Pith Number

pith:5DEESD76

pith:2025:5DEESD76ZBUKIIWKKIP5I53IHL

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Fast Reconstruction of Motion-Corrupted Data with Mobile-GRAPPA: Motion and dB0 Inhomogeneity Correction Leveraging Efficient GRAPPA

Aizada Nurdinova, Daniel Abraham, Daniel Polak, Kawin Setsompop, Nan Wang, Stephen Cauley, Xiaozhi Cao, Yimeng Lin

Mobile-GRAPPA cleans motion and dB0 corrupted k-space data with position-specific GRAPPA kernels so standard SENSE can reconstruct it quickly and accurately.

arxiv:2511.06257 v2 · 2025-11-09 · eess.SP

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\pithnumber{5DEESD76ZBUKIIWKKIP5I53IHL}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest 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).

C2weakest 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.

C3one 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.

References

18 extracted · 18 resolved · 0 Pith anchors

[1] Department of Electrical Engineering, Stanford University, Stanford, CA, USA

[2] Department of Radiology, Stanford University, Stanford, CA, USA

[3] 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

[4] Servo navigators: linear regression and feedback control for rigid‐body motion correction[J] 2024 · doi:10.1002/mrm.29967

[5] Rapid and accurate navigators for motion and B 0 tracking using QUEEN: Quantitatively enhanced parameter estimation from navigators[J] 2024 · doi:10.1002/mrm.29976

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:09:33.450041Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e8c8490ffec868a422ca521fd477683affb7af8dcabe92466d2b9f983d27b0d3

Aliases

arxiv: 2511.06257 · arxiv_version: 2511.06257v2 · doi: 10.48550/arxiv.2511.06257 · pith_short_12: 5DEESD76ZBUK · pith_short_16: 5DEESD76ZBUKIIWK · pith_short_8: 5DEESD76

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/5DEESD76ZBUKIIWKKIP5I53IHL \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e8c8490ffec868a422ca521fd477683affb7af8dcabe92466d2b9f983d27b0d3

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4c0248867af9ed05a259699c1691103a04de90ec29001a05cf2cae5ec3ef67e8",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2025-11-09T07:16:41Z",
    "title_canon_sha256": "6a9a051c2deb6230bd3168fee18a42906575fc1e64375a768851c505a750566f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2511.06257",
    "kind": "arxiv",
    "version": 2
  }
}