pith:DSXNOMYE
SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis
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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\pithnumber{DSXNOMYEEVCXMFODBW2BAZ3ZL5}
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Record completeness
Claims
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.
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.
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
· · · · ·Agent API
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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