Pith Number

pith:6JZ5634J

pith:2025:6JZ5634JBW7B2NLBVWIOJTOTXX

not attested not anchored not stored refs resolved

Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm

Jonathan I. Tamir, Martin Uecker, Moritz Blumenthal, Tina Holliber

Preconditioning the unadjusted Langevin algorithm enables fast, robust posterior sampling for diffusion-based MRI reconstruction from undersampled data.

arxiv:2512.05791 v2 · 2025-12-05 · physics.med-ph · cs.CV · cs.LG · math.PR

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\usepackage{pith}
\pithnumber{6JZ5634JBW7B2NLBVWIOJTOTXX}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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

For posterior sampling in Cartesian and non-Cartesian accelerated MRI the new approach outperforms annealed sampling and DPS in terms of reconstruction speed and sample quality.

C2weakest assumption

That the preconditioner derived for the reverse diffusion process remains effective and stable across different acceleration factors, trajectory types, and anatomical regions without retuning or retraining.

C3one line summary

Preconditioned ULA with exact likelihood enables faster, higher-quality posterior sampling for Cartesian and non-Cartesian MRI reconstructions than annealed sampling or DPS.

References

31 extracted · 31 resolved · 2 Pith anchors

[1] Robust compressed sensing MRI with deep generative priors 2021

[2] Score-based diffusion models for accelerated MRI

[3] Bayesian MRI reconstruc- tion with joint uncertainty estimation using diffusion models.Magnetic Resonance in Medicine2023; 90:295–311

[4] Generative Modeling by Estimating Gradients of the Data Distribution 2019

[5] Denoising diffusion probabilistic models 2020

Receipt and verification

First computed	2026-05-26T01:03:19.374405Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f273df6f890dbe1d3561ad90e4cdd3bdfa5214528d49d2e0d5356a57244b3260

Aliases

arxiv: 2512.05791 · arxiv_version: 2512.05791v2 · doi: 10.48550/arxiv.2512.05791 · pith_short_12: 6JZ5634JBW7B · pith_short_16: 6JZ5634JBW7B2NLB · pith_short_8: 6JZ5634J

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/6JZ5634JBW7B2NLBVWIOJTOTXX \
  | 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: f273df6f890dbe1d3561ad90e4cdd3bdfa5214528d49d2e0d5356a57244b3260

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2c46fb6674250f933f27b36c6d7aa15ec50a32365458f577390e6165d3880fc1",
    "cross_cats_sorted": [
      "cs.CV",
      "cs.LG",
      "math.PR"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "physics.med-ph",
    "submitted_at": "2025-12-05T15:17:29Z",
    "title_canon_sha256": "e50d015cc3a42f39cef0a46079884772b721690a92be2ff65e218ac3961607a1"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2512.05791",
    "kind": "arxiv",
    "version": 2
  }
}