{"oa_version":"Preprint","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","conference":{"name":"IEEE International Conference on Acoustics, Speech, and Signal Processing","location":"Barcelona, Spain","start_date":"2020-05-04","end_date":"2020-05-08"},"_id":"18249","citation":{"mla":"Weiss, Tomer, et al. “Joint Learning of Cartesian Undersampling and Reconstruction for Accelerated MRI.” <i>ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>, 9054542, IEEE, 2020, doi:<a href=\"https://doi.org/10.1109/icassp40776.2020.9054542\">10.1109/icassp40776.2020.9054542</a>.","apa":"Weiss, T., Vedula, S., Senouf, O., Michailovich, O., Zibulevsky, M., &#38; Bronstein, A. M. (2020). Joint learning of cartesian undersampling and reconstruction for accelerated MRI. In <i>ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>. Barcelona, Spain: IEEE. <a href=\"https://doi.org/10.1109/icassp40776.2020.9054542\">https://doi.org/10.1109/icassp40776.2020.9054542</a>","chicago":"Weiss, Tomer, Sanketh Vedula, Ortal Senouf, Oleg Michailovich, Michael Zibulevsky, and Alex M. Bronstein. “Joint Learning of Cartesian Undersampling and Reconstruction for Accelerated MRI.” In <i>ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>. IEEE, 2020. <a href=\"https://doi.org/10.1109/icassp40776.2020.9054542\">https://doi.org/10.1109/icassp40776.2020.9054542</a>.","short":"T. Weiss, S. Vedula, O. Senouf, O. Michailovich, M. Zibulevsky, A.M. Bronstein, in:, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2020.","ista":"Weiss T, Vedula S, Senouf O, Michailovich O, Zibulevsky M, Bronstein AM. 2020. Joint learning of cartesian undersampling and reconstruction for accelerated MRI. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics, Speech, and Signal Processing, 9054542.","ama":"Weiss T, Vedula S, Senouf O, Michailovich O, Zibulevsky M, Bronstein AM. Joint learning of cartesian undersampling and reconstruction for accelerated MRI. In: <i>ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>. IEEE; 2020. doi:<a href=\"https://doi.org/10.1109/icassp40776.2020.9054542\">10.1109/icassp40776.2020.9054542</a>","ieee":"T. Weiss, S. Vedula, O. Senouf, O. Michailovich, M. Zibulevsky, and A. M. Bronstein, “Joint learning of cartesian undersampling and reconstruction for accelerated MRI,” in <i>ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>, Barcelona, Spain, 2020."},"article_processing_charge":"No","arxiv":1,"date_created":"2024-10-08T13:05:24Z","type":"conference","date_updated":"2024-12-11T16:06:20Z","scopus_import":"1","author":[{"last_name":"Weiss","full_name":"Weiss, Tomer","first_name":"Tomer"},{"last_name":"Vedula","first_name":"Sanketh","full_name":"Vedula, Sanketh"},{"last_name":"Senouf","full_name":"Senouf, Ortal","first_name":"Ortal"},{"first_name":"Oleg","full_name":"Michailovich, Oleg","last_name":"Michailovich"},{"full_name":"Zibulevsky, Michael","first_name":"Michael","last_name":"Zibulevsky"},{"full_name":"Bronstein, Alexander","orcid":"0000-0001-9699-8730","first_name":"Alexander","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"}],"publication_identifier":{"eissn":["2379-190X"],"isbn":["9781509066322"]},"date_published":"2020-04-09T00:00:00Z","title":"Joint learning of cartesian undersampling and reconstruction for accelerated MRI","publisher":"IEEE","language":[{"iso":"eng"}],"publication_status":"published","publication":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","status":"public","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1905.09324"}],"day":"09","quality_controlled":"1","abstract":[{"lang":"eng","text":"Magnetic Resonance Imaging (MRI) is considered today the golden-standard modality for soft tissues. The long acquisition times, however, make it more prone to motion artifacts as well as contribute to the relative high costs of this examination. Over the years, multiple studies concentrated on designing reduced measurement schemes and image reconstruction schemes for MRI, however these problems have been so far addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of simultaneous learning-based design of the acquisition and reconstruction schemes manifesting significant improvement in the reconstruction quality with a constrained time budget. Inspired by these successes, in this work, we propose to learn accelerated MR acquisition schemes (in the form of Cartesian trajectories) jointly with the image reconstruction operator. To this end, we propose an algorithm for training the combined acquisition-reconstruction pipeline end-to-end in a differentiable way. We demonstrate the significance of using the learned Cartesian trajectories at different speed up rates. Code available at https://github.com/tomer196/fastMRI-Cartesian."}],"doi":"10.1109/icassp40776.2020.9054542","extern":"1","oa":1,"article_number":"9054542","month":"04","external_id":{"arxiv":["1905.09324"]},"year":"2020"}