page":"e784","DOI":"10.1016\/S2589-7500(21)00205-3","volume":"3","author":"CJ Preetha","year":"2021","unstructured":"Preetha, C. J. et al. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digital Health 3, e784\u2013e794 (2021).","journal-title":"Lancet Digital Health"},{"key":"1338_CR21","doi-asserted-by":"crossref","unstructured":"Ge, R. et al. Stereo-correlation and noise-distribution aware ResVoxGAN for dense slices reconstruction and noise reduction in thick low-dose CT. In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part VI 22 (pp. 328\u2013338) (Springer International Publishing, 2019).","DOI":"10.1007\/978-3-030-32226-7_37"},{"key":"1338_CR22","doi-asserted-by":"crossref","unstructured":"Peng, C., Lin, W. A., Liao, H., Chellappa, R. & Zhou, S. K. Saint: spatially aware interpolation network for medical slice synthesis. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 7750\u20137759) (IEEE, 2020).","DOI":"10.1109\/CVPR42600.2020.00777"},{"key":"1338_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Q. et al. Multi-stream progressive up-sampling network for dense CT image reconstruction. In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part VI 23 (pp. 518\u2013528) (Springer International Publishing, 2020).","DOI":"10.1007\/978-3-030-59725-2_50"},{"key":"1338_CR24","doi-asserted-by":"crossref","unstructured":"Yu, P. et al. RPLHR-CT dataset and transformer baseline for volumetric super-resolution from CT scans. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 344\u2013353) (Cham: Springer Nature Switzerland, 2022).","DOI":"10.1007\/978-3-031-16446-0_33"},{"key":"1338_CR25","doi-asserted-by":"publisher","first-page":"45","DOI":"10.3389\/fninf.2013.00045","volume":"7","author":"BC Lowekamp","year":"2013","unstructured":"Lowekamp, B. C. et al. The design of SimpleITK. Front. Neuroinformatics 7, 45 (2013).","journal-title":"Front. Neuroinformatics"},{"key":"1338_CR26","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","volume":"43","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Chen, J. & Hoi, S. C. Deep learning for image super-resolution: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 43, 3365\u20133387 (2020).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1338_CR27","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u2013612 (2004).","journal-title":"IEEE Trans. Image Process."},{"key":"1338_CR28","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1148\/radiol.2015142554","volume":"277","author":"DF Yankelevitz","year":"2015","unstructured":"Yankelevitz, D. F. et al. CT screening for lung cancer: nonsolid nodules in baseline and annual repeat rounds. Radiology 277, 555\u2013564 (2015).","journal-title":"Radiology"},{"key":"1338_CR29","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1183\/09031936.00005914","volume":"45","author":"ET Scholten","year":"2015","unstructured":"Scholten, E. T. et al. Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules? Eur. Respiratory J. 45, 765\u2013773 (2015).","journal-title":"Eur. Respiratory J."},{"key":"1338_CR30","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1148\/radiol.2016152333","volume":"281","author":"R Yip","year":"2016","unstructured":"Yip, R. et al. Lung cancer deaths in the National Lung Screening Trial attributed to nonsolid nodules. Radiology 281, 589\u2013596 (2016).","journal-title":"Radiology"},{"key":"1338_CR31","doi-asserted-by":"publisher","first-page":"102846","DOI":"10.1016\/j.media.2023.102846","volume":"88","author":"A Kazerouni","year":"2023","unstructured":"Kazerouni, A. et al. Diffusion models in medical imaging: A comprehensive survey. Med. Image Anal. 88, 102846 (2023).","journal-title":"Med. Image Anal."},{"key":"1338_CR32","unstructured":"Bae, W., Lee, S., Park, G., Park, H. & Jung, K. H. Residual CNN-based image super-resolution for CT slice thickness reduction using paired CT scans: preliminary validation study. In Proc. Medical Imaging with Deep Learning, pp. 1\u20138, (MIDL, 2018)."},{"key":"1338_CR33","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhou, S. K. & Chellappa, R. D. A.-VSR: domain adaptable volumetric super-resolution for medical images. In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part VI 24 (pp. 75\u201385). (Springer International Publishing, 2021).","DOI":"10.1007\/978-3-030-87231-1_8"},{"key":"1338_CR34","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yang, L., Lai, J. H., & Xie, X. CuNeRF: Cube-based neural radiance field for zero-shot medical image arbitrary-scale super resolution. In Proc. IEEE\/CVF International Conference on Computer Vision (pp. 21185\u201321195) (IEEE, 2023).","DOI":"10.1109\/ICCV51070.2023.01937"},{"key":"1338_CR35","doi-asserted-by":"crossref","unstructured":"Fang, C., et al. Incremental cross-view mutual distillation for self-supervised medical CT synthesis. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 20677\u201320686) (2022).","DOI":"10.1109\/CVPR52688.2022.02002"},{"key":"1338_CR36","doi-asserted-by":"crossref","unstructured":"Shi, J., Pelt, D. M. & Batenburg, K. J. SR4ZCT: Self-supervised Through-Plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap. In International Workshop on Machine Learning in Medical Imaging (pp. 52\u201361) (Cham: Springer Nature Switzerland, 2023).","DOI":"10.1007\/978-3-031-45673-2_6"},{"key":"1338_CR37","first-page":"3499","volume":"33","author":"S Zhou","year":"2020","unstructured":"Zhou, S., Zhang, J., Zuo, W. & Loy, C. C. Cross-scale internal graph neural network for image super-resolution. Adv. Neural Inf. Process. Syst. 33, 3499\u20133509 (2020).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1338_CR38","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","volume":"45","author":"K Han","year":"2022","unstructured":"Han, K. et al. A survey on vision transformer. IEEE Trans. Pattern Anal. Mach. Intell. 45, 87\u2013110 (2022).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1338_CR39","doi-asserted-by":"crossref","unstructured":"Liu, et al. Swin transformer: Hierarchical vision transformer using shifted windows. In Proc. IEEE\/CVF international conference on computer vision (pp. 10012\u201310022) (IEEE, 2021).","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1338_CR40","doi-asserted-by":"crossref","unstructured":"Liang, J. et al. Swinir: Image restoration using swin transformer. In Proc. IEEE\/CVF international conference on computer vision (pp. 1833\u20131844) (IEEE, 2021).","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"1338_CR41","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al. Video swin transformer. In Proc. IEEE\/CVF conference on computer vision and pattern recognition (pp. 3202\u20133211) (IEEE, 2022).","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"1338_CR42","unstructured":"Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. In Proc. International Conference on Learning Representations (ICLR) (OpenReview.net, 2019)."},{"key":"1338_CR43","doi-asserted-by":"crossref","unstructured":"Chen, H. et al. Pre-trained image processing transformer. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 12299\u201312310) (IEEE, 2021).","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"1338_CR44","doi-asserted-by":"crossref","unstructured":"Wang, Z. et al. Uformer: A general u-shaped transformer for image restoration. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 17683\u201317693) (IEEE, 2022).","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"1338_CR45","doi-asserted-by":"crossref","unstructured":"Zamir, S. W. et al. Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 5728\u20135739) (IEEE, 2022).","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"1338_CR46","unstructured":"Zhang, Ji. et al. Accurate image restoration with attention retractable transformer. In Proc. International Conference on Learning Representations (ICLR) (OpenReview.net, 2023)."},{"key":"1338_CR47","unstructured":"Xiao, J., Fu, X., Zhou, M., Liu, H. & Zha, Z. J. Random shuffle transformer for image restoration. In International Conference on Machine Learning (pp. 38039\u201338058) (ICML, 2023)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01338-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01338-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01338-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T20:03:00Z","timestamp":1732392180000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01338-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,23]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1338"],"URL":"https:\/\/doi.org\/10.1038\/s41746-024-01338-8","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,23]]},"assertion":[{"value":"17 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"P.Y. and D.W. are employed by Infervision Medical Technology Co. Ltd. All other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"335"}}