{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:47:28Z","timestamp":1782953248295,"version":"3.54.5"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032233431","type":"print"},{"value":"9783032233448","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-23344-8_13","type":"book-chapter","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:19:10Z","timestamp":1782951550000},"page":"115-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ultra-Low-Field Brain MRI Enhancement using Resfusion and\u00a0Residual Artifact Suppression Network"],"prefix":"10.1007","author":[{"given":"Youngmin","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeongchan","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taehoon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaeyun","family":"Shin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhyeon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongchul","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","unstructured":"Altaf, A., et al.: Applications, limitations and advancements of ultra-low-field magnetic resonance imaging: A scoping review. Surg. Neurol. Int. 15, 218 (2024). https:\/\/doi.org\/10.25259\/SNI_162_2024","DOI":"10.25259\/SNI_162_2024"},{"key":"13_CR2","doi-asserted-by":"publisher","unstructured":"Zeng, W., Peng, J., Wang, S., Liu, Q.: A comparative study of CNN-based super-resolution methods in MRI reconstruction and its beyond. Sig. Process. Image Commun. 81, 115701 (2020). https:\/\/doi.org\/10.1016\/j.image.2019.115701","DOI":"10.1016\/j.image.2019.115701"},{"key":"13_CR3","doi-asserted-by":"publisher","unstructured":"Li, K., Tang, B., Huang, J., Li, J.: 3D-MRI super-resolution reconstruction using multi-modality based on multi-resolution CNN. Comput. Methods Prog. Biomed. 248, 108110 (2024). https:\/\/doi.org\/10.1016\/j.cmpb.2024.108110","DOI":"10.1016\/j.cmpb.2024.108110"},{"key":"13_CR4","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 27, pp. 2672\u20132680 (2014). https:\/\/papers.nips.cc\/paper\/2014\/hash\/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html"},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Yang, Q., Li, N., Zhao, Z., Fan, X., Chang, E.-I.-C., Xu, Y.: MRI cross-modality image-to-image translation. Sci. Rep. 10(1), 3753 (2020). https:\/\/doi.org\/10.1038\/s41598-020-60520-6","DOI":"10.1038\/s41598-020-60520-6"},{"key":"13_CR6","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33, 6840\u20136851 (2020). https:\/\/arxiv.org\/abs\/2006.11239"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Shi, Z., et al.: Resfusion: denoising diffusion probabilistic models for image restoration based on prior residual noise. arXiv preprint arXiv:2311.14900 (2023). https:\/\/doi.org\/10.48550\/arXiv.2311.14900","DOI":"10.52202\/079017-4153"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Islam, K.T., et al.: Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images. Sci. Rep. 13(1), 21183 (2023). https:\/\/doi.org\/10.1038\/s41598-023-48438-1","DOI":"10.1038\/s41598-023-48438-1"},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Dayarathna, S., et al.: Deep learning based synthesis of MRI, CT and PET: review and analysis. Med. Image Anal. 92, 103046 (2024). https:\/\/doi.org\/10.1016\/j.media.2023.103046","DOI":"10.1016\/j.media.2023.103046"},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Dayarathna, S., et al.: McCaD: multi-contrast MRI conditioned, adaptive adversarial diffusion model for high-fidelity MRI synthesis. In: 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 670\u2013679. IEEE (2025). https:\/\/doi.org\/10.1109\/WACV61041.2025.00075","DOI":"10.1109\/WACV61041.2025.00075"},{"key":"13_CR11","doi-asserted-by":"publisher","unstructured":"Dayarathna, S., et al.: Ultra low-field to high-field MRI translation using adversarial diffusion. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1\u20134. IEEE (2024). https:\/\/doi.org\/10.1109\/ISBI56570.2024.10635808","DOI":"10.1109\/ISBI56570.2024.10635808"},{"key":"13_CR12","doi-asserted-by":"publisher","unstructured":"Lee, S., Chung, H., Park, M., Park, J., Ryu, W.-S., Ye, J.C.: Improving 3D imaging with pre-trained perpendicular 2D diffusion models. arXiv preprint arXiv:2303.08440 (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.08440","DOI":"10.48550\/arXiv.2303.08440"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Chung, H., Ryu, D., McCann, M.T., Klasky, M.L., Ye, J.C.: Solving 3D inverse problems using pre-trained 2d diffusion models. arXiv preprint arXiv:2211.10655 (2022). https:\/\/doi.org\/10.48550\/arXiv.2211.10655","DOI":"10.1109\/CVPR52729.2023.02159"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: 2.5D multi-view averaging diffusion model for 3d medical image translation: application to low-count PET reconstruction with CT-less attenuation correction. arXiv preprint arXiv:2406.08374 (2024). https:\/\/doi.org\/10.48550\/arXiv.2406.08374","DOI":"10.1109\/TMI.2025.3570342"},{"key":"13_CR15","doi-asserted-by":"publisher","unstructured":"Isensee, F., et al.: Automated brain extraction of multi-sequence MRI using artificial neural networks. Hum. Brain Map. 1\u201313 (2019). https:\/\/doi.org\/10.1002\/hbm.24750","DOI":"10.1002\/hbm.24750"}],"container-title":["Lecture Notes in Computer Science","Enhancing Ultra-Low-Field MRI with Paired High-Field MRI Comparisons for Brain Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-23344-8_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:19:21Z","timestamp":1782951561000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-23344-8_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032233431","9783032233448"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-23344-8_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ULF-EnC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Challenge on Ultra-Low-Field MRI Image Enhancement","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ulf-enc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.synapse.org\/Synapse:syn65485242\/wiki\/631224","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}