{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T21:39:39Z","timestamp":1768253979157,"version":"3.49.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031340475","type":"print"},{"value":"9783031340482","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\/978-3-031-34048-2_43","type":"book-chapter","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T12:03:35Z","timestamp":1686139415000},"page":"563-575","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Geometric Deep Learning for\u00a0Unsupervised Registration of\u00a0Diffusion Magnetic Resonance Images"],"prefix":"10.1007","author":[{"given":"Jose J.","family":"Bouza","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2522-5957","authenticated-orcid":false,"given":"Chun-Hao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baba C.","family":"Vemuri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"issue":"365","key":"43_CR1","first-page":"1","volume":"2","author":"BB Avants","year":"2009","unstructured":"Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ANTS). Insight j 2(365), 1\u201335 (2009)","journal-title":"Insight j"},{"key":"43_CR2","doi-asserted-by":"crossref","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., et al.: An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE CVPR, pp. 9252\u20139260 (2018)","DOI":"10.1109\/CVPR.2018.00964"},{"key":"43_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1007\/978-3-642-04268-3_79","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2009","author":"A Barmpoutis","year":"2009","unstructured":"Barmpoutis, A., Vemuri, B.C.: Groupwise registration and atlas construction of 4th-order tensor fields using the R+ Riemannian metric. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 640\u2013647. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04268-3_79"},{"key":"43_CR4","unstructured":"Bouza, J.J.: Manifold valued Volterra CNNs with apps. Med. Img. Ph.D. thesis, University of Florida (2022)"},{"key":"43_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1007\/978-3-030-78191-0_24","volume-title":"Information Processing in Medical Imaging","author":"JJ Bouza","year":"2021","unstructured":"Bouza, J.J., Yang, C.-H., Vaillancourt, D., Vemuri, B.C.: A higher order manifold-valued convolutional neural network with applications to diffusion MRI processing. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 304\u2013317. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_24"},{"issue":"2","key":"43_CR6","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1109\/TPAMI.2020.3003846","volume":"44","author":"R Chakraborty","year":"2022","unstructured":"Chakraborty, R., Bouza, J., Manton, J., Vemuri, B.C.: ManifoldNet: a deep neural network for manifold-valued data with applications. IEEE TPAMI 44(2), 799\u2013810 (2022)","journal-title":"IEEE TPAMI"},{"key":"43_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/978-3-642-04268-3_24","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2009","author":"G Cheng","year":"2009","unstructured":"Cheng, G., Vemuri, B.C., Carney, P.R., Mareci, T.H.: Non-rigid registration of high angular resolution diffusion images represented by Gaussian mixture fields. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 190\u2013197. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04268-3_24"},{"key":"43_CR8","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.media.2019.07.006","volume":"57","author":"AV Dalca","year":"2019","unstructured":"Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Img. Anal. 57, 226\u2013236 (2019)","journal-title":"Med. Img. Anal."},{"key":"43_CR9","unstructured":"Dhollander, T., Raffelt, D., Connelly, A.: Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. In: ISMRM Workshop on Breaking the Barriers of Diffusion MRI, vol. 5. ISMRM (2016)"},{"key":"43_CR10","unstructured":"Henrik, J.G.: Density-based similarity in the registration of diffusion-weighted images. Ph.D. thesis, University of Copenhagen (2018)"},{"key":"43_CR11","unstructured":"Hoffmann, M., Billot, B., Greve, D.N., Iglesias, J.E., Fischl, B., Dalca, A.V.: SynthMorph: learning contrast-invariant registration without acquired images. arXiv preprint arXiv:2004.10282 (2020)"},{"key":"43_CR12","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in NeurIPS, pp. 2017\u20132025 (2015)"},{"key":"43_CR13","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.neuroimage.2014.07.061","volume":"103","author":"B Jeurissen","year":"2014","unstructured":"Jeurissen, B., Tournier, J.D., Dhollander, T., Connelly, A., Sijbers, J.: Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage 103, 411\u2013426 (2014)","journal-title":"Neuroimage"},{"issue":"2","key":"43_CR14","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1006\/nimg.2002.1148","volume":"17","author":"D Jones","year":"2002","unstructured":"Jones, D., Lewis, D., Alexander, D., et al.: Spatial normalization and averaging of diffusion tensor MRI data sets. Neuroimage 17(2), 592\u2013617 (2002)","journal-title":"Neuroimage"},{"issue":"3","key":"43_CR15","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/j.neuroimage.2008.12.037","volume":"46","author":"A Klein","year":"2009","unstructured":"Klein, A., Andersson, J., Ardekani, B.A., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46(3), 786\u2013802 (2009)","journal-title":"Neuroimage"},{"key":"43_CR16","doi-asserted-by":"crossref","unstructured":"Li, H., Fan, Y.: Non-rigid image registration using fully convolutional networks with deep self-supervision. arXiv preprint arXiv:1709.00799 (2017)","DOI":"10.1109\/ISBI.2018.8363757"},{"issue":"3","key":"43_CR17","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1002\/mrm.23058","volume":"67","author":"D Raffelt","year":"2012","unstructured":"Raffelt, D., Tournier, J.D., Crozier, S., Connelly, A., Salvado, O.: Reorientation of fiber orientation distributions using apodized point spread functions. MRM 67(3), 844\u2013855 (2012)","journal-title":"MRM"},{"issue":"3","key":"43_CR18","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1016\/j.neuroimage.2011.02.014","volume":"56","author":"D Raffelt","year":"2011","unstructured":"Raffelt, D., Tournier, J.D., Fripp, J., et al.: Symmetric diffeomorphic registration of fibre orientation distributions. Neuroimage 56(3), 1171\u20131180 (2011)","journal-title":"Neuroimage"},{"key":"43_CR19","doi-asserted-by":"crossref","unstructured":"Srivastava, A., Jermyn, I., Joshi, S.: Riemannian analysis of probability density functions with applications in vision. In: 2007 IEEE CVPR, pp. 1\u20138. IEEE (2007)","DOI":"10.1109\/CVPR.2007.383188"},{"issue":"4","key":"43_CR20","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1016\/j.neuroimage.2007.02.016","volume":"35","author":"JD Tournier","year":"2007","unstructured":"Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35(4), 1459\u20131472 (2007)","journal-title":"Neuroimage"},{"issue":"4","key":"43_CR21","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","volume":"62","author":"DC Van Essen","year":"2012","unstructured":"Van Essen, D.C., Ugurbil, K., Auerbach, E., et al.: The human connectome project: a data acquisition perspective. Neuroimage 62(4), 2222\u20132231 (2012)","journal-title":"Neuroimage"},{"key":"43_CR22","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.media.2019.06.012","volume":"57","author":"BC Vemuri","year":"2019","unstructured":"Vemuri, B.C., Sun, J., Banerjee, M., et al.: A geometric framework for ensemble average propagator reconstruction from diffusion MRI. Med. Img. Anal. 57, 89\u2013105 (2019)","journal-title":"Med. Img. Anal."},{"key":"43_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/978-3-319-67558-9_24","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"BD de Vos","year":"2017","unstructured":"de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M., I\u0161gum, I.: End-to-end unsupervised deformable image registration with a convolutional neural network. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS 2017. LNCS, vol. 10553, pp. 204\u2013212. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_24"},{"key":"43_CR24","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.neuroimage.2018.07.070","volume":"183","author":"J Wasserthal","year":"2018","unstructured":"Wasserthal, J., Neher, P., Maier-Hein, K.H.: TractSeg-fast and accurate white matter tract segmentation. Neuroimage 183, 239\u2013253 (2018)","journal-title":"Neuroimage"},{"key":"43_CR25","doi-asserted-by":"crossref","unstructured":"van Werkhoven, B., Maassen, J., Bal, H.E., Seinstra, F.J.: Optimizing convolution operations on GPUs using adaptive tiling. Future Gener. Comput. Syst. 30(C), 14\u201326 (2014)","DOI":"10.1016\/j.future.2013.09.003"},{"key":"43_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1007\/978-3-540-85990-1_109","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2008","author":"J Yang","year":"2008","unstructured":"Yang, J., Shen, D., Davatzikos, C., Verma, R.: Diffusion tensor image registration using tensor geometry and orientation features. In: Metaxas, D., Axel, L., Fichtinger, G., Sz\u00e9kely, G. (eds.) MICCAI 2008. LNCS, vol. 5242, pp. 905\u2013913. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-85990-1_109"},{"key":"43_CR27","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neuroimage.2017.07.008","volume":"158","author":"X Yang","year":"2017","unstructured":"Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: fast predictive image registration-a deep learning approach. Neuroimage 158, 378\u2013396 (2017)","journal-title":"Neuroimage"},{"key":"43_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, F., Wells, W.M., O\u2019Donnell, L.J.: Deep diffusion MRI registration (DDMReg): a deep learning method for diffusion MRI registration. IEEE Trans. Med. Img. (2021)","DOI":"10.1101\/2021.03.04.433968"},{"key":"43_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-642-40763-5_4","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"P Zhang","year":"2013","unstructured":"Zhang, P., Niethammer, M., Shen, D., Yap, P.-T.: Large deformation diffeomorphic registration of diffusion-weighted images with explicit orientation optimization. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 27\u201334. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_4"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34048-2_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T12:08:22Z","timestamp":1686139702000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34048-2_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031340475","9783031340482"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34048-2_43","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":"8 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Carlos de Bariloche","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Argentina","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":"12 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2023","order":10,"name":"conference_id","label":"Conference ID","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":"169","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":"63","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":"37% - 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":"4","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)"}}]}}