{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T02:52:11Z","timestamp":1761015131983,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322472"},{"type":"electronic","value":"9783030322489"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32248-9_5","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"39-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework"],"prefix":"10.1007","author":[{"given":"Viswanath P.","family":"Sudarshan","sequence":"first","affiliation":[]},{"given":"Kratika","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Gary","family":"Egan","sequence":"additional","affiliation":[]},{"given":"Zhaolin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Suyash P.","family":"Awate","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"5_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/11505730_56","volume-title":"Information Processing in Medical Imaging","author":"SP Awate","year":"2005","unstructured":"Awate, S.P., Whitaker, R.T.: Nonparametric neighborhood statistics for MRI denoising. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 677\u2013688. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11505730_56"},{"issue":"9","key":"5_CR2","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1109\/TMI.2007.900319","volume":"26","author":"SP Awate","year":"2007","unstructured":"Awate, S.P., Whitaker, R.T.: Feature-preserving MRI denoising: a nonparametric empirical-Bayes approach. IEEE Trans. Med. Imaging 26(9), 1242\u20131255 (2007)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"5_CR3","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1002\/mrm.22956","volume":"66","author":"B Bilgic","year":"2011","unstructured":"Bilgic, B., Goyal, V., Adalsteinsson, E.: Multi-contrast reconstruction with Bayesian compressed sensing. Magn. Reson. Med. 66(6), 1601 (2011)","journal-title":"Magn. Reson. Med."},{"issue":"5","key":"5_CR4","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1109\/42.538945","volume":"15","author":"J Bowsher","year":"1996","unstructured":"Bowsher, J., Johnson, V., Turkington, T., Jaszczak, R., Floyd, C., Coleman, R.: Bayesian reconstruction and use of anatomical a priori information for emission tomography. IEEE Trans. Med. Imaging 15(5), 673\u2013686 (1996)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"5_CR5","doi-asserted-by":"publisher","first-page":"015001","DOI":"10.1088\/0266-5611\/31\/1\/015001","volume":"31","author":"M Ehrhardt","year":"2014","unstructured":"Ehrhardt, M., et al.: Joint reconstruction of PET-MRI by exploiting structural similarity. Inverse Problems 31(1), 015001 (2014)","journal-title":"Inverse Problems"},{"issue":"2","key":"5_CR6","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1002\/mrm.22595","volume":"65","author":"F Knoll","year":"2011","unstructured":"Knoll, F., Bredies, K., Pock, T., Stollberger, R.: Second order total generalized variation (TGV) for MRI. Magn. Reson. Med. 65(2), 480\u2013491 (2011)","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"5_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMI.2016.2564989","volume":"36","author":"F Knoll","year":"2017","unstructured":"Knoll, F., Holler, M., Koesters, T., Otazo, R., Bredies, K., Sodickson, D.: Joint MR-PET reconstruction using a multi-channel image regularizer. IEEE Trans. Med. Imaging 36(1), 1\u201316 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Leahy, R., Yan, X.: Incorporation of anatomical MR data for improved functional imaging with PET. In: Information Processing in Medical Imaging, pp. 105\u2013120 (1991)","DOI":"10.1007\/BFb0033746"},{"issue":"15","key":"5_CR9","doi-asserted-by":"publisher","first-page":"5975","DOI":"10.1088\/1361-6560\/aa7670","volume":"62","author":"A Mehranian","year":"2017","unstructured":"Mehranian, A., et al.: PET image reconstruction using multi-parametric anato-functional priors. Phys. Med. Biol. 62(15), 5975\u20136007 (2017)","journal-title":"Phys. Med. Biol."},{"issue":"1","key":"5_CR10","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TMI.2017.2691044","volume":"37","author":"A Mehranian","year":"2018","unstructured":"Mehranian, A., Belzunce, M., Prieto, C., Hammers, A., Reader, A.: Synergistic PET and SENSE MR image reconstruction using joint sparsity regularization. IEEE Trans. Med. Imaging 37(1), 20\u201334 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"5_CR11","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1109\/TMI.2003.816960","volume":"22","author":"J Nuyts","year":"2003","unstructured":"Nuyts, J., Fessler, J.: A penalized-likelihood image reconstruction method for emission tomography, compared to post-smoothed maximum-likelihood with matched spatial resolution. IEEE Trans. Med. Imaging 22(9), 1042\u20131052 (2003)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"5_CR12","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S","volume":"42","author":"K Pruessmann","year":"1999","unstructured":"Pruessmann, K., Weiger, M., Scheidegger, M., Boesiger, P.: SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42(5), 952\u2013962 (1999)","journal-title":"Magn. Reson. Med."},{"issue":"2","key":"5_CR13","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1109\/TMI.1982.4307558","volume":"1","author":"L Shepp","year":"1982","unstructured":"Shepp, L., Vardi, Y.: Maximum likelihood reconstruction for emission tomography. IEEE Trans. Med. Imaging 1(2), 113\u2013122 (1982)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"5_CR14","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1109\/TMI.2010.2076827","volume":"30","author":"S Somayajula","year":"2011","unstructured":"Somayajula, S., Panagiotou, C., Rangarajan, A., Li, Q., Arridge, S., Leahy, R.: PET image reconstruction using information theoretic anatomical priors. IEEE Trans. Med. Imaging 30(3), 537\u2013549 (2011)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Sudarshan, V., Chen, Z., Awate, S.: Joint PET+MRI patch-based dictionary for Bayesian random field PET reconstruction. In: Medical Image Computing and Computer-Assisted Intervention, pp. 338\u2013346 (2018)","DOI":"10.1007\/978-3-030-00928-1_39"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Tang, J., Wang, Y., Yao, R., Ying, L.: Sparsity-based PET image reconstruction using MRI learned dictionaries. In: IEEE International Symposium on Biomedical Imaging, p. 1087 (2014)","DOI":"10.1109\/ISBI.2014.6868063"},{"key":"5_CR17","doi-asserted-by":"publisher","first-page":"R115","DOI":"10.1088\/0031-9155\/60\/4\/R115","volume":"60","author":"S Vandenberghe","year":"2015","unstructured":"Vandenberghe, S., Marsden, P.: PET-MRI: a review of challenges and solutions in the development of integrated multimodality imaging. Phys. Med. Biol. 60, R115\u2013R154 (2015)","journal-title":"Phys. Med. Biol."},{"issue":"4","key":"5_CR18","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Proc. 13(4), 600 (2004)","journal-title":"IEEE Trans. Image Proc."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32248-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:21:18Z","timestamp":1728519678000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32248-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322472","9783030322489"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32248-9_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","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":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"1730","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":"539","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":"31% - 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":"3.07","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":"6.31","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}