{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:59:48Z","timestamp":1742936388104,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031723834"},{"type":"electronic","value":"9783031723841"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72384-1_44","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T11:02:53Z","timestamp":1727866973000},"page":"467-476","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The MRI Scanner as\u00a0a\u00a0Diagnostic: Image-Less Active Sampling"],"prefix":"10.1007","author":[{"given":"Yuning","family":"Du","sequence":"first","affiliation":[]},{"given":"Rohan","family":"Dharmakumar","sequence":"additional","affiliation":[]},{"given":"Sotirios A.","family":"Tsaftaris","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Bahadir, C.D., Wang, A.Q., Dalca, A.V., Sabuncu, M.R.: Deep-learning-based optimization of the under-sampling pattern in MRI. IEEE Trans. Comput. Imaging 6, 1139\u20131152 (2020)","DOI":"10.1109\/TCI.2020.3006727"},{"key":"44_CR2","unstructured":"Bakker, T., van Hoof, H., Welling, M.: Experimental design for MRI by greedy policy search. Adv. Neural Inf. Process. Syst. 33, 18954\u201318966 (2020)"},{"key":"44_CR3","doi-asserted-by":"crossref","unstructured":"Cai, L., Gao, J., Zhao, D.: A review of the application of deep learning in medical image classification and segmentation. Ann. Transl. Med. 8(11), 713 (2020)","DOI":"10.21037\/atm.2020.02.44"},{"issue":"7","key":"44_CR4","doi-asserted-by":"publisher","first-page":"e65","DOI":"10.1002\/jmri.26638","volume":"49","author":"S Geethanath","year":"2019","unstructured":"Geethanath, S., Vaughan\u00a0Jr, J.T.: Accessible magnetic resonance imaging: a review. Journal of Magnetic Resonance Imaging 49(7), e65\u2013e77 (2019)","journal-title":"J. Magn. Reson. Imaging"},{"key":"44_CR5","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision - ECCV 2016, ECCV 2016, LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"44_CR6","unstructured":"Kool, W., van Hoof, H., Welling, M.: Buy 4 reinforce samples, get a baseline for free! (2019)"},{"key":"44_CR7","unstructured":"Kuperman, V.: Magnetic resonance imaging: physical principles and applications. Elsevier (2000)"},{"key":"44_CR8","doi-asserted-by":"crossref","unstructured":"Lin, D.J., Johnson, P.M., Knoll, F., Lui, Y.W.: Artificial intelligence for MR image reconstruction: an overview for clinicians. J. Magn. Reson. Imaging 53(4), 1015\u20131028 (2021)","DOI":"10.1002\/jmri.27078"},{"issue":"1","key":"44_CR9","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1038\/s41597-023-02181-4","volume":"10","author":"M Lyu","year":"2023","unstructured":"Lyu, M., et al.: M4raw: a multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research. Sci. Data 10(1), 264 (2023)","journal-title":"Sci. Data"},{"key":"44_CR10","unstructured":"Massimiliano, L., et\u00a0al.: Role of low field MRI in detecting knee lesions. Acta Bio Medica: Atenei Parmensis 90(Suppl 1), \u00a0116 (2019)"},{"key":"44_CR11","doi-asserted-by":"publisher","unstructured":"Pineda, L., Basu, S., Romero, A., Calandra, R., Drozdzal, M.: Active MR k-space sampling with reinforcement learning. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020, MICCAI 2020, Part II, LNCS, vol. 12262, pp 23\u201333. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_3","DOI":"10.1007\/978-3-030-59713-9_3"},{"key":"44_CR12","unstructured":"Ravula, S., Levac, B., Jalal, A., Tamir, J.I., Dimakis, A.G.: Optimizing sampling patterns for compressed sensing MRI with diffusion generative models. arXiv preprint arXiv:2306.03284 (2023)"},{"key":"44_CR13","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, MICCAI 2015, Part III, LNCS, vol. 9351, pp 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"44_CR14","doi-asserted-by":"publisher","unstructured":"Schlemper, J., et al.: Cardiac MR segmentation from undersampled k-space using deep latent representation learning. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, MICCAI 2018, Part I, LNCS, vol. 11070, pp. 259\u2013267. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_30","DOI":"10.1007\/978-3-030-00928-1_30"},{"key":"44_CR15","unstructured":"Singhal, R., et al.: On the feasibility of machine learning augmented magnetic resonance for point-of-care identification of disease. arXiv preprint arXiv:2301.11962 (2023)"},{"key":"44_CR16","unstructured":"Sondik, E.J.: The Optimal Control of Partially Observable Markov Processes. Stanford University, Stanford (1971)"},{"key":"44_CR17","doi-asserted-by":"publisher","first-page":"107150","DOI":"10.1016\/j.cmpb.2022.107150","volume":"226","author":"J Wang","year":"2022","unstructured":"Wang, J., Yang, Q., Yang, Q., Xu, L., Cai, C., Cai, S.: Joint optimization of cartesian sampling patterns and reconstruction for single-contrast and multi-contrast fast magnetic resonance imaging. Computer Methods and Programs in Biomedicine 226, 107150 (2022)","journal-title":"Comput. Methods Program. Biomed."},{"key":"44_CR18","doi-asserted-by":"publisher","unstructured":"Xuan, K., Sun, S., Xue, Z., Wang, Q., Liao, S.: Learning MRI k-space subsampling pattern using progressive weight pruning. In: Martel, A.L., et al. (ed.) Medical Image Computing and Computer Assisted Intervention - MICCAI 2020. MICCAI 2020, Part II, LNCS, vol. 12262, pp. 178\u2013187. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_18","DOI":"10.1007\/978-3-030-59713-9_18"},{"key":"44_CR19","unstructured":"Zbontar, J., et\u00a0al.: fastMRI: an open dataset and benchmarks for accelerated MRI. arXiv preprint arXiv:1811.08839 (2018)"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Romero, A., Muckley, M.J., Vincent, P., Yang, L., Drozdzal, M.: Reducing uncertainty in undersampled MRI reconstruction with active acquisition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2049\u20132058 (2019)","DOI":"10.1109\/CVPR.2019.00215"},{"key":"44_CR21","doi-asserted-by":"crossref","unstructured":"Zhao, R., et al.: fastMRI+: clinical pathology annotations for knee and brain fully sampled multi-coil MRI data. arXiv preprint arXiv:2109.03812 (2021)","DOI":"10.1038\/s41597-022-01255-z"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72384-1_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T11:18:46Z","timestamp":1727867926000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72384-1_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723834","9783031723841"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72384-1_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}