{"paper":{"title":"Accelerated Magnetic Resonance Thermometry in Presence of Uncertainties","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"math.NA","authors_text":"Christopher MacLellan, David Fuentes, Ganesh Rao, Jason Stafford, Jeffrey S. Weinberg, John Hazle, Reza Madankan, Samuel Fahrenholtz, Wolfgang Stefan","submitted_at":"2015-10-29T20:15:49Z","abstract_excerpt":"An accelerated model-based information theoretic approach is presented to perform the task of Magnetic Resonance (MR) thermal image reconstruction from a limited number of observed samples on k-space. The key idea of the proposed approach is to utilize information theoretic techniques to optimally detect samples of k-space that are information rich with respect to a model of the thermal data acquisition. These highly informative k-space samples are then used to refine the mathematical model and reconstruct the image. The information theoretic reconstruction is demonstrated retrospectively in d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.08875","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}