{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:22:41Z","timestamp":1761952961910,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533978","type":"print"},{"value":"9789819533985","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3398-5_16","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:19:32Z","timestamp":1761952772000},"page":"187-198","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Dual-Branch Cross-Attention Transformer Network for Low-Dose CT Denoising"],"prefix":"10.1007","author":[{"given":"Yuqin","family":"Li","sequence":"first","affiliation":[]},{"given":"Mengcheng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Fei","family":"He","sequence":"additional","affiliation":[]},{"given":"Zhengang","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"issue":"22","key":"16_CR1","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1056\/NEJMra072149","volume":"357","author":"DJ Brenner","year":"2007","unstructured":"Brenner, D.J., Hall, E.J.: Computed tomography\u2014an increasing source of radiation exposure. N. Engl. J. Med. 357(22), 2277\u20132284 (2007)","journal-title":"N. Engl. J. Med."},{"issue":"7","key":"16_CR2","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1109\/TMI.2003.815073","volume":"22","author":"S Schaller","year":"2003","unstructured":"Schaller, S., Wildberger, J.E., Raupach, R., et al.: Spatial domain filtering for fast modification of the tradeoff between image sharpness and pixel noise in computed tomography. IEEE Trans. Med. Imaging 22(7), 846\u2013853 (2003)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"16_CR3","doi-asserted-by":"publisher","first-page":"4911","DOI":"10.1118\/1.3232004","volume":"36","author":"A Manduca","year":"2009","unstructured":"Manduca, A., Yu, L., Trzasko, J.D., et al.: Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med. Phys. 36(11), 4911\u20134919 (2009)","journal-title":"Med. Phys."},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol. 2, pp. 60\u201365. IEEE (2005)","DOI":"10.1109\/CVPR.2005.38"},{"issue":"18","key":"16_CR5","doi-asserted-by":"publisher","first-page":"3348","DOI":"10.1016\/j.ijleo.2012.10.044","volume":"124","author":"Y Liu","year":"2013","unstructured":"Liu, Y., Gui, Z., Zhang, Q.: Noise reduction for low-dose X-ray CT based on fuzzy logical in stationary wavelet domain. Optik-Int. J. Light Electron Optics 124(18), 3348\u20133352 (2013)","journal-title":"Optik-Int. J. Light Electron Optics"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Li, H., Wu, H.: A CT image denoise method using curvelet transform. In: Communication Systems and Information Technology: Selected Papers from the 2011 International Conference on Electric and Electronics (EEIC 2011) in Nanchang, China on June 20\u201322, 2011, Volume 4. Springer Berlin Heidelberg, pp. 681\u2013687 (2011)","DOI":"10.1007\/978-3-642-21762-3_89"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Kang, D., Slomka, P., Nakazato, R., et al.: Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm. In: Medical Imaging 2013: Image Processing. SPIE, vol. 8669, pp. 671\u2013676 (2013)","DOI":"10.1117\/12.2006907"},{"issue":"12","key":"16_CR8","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1109\/TMI.2014.2336860","volume":"33","author":"Y Chen","year":"2014","unstructured":"Chen, Y., Shi, L., Feng, Q., et al.: Artifact suppressed dictionary learning for low-dose CT image processing. IEEE Trans. Med. Imaging 33(12), 2271\u20132292 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Yu, N.N., Qiu, T.S., Liu, W.: Medical image fusion based on sparse representation with KSVD. In: World Congress on Medical Physics and Biomedical Engineering May 26-31, 2012, Beijing, China. Springer Berlin Heidelberg, pp. 550\u2013553 (2013)","DOI":"10.1007\/978-3-642-29305-4_144"},{"issue":"12","key":"16_CR10","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TMI.2017.2715284","volume":"36","author":"H Chen","year":"2017","unstructured":"Chen, H., Zhang, Y., Kalra, M.K., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(12), 2524\u20132535 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"16_CR11","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., et al.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"16_CR12","doi-asserted-by":"publisher","first-page":"2035","DOI":"10.1109\/TMI.2019.2963248","volume":"39","author":"F Fan","year":"2019","unstructured":"Fan, F., Shan, H., Kalra, M.K., et al.: Quadratic autoencoder (Q-AE) for low-dose CT denoising. IEEE Trans. Med. Imaging 39(6), 2035\u20132050 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Liang, T., Jin, Y., Li, Y., et al.: EDCNN: edge enhancement-based densely connected network with compound loss for low-dose CT denoising. In: 2020 15th IEEE International conference on signal processing (ICSP). IEEE, vol. 1, pp. 193\u2013198 (2020)","DOI":"10.1109\/ICSP48669.2020.9320928"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yu, L., Liang, X., et al.: TransCT: dual-path transformer for low dose computed tomography. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part VI 24. Springer International Publishing, pp. 55\u201364 (2021)","DOI":"10.1007\/978-3-030-87231-1_6"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., et al.: SwinIR: image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"issue":"6","key":"16_CR16","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acc000","volume":"68","author":"D Wang","year":"2023","unstructured":"Wang, D., Fan, F., Wu, Z., et al.: CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising. Phys. Med. Biol. 68(6), 065012 (2023)","journal-title":"Phys. Med. Biol."},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Pan, J., Liu, S., Sun, D., et al.: Learning dual convolutional neural networks for low-level vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3070\u20133079 (2018)","DOI":"10.1109\/CVPR.2018.00324"},{"key":"16_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110291","volume":"149","author":"W Wu","year":"2024","unstructured":"Wu, W., Liu, S., Xia, Y., et al.: Dual residual attention network for image denoising. Pattern Recogn. 149, 110291 (2024)","journal-title":"Pattern Recogn."},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ye, L., Gong, W., et al.: A novel network for low-dose CT denoising based on dual-branch structure and multi-scale residual attention. J. Imaging Inf. Med. 1\u201320 (2024)","DOI":"10.1007\/s10278-024-01254-z"},{"issue":"10","key":"16_CR20","doi-asserted-by":"publisher","first-page":"e339","DOI":"10.1002\/mp.12345","volume":"44","author":"CH McCollough","year":"2017","unstructured":"McCollough, C.H., Bartley, A.C., Carter, R.E., et al.: Low-dose CT for the detection and classification of metastatic liver lesions: results of the 2016 low dose CT grand challenge. Med. Phys. 44(10), e339\u2013e352 (2017)","journal-title":"Med. Phys."}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3398-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:19:34Z","timestamp":1761952774000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3398-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9789819533978","9789819533985"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3398-5_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xuzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icig.csig.org.cn\/2025\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}