{"publication":"Computational Diffusion MRI","publisher":"Springer Nature","day":"30","place":"Cham","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2009.03008","open_access":"1"}],"doi":"10.1007/978-3-030-73018-5_2","OA_place":"repository","oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","alternative_title":["Mathematics and Visualization"],"editor":[{"last_name":"Gyori","first_name":"Noemi","full_name":"Gyori, Noemi"},{"full_name":"Hutter, Jana","last_name":"Hutter","first_name":"Jana"},{"full_name":"Nath, Vishwesh","first_name":"Vishwesh","last_name":"Nath"},{"last_name":"Palombo","first_name":"Marco","full_name":"Palombo, Marco"},{"last_name":"Pizzolato","first_name":"Marco","full_name":"Pizzolato, Marco"},{"full_name":"Zhang, Fan","last_name":"Zhang","first_name":"Fan"}],"extern":"1","date_created":"2024-10-08T13:03:26Z","type":"book_chapter","conference":{"location":"Lima, Peru/Virtual","name":"MICCAI: Conference on Medical Image Computing and Computer-Assisted Intervention","start_date":"2020-10-08","end_date":"2020-10-08"},"page":"13-28","OA_type":"green","oa":1,"quality_controlled":"1","publication_identifier":{"isbn":["9783030730178"],"eisbn":["9783030730185"],"issn":["1612-3786"]},"author":[{"first_name":"Tomer","last_name":"Weiss","full_name":"Weiss, Tomer"},{"full_name":"Vedula, Sanketh","first_name":"Sanketh","last_name":"Vedula"},{"first_name":"Ortal","last_name":"Senouf","full_name":"Senouf, Ortal"},{"first_name":"Oleg","last_name":"Michailovich","full_name":"Michailovich, Oleg"},{"orcid":"0000-0001-9699-8730","last_name":"Bronstein","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander"}],"language":[{"iso":"eng"}],"month":"09","date_published":"2021-09-30T00:00:00Z","publication_status":"published","status":"public","citation":{"mla":"Weiss, Tomer, et al. “Towards Learned Optimal Q-Space Sampling in Diffusion MRI.” <i>Computational Diffusion MRI</i>, edited by Noemi Gyori et al., Springer Nature, 2021, pp. 13–28, doi:<a href=\"https://doi.org/10.1007/978-3-030-73018-5_2\">10.1007/978-3-030-73018-5_2</a>.","apa":"Weiss, T., Vedula, S., Senouf, O., Michailovich, O., &#38; Bronstein, A. M. (2021). Towards learned optimal q-space sampling in diffusion MRI. In N. Gyori, J. Hutter, V. Nath, M. Palombo, M. Pizzolato, &#38; F. Zhang (Eds.), <i>Computational Diffusion MRI</i> (pp. 13–28). Cham: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-73018-5_2\">https://doi.org/10.1007/978-3-030-73018-5_2</a>","ieee":"T. Weiss, S. Vedula, O. Senouf, O. Michailovich, and A. M. Bronstein, “Towards learned optimal q-space sampling in diffusion MRI,” in <i>Computational Diffusion MRI</i>, N. Gyori, J. Hutter, V. Nath, M. Palombo, M. Pizzolato, and F. Zhang, Eds. Cham: Springer Nature, 2021, pp. 13–28.","ista":"Weiss T, Vedula S, Senouf O, Michailovich O, Bronstein AM. 2021.Towards learned optimal q-space sampling in diffusion MRI. In: Computational Diffusion MRI. Mathematics and Visualization, , 13–28.","chicago":"Weiss, Tomer, Sanketh Vedula, Ortal Senouf, Oleg Michailovich, and Alex M. Bronstein. “Towards Learned Optimal Q-Space Sampling in Diffusion MRI.” In <i>Computational Diffusion MRI</i>, edited by Noemi Gyori, Jana Hutter, Vishwesh Nath, Marco Palombo, Marco Pizzolato, and Fan Zhang, 13–28. Cham: Springer Nature, 2021. <a href=\"https://doi.org/10.1007/978-3-030-73018-5_2\">https://doi.org/10.1007/978-3-030-73018-5_2</a>.","short":"T. Weiss, S. Vedula, O. Senouf, O. Michailovich, A.M. Bronstein, in:, N. Gyori, J. Hutter, V. Nath, M. Palombo, M. Pizzolato, F. Zhang (Eds.), Computational Diffusion MRI, Springer Nature, Cham, 2021, pp. 13–28.","ama":"Weiss T, Vedula S, Senouf O, Michailovich O, Bronstein AM. Towards learned optimal q-space sampling in diffusion MRI. In: Gyori N, Hutter J, Nath V, Palombo M, Pizzolato M, Zhang F, eds. <i>Computational Diffusion MRI</i>. Cham: Springer Nature; 2021:13-28. doi:<a href=\"https://doi.org/10.1007/978-3-030-73018-5_2\">10.1007/978-3-030-73018-5_2</a>"},"title":"Towards learned optimal q-space sampling in diffusion MRI","_id":"18242","date_updated":"2024-10-16T09:51:45Z","article_processing_charge":"No","abstract":[{"lang":"eng","text":"Fiber tractography is an important tool of computational neuroscience that enables reconstructing the spatial connectivity and organization of white matter of the brain. Fiber tractography takes advantage of diffusion Magnetic Resonance Imaging (dMRI) which allows measuring the apparent diffusivity of cerebral water along different spatial directions. Unfortunately, collecting such data comes at the price of reduced spatial resolution and substantially elevated acquisition times, which limits the clinical applicability of dMRI. This problem has been thus far addressed using two principal strategies. Most of the efforts have been extended towards improving the quality of signal estimation for any, yet fixed sampling scheme (defined through the choice of diffusion-encoding gradients). On the other hand, optimization over the sampling scheme has also proven to be effective. Inspired by the previous results, the present work consolidates the above strategies into a unified estimation framework, in which the optimization is carried out with respect to both estimation model and sampling design concurrently. The proposed solution offers substantial improvements in the quality of signal estimation as well as the accuracy of ensuing analysis by means of fiber tractography. While proving the optimality of the learned estimation models would probably need more extensive evaluation, we nevertheless claim that the learned sampling schemes can be of immediate use, offering a way to improve the dMRI analysis without the necessity of deploying the neural network used for their estimation. We present a comprehensive comparative analysis based on the Human Connectome Project data. Code and learned sampling designs available at https://github.com/tomer196/Learned_dMRI."}],"external_id":{"arxiv":["2009.03008"]},"scopus_import":"1","related_material":{"link":[{"relation":"software","url":"https://github.com/tomer196/Learned_dMRI"}]},"year":"2021"}