{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:41:47Z","timestamp":1773801707361,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Cardiac magnetic resonance (CMR) imaging is widely used to characterize cardiac morphology and function. To accelerate CMR imaging, various methods have been proposed to recover high-quality spatiotemporal CMR images from highly undersampled k-t space data. However, current CMR reconstruction techniques either fail to achieve satisfactory image quality or are restricted by the scarcity of ground truth data, leading to limited applicability in clinical scenarios. \nIn this work, we proposed MoCo\u2011INR, a new unsupervised method that integrates implicit neural representations (INR) with the conventional motion\u2011compensated (MoCo) framework. Using the explicit motion modeling and the continuous prior of INRs, our MoCo-INR can produce accurate cardiac motion decomposition and high-quality CMR reconstruction. Moreover, we present a new INR network architecture tailored to the CMR problem, which can greatly stabilize model optimization.\nExperiments on retrospective (i.e., simulated) datasets demonstrate the superiority of MoCo\u2011INR over state\u2011of\u2011the\u2011art methods, achieving fast convergence and fine\u2011detailed reconstructions at ultra\u2011high acceleration factors (e.g., 20x in VISTA sampling).\nIn addition, evaluations on prospective (i.e., real-acquired) free\u2011breathing CMR scans highlight its clinical practicality for real\u2011time imaging. Several ablation studies also confirm the effectiveness of critical components of MoCo-INR.<\/jats:p>","DOI":"10.1609\/aaai.v40i11.37914","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:50:36Z","timestamp":1773791436000},"page":"9529-9537","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Motion-Compensated Decomposition for Cardiac MRI Reconstruction via Neural Representation"],"prefix":"10.1609","volume":"40","author":[{"given":"Xuanyu","family":"Tian","sequence":"first","affiliation":[]},{"given":"Lixuan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Yuyao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hongjiang","family":"Wei","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37914\/41876","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37914\/41876","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:50:36Z","timestamp":1773791436000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37914"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i11.37914","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}