{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T06:42:28Z","timestamp":1760424148197,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031448577"},{"type":"electronic","value":"9783031448584"}],"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\/978-3-031-44858-4_2","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T05:01:55Z","timestamp":1696654915000},"page":"14-22","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-shell dMRI Estimation from Single-Shell Data via Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6248-8477","authenticated-orcid":false,"given":"Reagan","family":"Dugan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0576-0047","authenticated-orcid":false,"given":"Owen","family":"Carmichael","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"2_CR1","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, 1000\u20131016 (2012). https:\/\/doi.org\/10.1016\/j.neuroimage.2012.03.072","journal-title":"Neuroimage"},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1002\/hbm.21032","volume":"32","author":"B Jeurissen","year":"2011","unstructured":"Jeurissen, B., Leemans, A., Jones, D.K., Tournier, J.D., Sijbers, J.: Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Hum. Brain Mapp. 32, 461\u2013479 (2011). https:\/\/doi.org\/10.1002\/hbm.21032","journal-title":"Hum. Brain Mapp."},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.neuroimage.2014.03.057","volume":"96","author":"N Kunz","year":"2014","unstructured":"Kunz, N., et al.: Assessing white matter microstructure of the newborn with multi-shell diffusion MRI and biophysical compartment models. Neuroimage 96, 288\u2013299 (2014). https:\/\/doi.org\/10.1016\/j.neuroimage.2014.03.057","journal-title":"Neuroimage"},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Chang, Y.S., et al.: White matter changes of neurite density and fiber orientation dispersion during human brain maturation. PLoS One 10, (2015). https:\/\/doi.org\/10.1371\/journal.pone.0123656","DOI":"10.1371\/journal.pone.0123656"},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1523\/JNEUROSCI.3979-14.2015","volume":"35","author":"A Nazeri","year":"2015","unstructured":"Nazeri, A., et al.: Functional consequences of neurite orientation dispersion and density in humans across the adult lifespan. J. Neurosci. 35, 1753\u20131762 (2015). https:\/\/doi.org\/10.1523\/JNEUROSCI.3979-14.2015","journal-title":"J. Neurosci."},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1002\/nbm.3017","volume":"26","author":"JD Tournier","year":"2013","unstructured":"Tournier, J.D., Calamante, F., Connelly, A.: Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed. 26, 1775\u20131786 (2013). https:\/\/doi.org\/10.1002\/nbm.3017","journal-title":"NMR Biomed."},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Koppers, S., Haarburger, C., Merhof, D.: Diffusion MRI Signal Augmentation: From Single Shell to Multi Shell with Deep Learning (2016)","DOI":"10.1007\/978-3-319-54130-3_5"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Jha, R.R., Nigam, A., Bhavsar, A., Pathak, S.K., Schneider, W., Rathish, K.: Multi-shell D-MRI reconstruction via residual learning utilizing encoder-decoder network with attention (MSR-Net). In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBS. 2020-July, pp. 1709\u20131713 (2020). https:\/\/doi.org\/10.1109\/EMBC44109.2020.9175455","DOI":"10.1109\/EMBC44109.2020.9175455"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.media.2018.01.003","volume":"46","author":"H Yan","year":"2018","unstructured":"Yan, H., Carmichael, O., Paul, D., Peng, J.: Estimating fiber orientation distribution from diffusion MRI with spherical needlets. Med. Image Anal. 46, 57\u201372 (2018). https:\/\/doi.org\/10.1016\/j.media.2018.01.003","journal-title":"Med. Image Anal."},{"key":"2_CR10","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). https:\/\/doi.org\/10.1016\/j.neuroimage.2014.07.061","journal-title":"Neuroimage"},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1002\/mrm.27568","volume":"81","author":"EK Gibbons","year":"2019","unstructured":"Gibbons, E.K., et al.: Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning. Magn. Reson. Med. 81, 2399\u20132411 (2019). https:\/\/doi.org\/10.1002\/mrm.27568","journal-title":"Magn. Reson. Med."},{"key":"2_CR12","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/978-3-030-32248-9_59","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y Hong","year":"2019","unstructured":"Hong, Y., Chen, G., Yap, P.-T., Shen, D.: Reconstructing high-quality diffusion MRI data from orthogonal slice-undersampled data using graph convolutional neural networks. In: Shen, D., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019, pp. 529\u2013537. Springer International Publishing, Cham (2019)"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Feinberg, D.A., et al.: Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One 5 (2010). https:\/\/doi.org\/10.1371\/journal.pone.0015710","DOI":"10.1371\/journal.pone.0015710"},{"key":"2_CR14","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.neuroimage.2012.06.033","volume":"63","author":"K Setsompop","year":"2012","unstructured":"Setsompop, K., et al.: Improving diffusion MRI using simultaneous multi-slice echo planar imaging. Neuroimage 63, 569\u2013580 (2012). https:\/\/doi.org\/10.1016\/j.neuroimage.2012.06.033","journal-title":"Neuroimage"},{"key":"2_CR15","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, pp. 234\u2013241. Springer International Publishing, Cham (2015)"},{"key":"2_CR16","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1097\/00000441-200111000-00007","volume":"322","author":"GS Berenson","year":"2001","unstructured":"Berenson, G.S.: Bogalusa heart study: a long-term community study of a rural biracial (Black\/White) population. Am. J. Med. Sci. 322, 267\u2013274 (2001). https:\/\/doi.org\/10.1097\/00000441-200111000-00007","journal-title":"Am. J. Med. Sci."},{"key":"2_CR17","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1016\/j.neuroimage.2005.02.018","volume":"26","author":"J Ashburner","year":"2005","unstructured":"Ashburner, J., Friston, K.J.: Unified segmentation. Neuroimage 26, 839\u2013851 (2005). https:\/\/doi.org\/10.1016\/j.neuroimage.2005.02.018","journal-title":"Neuroimage"},{"key":"2_CR18","unstructured":"Descoteaux, M.: High Angular Resolution Diffusion MRI: from Local Estimation to Segmentation and Tractography (2008)"},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.neuroimage.2013.04.127.The","volume":"80","author":"MF Glasser","year":"2013","unstructured":"Glasser, M.F., et al.: The minimal preprocessing pipelines for the human connectome project and for the WU-Minn HCP consortium. Neuroimage 80, 105\u201312404 (2013). https:\/\/doi.org\/10.1016\/j.neuroimage.2013.04.127.The","journal-title":"Neuroimage"},{"key":"2_CR20","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1006\/nimg.2002.1132","volume":"17","author":"M Jenkinson","year":"2002","unstructured":"Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825\u2013841 (2002)","journal-title":"Neuroimage"},{"key":"2_CR21","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1016\/j.neuroimage.2019.06.039","volume":"200","author":"L Cordero-Grande","year":"2019","unstructured":"Cordero-Grande, L., Christiaens, D., Hutter, J., Price, A.N., Hajnal, J.V.: Complex diffusion-weighted image estimation via matrix recovery under genera noise models. Neuroimage 200, 391\u2013404 (2019)","journal-title":"Neuroimage"},{"key":"2_CR22","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1002\/mrm.20667","volume":"54","author":"AW Anderson","year":"2005","unstructured":"Anderson, A.W.: Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magn. Reson. Med. 54, 1194\u20131206 (2005). https:\/\/doi.org\/10.1002\/mrm.20667","journal-title":"Magn. Reson. Med."}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Clinical Neuroimaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44858-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:44:39Z","timestamp":1710261879000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44858-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031448577","9783031448584"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44858-4_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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":"MLCN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Clinical Neuroimaging","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":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlcn2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlcnworkshop.github.io\/","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":"27","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":"16","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":"59% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}