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pith:DSXNOMYE

pith:2026:DSXNOMYEEVCXMFODBW2BAZ3ZL5
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SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis

Agamdeep S. Chopra, Caitlin Neher, Hesamoddin Jahanian, Juampablo E. Heras Rivera, Mehmet Kurt, Tianyi Ren

A disentangled quantized Half-UNet generates tau-PET images from T1-weighted and FLAIR MRI with top fidelity and Braak-stage accuracy.

arxiv:2602.22545 v3 · 2026-02-26 · cs.CV · cs.AI

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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

Across 17 evaluated models, the proposed DQ2H-MSE-Inf variant achieved the best raw PET fidelity and the strongest downstream Braak-stage performance, while remaining competitive on SUVR reconstruction and regional agreement.

C2weakest assumption

That the partial information decomposition components remain clinically meaningful and that the synthesized tau-PET does not introduce artifacts that would alter Braak staging or regional uptake interpretation in real patients.

C3one line summary

A Partial Information Decomposition-guided disentangled quantized Half-UNet synthesizes tau-PET from multimodal MRI and outperforms baselines on raw fidelity and Braak-stage tracking in ADNI-3 and OASIS-3 data.

Receipt and verification
First computed 2026-07-07T00:15:52.795604Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

1caed7330425457615c30db41067795f7338dec88f327952c4c0e4d847b34926

Aliases

arxiv: 2602.22545 · arxiv_version: 2602.22545v3 · doi: 10.48550/arxiv.2602.22545 · pith_short_12: DSXNOMYEEVCX · pith_short_16: DSXNOMYEEVCXMFOD · pith_short_8: DSXNOMYE
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DSXNOMYEEVCXMFODBW2BAZ3ZL5 \
  | 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: 1caed7330425457615c30db41067795f7338dec88f327952c4c0e4d847b34926
Canonical record JSON
{
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    "abstract_canon_sha256": "cf53d7ef7ad283c4a678ff407b927f29b3a091d7503ad44e7bd5efdec4cb80d0",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-02-26T02:37:38Z",
    "title_canon_sha256": "4b493bf80202429482751f6346d1e789e00b152ccd8f348581f47d4834dacd40"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 3
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}