[{"language":[{"iso":"eng"}],"date_created":"2026-06-07T22:01:36Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2026","_id":"21949","ddc":["000"],"intvolume":"       315","publication":"Medical Imaging with Deep Learning","type":"conference","abstract":[{"text":"Cardiac T1 mapping provides critical quantitative insights into myocardial tissue composition, enabling the assessment of pathologies such as fibrosis, inflammation, and edema.\r\nHowever, the inherently dynamic nature of the heart imposes strict limits on acquisition\r\ntimes, making high-resolution T1 mapping a persistent challenge. Compressed sensing (CS)\r\napproaches have reduced scan durations by undersampling k-space and reconstructing images from partial data, and recent studies show that jointly optimizing the undersampling\r\npatterns with the reconstruction network can substantially improve performance. Still,\r\nmost current T1 mapping pipelines rely on static, hand-crafted masks that do not exploit\r\nthe full acceleration and accuracy potential. Furthermore, most existing methods do not\r\nlevarage the physical T1 decay model in optimization. In this work, we introduce T1-\r\nPILOT: an end-to-end method that explicitly incorporates the T1 signal relaxation model\r\ninto the sampling–reconstruction framework to guide the learning of non-Cartesian trajectories, cross-frame alignment, and T1 decay estimation. Through extensive experiments\r\non the CMRxRecon dataset, T1-PILOT significantly outperforms several baseline strategies (including learned single-mask and fixed radial or golden-angle sampling schemes),\r\nachieving higher T1 map fidelity at greater acceleration factors. In particular, we observe consistent gains in PSNR and VIF relative to existing methods, along with marked\r\nimprovements in delineating finer myocardial structures. Our results highlight that optimizing sampling trajectories in tandem with the physical relaxation model leads to both\r\nenhanced quantitative accuracy and reduced acquisition times. Code for reproducing all\r\nexperiments and results is available at https://github.com/tamirshor7/T1-PILOT","lang":"eng"}],"title":"T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration","page":"1969-1982","alternative_title":["PMLR"],"publication_status":"accepted","main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=nZaPtHbd6N#discussion"}],"oa":1,"OA_place":"publisher","related_material":{"link":[{"relation":"software","url":"https://github.com/tamirshor7/T1-PILOT"}]},"tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"volume":315,"date_published":"2026-03-17T00:00:00Z","article_processing_charge":"No","has_accepted_license":"1","author":[{"last_name":"Shor","full_name":"Shor, Tamir","first_name":"Tamir"},{"first_name":"Moti","last_name":"Freiman","full_name":"Freiman, Moti"},{"first_name":"Chaim","last_name":"Baskin","full_name":"Baskin, Chaim"},{"first_name":"Alexander","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","orcid":"0000-0001-9699-8730"}],"status":"public","publication_identifier":{"eissn":["2640-3498"]},"department":[{"_id":"AlBr"}],"corr_author":"1","date_updated":"2026-06-08T08:05:24Z","citation":{"ama":"Shor T, Freiman M, Baskin C, Bronstein AM. T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration. In: <i>Medical Imaging with Deep Learning</i>. Vol 315. ML Research Press; :1969-1982.","ieee":"T. Shor, M. Freiman, C. Baskin, and A. M. Bronstein, “T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration,” in <i>Medical Imaging with Deep Learning</i>, Taipei, Taiwan, vol. 315, pp. 1969–1982.","chicago":"Shor, Tamir, Moti Freiman, Chaim Baskin, and Alex M. Bronstein. “T1-PILOT: Physics-Informed Learned Optimized Trajectories for T1 Mapping Acceleration.” In <i>Medical Imaging with Deep Learning</i>, 315:1969–82. ML Research Press, n.d.","mla":"Shor, Tamir, et al. “T1-PILOT: Physics-Informed Learned Optimized Trajectories for T1 Mapping Acceleration.” <i>Medical Imaging with Deep Learning</i>, vol. 315, ML Research Press, pp. 1969–82.","apa":"Shor, T., Freiman, M., Baskin, C., &#38; Bronstein, A. M. (n.d.). T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration. In <i>Medical Imaging with Deep Learning</i> (Vol. 315, pp. 1969–1982). Taipei, Taiwan: ML Research Press.","ista":"Shor T, Freiman M, Baskin C, Bronstein AM. T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration. Medical Imaging with Deep Learning. MIDL: Medical Imaging with Deep Learning, PMLR, vol. 315, 1969–1982.","short":"T. Shor, M. Freiman, C. Baskin, A.M. Bronstein, in:, Medical Imaging with Deep Learning, ML Research Press, n.d., pp. 1969–1982."},"publisher":"ML Research Press","OA_type":"gold","scopus_import":"1","oa_version":"Published Version","quality_controlled":"1","conference":{"start_date":"2026-07-08","name":"MIDL: Medical Imaging with Deep Learning","end_date":"2026-07-10","location":"Taipei, Taiwan"},"keyword":["Cardiac T1 Mapping","Trajectory Optimization and Reconstruction","PhysicsInformed Deep-Learning"],"month":"03","day":"17"}]