{"paper":{"title":"Circumventing the Curse of Dimensionality in Magnetic Resonance Fingerprinting through a Deep Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.med-ph","authors_text":"Claudia Testa, Daniel Remondini, Enrico Giampieri, Francesco Solera, Gastone Castellani, Leonardo Brizi, Marco Barbieri","submitted_at":"2018-11-28T10:13:27Z","abstract_excerpt":"MR fingerprinting (MRF) is a rapid growing approach for fast quantitave MRI. A typical drawback of dictionary-based MRF is its explosion in size as a function of the number of reconstructed parameters, according to the curse of dimensionality. Deep Neural Networks (NNs) have been proposed as a feasible alternative, but these approaches are still in their infancy.\n  We tested different NN pipelines on simulated data: we studied optimal training procedures by including different strategies of noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MR"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11477","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"}