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This work develops a T1\u2010mapping framework for efficient abdominal coverage based on rapid T1 recovery curve (T1RC) sampling, slice\u2010selective inversion, optimized slice interleaving, and a convolutional neural network (CNN)\u2010based T1 estimation. The effect of reducing the T1RC sampling was evaluated by comparing T1 estimates from T1RC ranging from 0.63 to 2.0\u00a0s with reference T1 values obtained from T1RC\u2009=\u00a02.5\u20135\u00a0s. Slice interleaving methodologies were evaluated by comparing the T1 variation in abdominal organs across slices. The repeatability of the proposed framework was demonstrated by performing acquisition on test subjects across imaging sessions. Analysis of in vivo data based on retrospectively shortening the T1RC showed that with the CNN framework, a T1RC\u2009=\u00a00.84\u2009s yielded T1 estimates without significant changes in mean T1 (<jats:italic>p<\/jats:italic>\u00a0&gt;\u20090.05) or significant increase in T1 variability (<jats:italic>p<\/jats:italic>\u00a0&gt;\u20090.48) compared to the reference. Prospectively acquired data using T1RC\u2009=\u00a00.84\u2009s, an optimized slice interleaving scheme, and the CNN framework enabled 21 slices in a 20\u2009s BHP. Analyses across abdominal organs produced T1 values within 2% of the reference. Repeatability experiments yielded Pearson's correlation, repeatability coefficient, and coefficient of variation of 0.99, 2.5%, and 0.12%, respectively. The proposed T1 mapping framework provides full abdominal coverage within a single BHP.<\/jats:p>","DOI":"10.1002\/nbm.5266","type":"journal-article","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T02:00:37Z","timestamp":1727920837000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Accelerated 2D radial Look\u2010Locker T1 mapping using a deep learning\u2010based rapid inversion recovery sampling technique"],"prefix":"10.1002","volume":"37","author":[{"given":"Eze","family":"Ahanonu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering The University of Arizona  Tucson Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ute","family":"Goerke","sequence":"additional","affiliation":[{"name":"Siemens Medical Solutions USA  Tucson Arizona 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