{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T09:43:35Z","timestamp":1758361415378,"version":"3.44.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032053244"},{"type":"electronic","value":"9783032053251"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-05325-1_65","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:05:37Z","timestamp":1758308737000},"page":"684-693","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WiD-PET: PET Image Reconstruction from\u00a0Low-Dose Data Using a\u00a0Wavelet-Informed Diffusion Model with\u00a0Fast Inference"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3215-4651","authenticated-orcid":false,"given":"Qingcheng","family":"Lyu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4312-7151","authenticated-orcid":false,"given":"Tong","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6335-4634","authenticated-orcid":false,"given":"Yiran","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6406-2505","authenticated-orcid":false,"given":"Erjian","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Luping","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"65_CR1","doi-asserted-by":"publisher","unstructured":"Chen, K., et al.: Ultra-low-dose 18f-florbetaben amyloid pet imaging using deep learning with multi-contrast mri inputs. Radiology 290(3), 649\u2013656 (2019). https:\/\/doi.org\/10.1148\/radiol.2018180940","DOI":"10.1148\/radiol.2018180940"},{"key":"65_CR2","doi-asserted-by":"publisher","unstructured":"Choudhury, C., Goel, T., Tanveer, M.: A coupled-gan architecture to fuse mri and pet image features for multi-stage classification of alzheimer\u2019s disease. Inf. Fusion 109, 102415 (2024). https:\/\/doi.org\/10.1016\/j.inffus.2024.102415. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253524001933","DOI":"10.1016\/j.inffus.2024.102415"},{"key":"65_CR3","unstructured":"Cui, J., Xie, Y., Guo, N., Feng, Y., Li, Q.: Pet image denoising using consistent denoising diffusion model. J. Nucl. Med. 65(supplement 2), 241799\u2013241799 (2024). https:\/\/jnm.snmjournals.org\/content\/65\/supplement_2\/241799"},{"key":"65_CR4","doi-asserted-by":"publisher","unstructured":"Cui, J., et al.: Image2points: a 3d point-based context clusters gan for high-quality pet image reconstruction. In: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1726\u20131730. IEEE (2024). https:\/\/doi.org\/10.1109\/icassp48485.2024.10446360","DOI":"10.1109\/icassp48485.2024.10446360"},{"key":"65_CR5","doi-asserted-by":"crossref","unstructured":"Friedrich, P., Durrer, A., Wolleb, J., Cattin, P.C.: cwdm: Conditional wavelet diffusion models for cross-modality 3d medical image synthesis (2024). https:\/\/arxiv.org\/abs\/2411.17203","DOI":"10.1007\/978-3-031-72744-3_2"},{"issue":"2","key":"65_CR6","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/s00259-023-06417-8","volume":"51","author":"K Gong","year":"2024","unstructured":"Gong, K., Johnson, K., El Fakhri, G., Li, Q., Pan, T.: Pet image denoising based on denoising diffusion probabilistic model. Eur. J. Nucl. Med. Mol. Imaging 51(2), 358\u2013368 (2024). https:\/\/doi.org\/10.1007\/s00259-023-06417-8","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"65_CR7","doi-asserted-by":"crossref","unstructured":"Han, Z., et al.: Contrastive diffusion model with auxiliary guidance for coarse-to-fine pet reconstruction (2023). https:\/\/arxiv.org\/abs\/2308","DOI":"10.1007\/978-3-031-43999-5_23"},{"key":"65_CR8","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models (2020). https:\/\/arxiv.org\/abs\/2006.11239"},{"issue":"1","key":"65_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40708-020-00104-2","volume":"7","author":"J Islam","year":"2020","unstructured":"Islam, J., Zhang, Y.: GAN-based synthetic brain PET image generation. Brain Inf. 7(1), 1\u201312 (2020). https:\/\/doi.org\/10.1186\/s40708-020-00104-2","journal-title":"Brain Inf."},{"key":"65_CR10","doi-asserted-by":"publisher","unstructured":"Li, J., Cheng, B., Chen, Y., Gao, G., Shi, J., Zeng, T.: Ewt: efficient wavelet-transformer for single image denoising. Neural Netw. 177, 106378 (2024). https:\/\/doi.org\/10.1016\/j.neunet.2024.106378. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893608024003022","DOI":"10.1016\/j.neunet.2024.106378"},{"issue":"2","key":"65_CR11","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11390-024-3414-z","volume":"39","author":"ZN Li","year":"2024","unstructured":"Li, Z.N., Chen, X.H., Guo, S.N., Wang, S.Q., Pun, C.M.: Wavenhancer: unifying wavelet and transformer for image enhancement. J. Comput. Sci. Technol. 39(2), 336\u2013345 (2024). https:\/\/doi.org\/10.1007\/s11390-024-3414-z","journal-title":"J. Comput. Sci. Technol."},{"key":"65_CR12","doi-asserted-by":"publisher","unstructured":"Luo, Y., et al.: Adaptive rectification based adversarial network with spectrum constraint for high-quality pet image synthesis. Med. Image Anal. 77, 102335 (2022). https:\/\/doi.org\/10.1016\/j.media.2021.102335. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841521003807","DOI":"10.1016\/j.media.2021.102335"},{"issue":"12","key":"65_CR13","doi-asserted-by":"publisher","first-page":"3955","DOI":"10.1109\/TMI.2021.3101937","volume":"40","author":"Y Ma","year":"2021","unstructured":"Ma, Y., et al.: Structure and illumination constrained gan for medical image enhancement. IEEE Trans. Med. Imaging 40(12), 3955\u20133967 (2021). https:\/\/doi.org\/10.1109\/TMI.2021.3101937","journal-title":"IEEE Trans. Med. Imaging"},{"key":"65_CR14","unstructured":"Metz, L., Poole, B., Pfau, D., Sohl-Dickstein, J.: Unrolled generative adversarial networks. CoRR arxiv:1611.02163 (2016)"},{"issue":"10","key":"65_CR15","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acca5c","volume":"68","author":"S Pan","year":"2023","unstructured":"Pan, S., et al.: 2d medical image synthesis using transformer-based denoising diffusion probabilistic model. Phys. Med. Biol. 68(10), 105004 (2023). https:\/\/doi.org\/10.1088\/1361-6560\/acca5c","journal-title":"Phys. Med. Biol."},{"key":"65_CR16","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans (2016). https:\/\/arxiv.org\/abs\/1606.03498"},{"key":"65_CR17","unstructured":"Shi, K., Guo, R., Xue, S., Rominger, A., Li, B.: Ultra-low dose pet imaging challenge 2022 (2022). https:\/\/zenodo.org\/records\/6361846"},{"key":"65_CR18","doi-asserted-by":"publisher","unstructured":"Sun, H., et al.: High-quality pet image synthesis from ultra-low-dose pet\/mri using bi-task deep learning. Quant. Imaging Med. Surg. 12(12), 5326 (2022). https:\/\/doi.org\/10.21037\/qims-22-116","DOI":"10.21037\/qims-22-116"},{"key":"65_CR19","unstructured":"Xie, H., et al.: Dose-aware diffusion model for 3d low-dose pet: multi-institutional validation with reader study and real low-dose data (2024). https:\/\/arxiv.org\/abs\/2405.12996"},{"issue":"7","key":"65_CR20","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1109\/TMI.2019.2895894","volume":"38","author":"B Yu","year":"2019","unstructured":"Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., Bourgeat, P.: Ea-gans: edge-aware generative adversarial networks for cross-modality mr image synthesis. IEEE Trans. Med. Imaging 38(7), 1750\u20131762 (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2895894","journal-title":"IEEE Trans. Med. Imaging"},{"key":"65_CR21","doi-asserted-by":"crossref","unstructured":"Zhao, K., et al.: Study of low-dose pet image recovery using supervised learning with cyclegan. Plos one 15(9), e0238455 (2020). https:\/\/pubmed.ncbi.nlm.nih.gov\/32886683\/","DOI":"10.1371\/journal.pone.0238455"},{"key":"65_CR22","doi-asserted-by":"publisher","unstructured":"Zhou, L., Schaefferkoetter, J.D., Tham, I.W., Huang, G., Yan, J.: Supervised learning with cyclegan for low-dose fdg pet image denoising. Med. Image Anal. 65, 101770 (2020) https:\/\/doi.org\/10.1016\/j.media.2020.101770. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841520301341","DOI":"10.1016\/j.media.2020.101770"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05325-1_65","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:05:41Z","timestamp":1758308741000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05325-1_65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032053244","9783032053251"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05325-1_65","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}