{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:29:13Z","timestamp":1772724553648,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439926","type":"print"},{"value":"9783031439933","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43993-3_3","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"25-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Towards Accurate Microstructure Estimation via\u00a03D Hybrid Graph Transformer"],"prefix":"10.1007","author":[{"given":"Junqing","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haotian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tewodros","family":"Tassew","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiquan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pew-Thian","family":"Yap","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"3_CR1","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)"},{"issue":"12","key":"3_CR2","doi-asserted-by":"publisher","first-page":"2838","DOI":"10.1109\/TMI.2019.2915629","volume":"38","author":"G Chen","year":"2019","unstructured":"Chen, G., Dong, B., Zhang, Y., Lin, W., Shen, D., Yap, P.T.: Denoising of diffusion MRI data via graph framelet matching in x-q space. IEEE Trans. Med. Imaging 38(12), 2838\u20132848 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.media.2019.06.010","volume":"57","author":"G Chen","year":"2019","unstructured":"Chen, G., Dong, B., Zhang, Y., Lin, W., Shen, D., Yap, P.T.: XQ-SR: joint x-q space super-resolution with application to infant diffusion MRI. Med. Image Anal. 57, 44\u201355 (2019)","journal-title":"Med. Image Anal."},{"key":"3_CR4","doi-asserted-by":"publisher","unstructured":"Chen, G., et al.: Estimating tissue microstructure with undersampled diffusion data via graph convolutional neural networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 280\u2013290. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_28","DOI":"10.1007\/978-3-030-59728-3_28"},{"key":"3_CR5","doi-asserted-by":"publisher","unstructured":"Chen, G., et al.: Hybrid graph transformer for tissue microstructure estimation with undersampled diffusion MRI data. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS, vol. 13431, pp. 113\u2013122. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16431-6_11","DOI":"10.1007\/978-3-031-16431-6_11"},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.media.2019.01.006","volume":"53","author":"G Chen","year":"2019","unstructured":"Chen, G., Wu, Y., Shen, D., Yap, P.T.: Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space. Med. Image Anal. 53, 79\u201394 (2019)","journal-title":"Med. Image Anal."},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.neuroimage.2017.04.041","volume":"170","author":"H Chen","year":"2018","unstructured":"Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage 170, 446\u2013455 (2018)","journal-title":"NeuroImage"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Daducci, A., et al.: Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data. NeuroImage 105, 32\u201344 (2015)","DOI":"10.1016\/j.neuroimage.2014.10.026"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Dou, Q., et al.: Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans. Med. Imaging 35(5), 1182\u20131195 (2016)","DOI":"10.1109\/TMI.2016.2528129"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Falk, T., et al.: U-net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16(1), 67\u201370 (2019)","DOI":"10.1038\/s41592-018-0261-2"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Gibbons, E.K., et al.: Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning. Magnet. Resonan. Med. 81(4), 2399\u20132411 (2019)","DOI":"10.1002\/mrm.27568"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Golkov, V., et al.: q-Space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans. Med. Imaging 35(5), 1344\u20131351 (2016)","DOI":"10.1109\/TMI.2016.2551324"},{"key":"3_CR13","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415 (2016)"},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Hong, Y., Chen, G., Yap, P.-T., Shen, D.: Multifold acceleration of diffusion MRI via deep learning reconstruction from slice-undersampled data. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 530\u2013541. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_41","DOI":"10.1007\/978-3-030-20351-1_41"},{"issue":"6","key":"3_CR15","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1002\/mrm.20508","volume":"53","author":"JH Jensen","year":"2005","unstructured":"Jensen, J.H., Helpern, J.A., Ramani, A., Lu, H., Kaczynski, K.: Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnet. Resonan. Med. 53(6), 1432\u20131440 (2005)","journal-title":"Magnet. Resonan. Med."},{"issue":"4","key":"3_CR16","doi-asserted-by":"publisher","first-page":"1752","DOI":"10.1002\/mrm.25734","volume":"75","author":"E Kaden","year":"2016","unstructured":"Kaden, E., Kruggel, F., Alexander, D.C.: Quantitative mapping of the per-axon diffusion coefficients in brain white matter. Magnet. Resonan. Med. 75(4), 1752\u20131763 (2016)","journal-title":"Magnet. Resonan. Med."},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Tian, Q., et al.: DeepDTI: high-fidelity six-direction diffusion tensor imaging using deep learning. NeuroImage 219, 117017 (2020)","DOI":"10.1016\/j.neuroimage.2020.117017"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. NeuroImage 80, 62\u201379 (2013)","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"key":"3_CR20","unstructured":"Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861\u20136871. PMLR (2019)"},{"key":"3_CR21","doi-asserted-by":"publisher","unstructured":"Ye, C.: Estimation of tissue microstructure using a deep network inspired by a sparse reconstruction framework. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 466\u2013477. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_37","DOI":"10.1007\/978-3-319-59050-9_37"},{"key":"3_CR22","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.media.2017.09.001","volume":"42","author":"C Ye","year":"2017","unstructured":"Ye, C.: Tissue microstructure estimation using a deep network inspired by a dictionary-based framework. Med. Image Anal. 42, 288\u2013299 (2017)","journal-title":"Med. Image Anal."},{"issue":"4","key":"3_CR23","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1016\/j.neuroimage.2012.03.072","volume":"61","author":"H Zhang","year":"2012","unstructured":"Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61(4), 1000\u20131016 (2012)","journal-title":"NeuroImage"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Zheng, T., et al.: A microstructure estimation transformer inspired by sparse representation for diffusion MRI. Med. Image Anal. 86, 102788 (2023)","DOI":"10.1016\/j.media.2023.102788"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, H.Y., et al.: nnFormer: volumetric medical image segmentation via a 3D transformer. IEEE Trans. Image Process. (2023)","DOI":"10.1109\/TIP.2023.3293771"},{"key":"3_CR26","doi-asserted-by":"publisher","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1","DOI":"10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43993-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:10:11Z","timestamp":1712074211000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43993-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439926","9783031439933"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43993-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"730","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}