{"paper":{"title":"Fast Reconstruction of Motion-Corrupted Data with Mobile-GRAPPA: Motion and dB0 Inhomogeneity Correction Leveraging Efficient GRAPPA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Mobile-GRAPPA cleans motion and dB0 corrupted k-space data with position-specific GRAPPA kernels so standard SENSE can reconstruct it quickly and accurately.","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Aizada Nurdinova, Daniel Abraham, Daniel Polak, Kawin Setsompop, Nan Wang, Stephen Cauley, Xiaozhi Cao, Yimeng Lin","submitted_at":"2025-11-09T07:16:41Z","abstract_excerpt":"Advanced motion navigations now enable rapid tracking of subject motion and dB0-induced phase, but accurately incorporating this high-temporal-resolution information into SENSE (Aligned-SENSE) is often computationally prohibitive. We propose \"Mobile-GRAPPA\", a k-space \"cleaning\" approach that uses local GRAPPA operators to remove motion and dB0 related corruption so that the resulting data can be reconstructed with standard SENSE. We efficiently train a family of k-space-position-specific Mobile-GRAPPA kernels via a lightweight multilayer perceptron (MLP) and apply them across k-space to gener"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Mobile-GRAPPA enabled accurate reconstruction with negligible time penalty, whereas full Aligned-SENSE was impractical (reconstruction times > 10 h for GRE and > 10 days for EPTI).","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a family of k-space-position-specific GRAPPA kernels trained by a lightweight MLP can remove motion and dB0 corruption accurately enough that standard SENSE produces images free of new artifacts introduced by the cleaning step.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Mobile-GRAPPA trains local GRAPPA operators with an MLP to clean motion- and dB0-corrupted k-space so that standard SENSE can reconstruct high-quality images from long, motion-prone scans in minutes rather than hours or days.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Mobile-GRAPPA cleans motion and dB0 corrupted k-space data with position-specific GRAPPA kernels so standard SENSE can reconstruct it quickly and accurately.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"cdcc03aa0fc9d485bba7feb3df2060c9b4a1a782c37fd1a2788f3f2ade623a96"},"source":{"id":"2511.06257","kind":"arxiv","version":2},"verdict":{"id":"48e109f7-6ac2-4796-b251-32f2db65caff","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T00:02:29.750285Z","strongest_claim":"Mobile-GRAPPA enabled accurate reconstruction with negligible time penalty, whereas full Aligned-SENSE was impractical (reconstruction times > 10 h for GRE and > 10 days for EPTI).","one_line_summary":"Mobile-GRAPPA trains local GRAPPA operators with an MLP to clean motion- and dB0-corrupted k-space so that standard SENSE can reconstruct high-quality images from long, motion-prone scans in minutes rather than hours or days.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a family of k-space-position-specific GRAPPA kernels trained by a lightweight MLP can remove motion and dB0 corruption accurately enough that standard SENSE produces images free of new artifacts introduced by the cleaning step.","pith_extraction_headline":"Mobile-GRAPPA cleans motion and dB0 corrupted k-space data with position-specific GRAPPA kernels so standard SENSE can reconstruct it quickly and accurately."},"references":{"count":18,"sample":[{"doi":"","year":null,"title":"Department of Electrical Engineering, Stanford University, Stanford, CA, USA","work_id":"5235be0f-96ad-47ba-b2d1-37eb16d3d193","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Department of Radiology, Stanford University, Stanford, CA, USA","work_id":"7a98d28e-4e72-42c7-985b-bd862aed4531","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Siemens Medical Solutions USA, Inc., Malvern, USA Abstract Purpose: Advanced motion-navigations are now enabling rapid-tracking of subject’s motion and dB0-phase, but accurately incorporating such inf","work_id":"a9d8e55d-c77b-42df-8679-daa6d15736b3","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1002/mrm.29967","year":2024,"title":"Servo navigators: linear regression and feedback control for rigid‐body motion correction[J]","work_id":"0c6fa025-36d6-4f75-813c-b77290c02b13","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1002/mrm.29976","year":2024,"title":"Rapid and accurate navigators for motion and B 0 tracking using QUEEN: Quantitatively enhanced parameter estimation from navigators[J]","work_id":"bf53cb76-9c82-4411-9f99-e91330c1216a","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":18,"snapshot_sha256":"4a62498b67d4d679f69f0160486fd3e64f7de1bd698e0d7689b3a1977a10f8df","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"2e1f9a148057728a5c80d6a3b664c29ade0ff09d345845d7aad7adfb33714daf"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}