{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:14:46Z","timestamp":1767320086732,"version":"3.48.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032095121","type":"print"},{"value":"9783032095138","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\/978-3-032-09513-8_62","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:10:50Z","timestamp":1767319850000},"page":"649-657","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AGFuse-Net: Enhancing Rapid SPECT\/CT Imaging via\u00a0Anatomy-Guided Attention Gates and\u00a0Multimodality Fusion Network"],"prefix":"10.1007","author":[{"given":"Muzi","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenfeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongmin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianchen","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"issue":"1","key":"62_CR1","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s12880-025-01571-x","volume":"25","author":"TO Alkahtani","year":"2025","unstructured":"Alkahtani, T.O.: Investigating the significance of SPECT\/CT-SUV for monitoring 177Lu-PSMA-targeted radionuclide therapy: a systematic review. BMC Med. Imaging 25(1), 28 (2025)","journal-title":"BMC Med. Imaging"},{"key":"62_CR2","doi-asserted-by":"crossref","unstructured":"Rix, L., Tushingham, S., Wright, K., et al.: Assessing SPECT\/CT for the identification of cartilage lesions in the knee joint: a systematic review. Osteoarthritis Cartilage Open 7(1) (2025)","DOI":"10.1016\/j.ocarto.2025.100577"},{"key":"62_CR3","doi-asserted-by":"crossref","unstructured":"Emmett, L.: SPECT Deserves RESPECT: The Potential of SPECT\/CT to Optimize Patient Outcomes with Theranostics Therapy. J. Nuclear Med. (2025)","DOI":"10.2967\/jnumed.124.268325"},{"key":"62_CR4","doi-asserted-by":"crossref","unstructured":"Tubben, A., Prakken, N.H.J., Ivashchenko, O.V., et al.: Feasibility of the absolute quantification and left ventricular segmentation of cardiac sympathetic innervation in wild\u2013type transthyretin amyloidosis cardiomyopathy with [123I]-MIBG SPECT\/CT: the I\u2013NERVE study. J. Nuclear Cardiol., 102146 (2025)","DOI":"10.1016\/j.nuclcard.2025.102146"},{"key":"62_CR5","doi-asserted-by":"crossref","unstructured":"Timon, C., Feeley, A., Mahon, J., et al.: Does arthrography improve accuracy of SPECT\/CT for diagnosis of aseptic loosening in patients with painful knee arthroplasty: a systematic review and meta analysis. J. Nuclear Med. Technol. (2025)","DOI":"10.2967\/jnmt.123.266050"},{"issue":"2","key":"62_CR6","doi-asserted-by":"publisher","first-page":"200","DOI":"10.3390\/life15020200","volume":"15","author":"E Ttofi","year":"2025","unstructured":"Ttofi, E., Kyriacou, C., Leontiou, T., et al.: A method for calculating small sizes of volumes in postsurgical thyroid SPECT\/CT imaging. Life 15(2), 200 (2025)","journal-title":"Life"},{"issue":"1","key":"62_CR7","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s00784-025-06170-2","volume":"29","author":"P Winnand","year":"2025","unstructured":"Winnand, P., Lammert, M., Ooms, M., et al.: Determination of adequate bony resection margins in inflammatory jaw pathologies using SPECT-CT in primary mandibular reconstruction with virtually planned vascularized bone flaps. Clin. Oral Invest. 29(1), 93 (2025)","journal-title":"Clin. Oral Invest."},{"issue":"1","key":"62_CR8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s12880-025-01570-y","volume":"25","author":"P Yang","year":"2025","unstructured":"Yang, P., Zhang, Z., Wei, J., et al.: Deep learning-based CT-free attenuation correction for cardiac SPECT: a new approach. BMC Med. Imaging 25(1), 38 (2025)","journal-title":"BMC Med. Imaging"},{"issue":"1","key":"62_CR9","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40658-025-00724-9","volume":"12","author":"Z Cheng","year":"2025","unstructured":"Cheng, Z., Chen, P., Yan, J.: A review of state-of-the-art resolution improvement techniques in SPECT imaging. EJNMMI Phys. 12(1), 9 (2025)","journal-title":"EJNMMI Phys."},{"issue":"5","key":"62_CR10","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1007\/s12350-022-03007-3","volume":"30","author":"X Chen","year":"2023","unstructured":"Chen, X., Liu, C.: Deep-learning-based methods of attenuation correction for SPECT and PET. J. Nucl. Cardiol. 30(5), 1859\u20131878 (2023)","journal-title":"J. Nucl. Cardiol."},{"issue":"3","key":"62_CR11","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s12149-023-01889-y","volume":"38","author":"M Kawakubo","year":"2024","unstructured":"Kawakubo, M., Nagao, M., Kaimoto, Y., et al.: Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging. Ann. Nucl. Med. 38(3), 199\u2013209 (2024)","journal-title":"Ann. Nucl. Med."},{"key":"62_CR12","doi-asserted-by":"crossref","unstructured":"Shao, W., Rowe, S.P., Du, Y.: SPECTnet: a deep learning neural network for SPECT image reconstruction. Ann. Transl. Med. 9(9) (2021)","DOI":"10.21037\/atm-20-3345"},{"issue":"1","key":"62_CR13","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s40658-022-00522-7","volume":"10","author":"ID Apostolopoulos","year":"2023","unstructured":"Apostolopoulos, I.D., Papandrianos, N.I., Feleki, A., et al.: Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies. EJNMMI Phys. 10(1), 6 (2023)","journal-title":"EJNMMI Phys."},{"issue":"4","key":"62_CR14","doi-asserted-by":"publisher","first-page":"652","DOI":"10.2967\/jnumed.122.264423","volume":"64","author":"RJH Miller","year":"2023","unstructured":"Miller, R.J.H., Pieszko, K., Shanbhag, A., et al.: Deep learning coronary artery calcium scores from SPECT\/CT attenuation maps improve prediction of major adverse cardiac events. J. Nucl. Med. 64(4), 652\u2013658 (2023)","journal-title":"J. Nucl. Med."},{"issue":"3","key":"62_CR15","doi-asserted-by":"publisher","first-page":"472","DOI":"10.2967\/jnumed.122.264429","volume":"64","author":"AD Shanbhag","year":"2023","unstructured":"Shanbhag, A.D., Miller, R.J.H., Pieszko, K., et al.: Deep learning\u2013based attenuation correction improves diagnostic accuracy of cardiac SPECT. J. Nucl. Med. 64(3), 472\u2013478 (2023)","journal-title":"J. Nucl. Med."},{"key":"62_CR16","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhou, B., Guo, X., et al.: DuDoCFNet: dual-domain coarse-to-fine progressive network for simultaneous denoising, limited-view reconstruction, and attenuation correction of cardiac SPECT. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3385650"},{"key":"62_CR17","doi-asserted-by":"crossref","unstructured":"Mehri A, Ardakani PB, Sappa AD. MPRNet: multi-path residual network for lightweight image super resolution. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2704\u20132713 (2021)","DOI":"10.1109\/WACV48630.2021.00275"},{"key":"62_CR18","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. Unet++: a nested U-Net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pp. 3\u201311. Springer International Publishing (2018)","DOI":"10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09513-8_62","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:10:52Z","timestamp":1767319852000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09513-8_62"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032095121","9783032095138"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09513-8_62","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":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}