pith:QIWCR4VN
Product-of-Gaussian-Mixture Diffusion Models for Joint Nonlinear MRI Reconstruction
A compact product-of-Gaussian-mixture diffusion model acts as an image prior for joint reconstruction of MRI images and coil sensitivities.
arxiv:2605.10629 v1 · 2026-05-11 · cs.CV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{QIWCR4VNM3DSZRVAZZJA3YVGZQ}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
Claims
We jointly reconstruct the image and the coil sensitivities by combining the parameter-efficient product-of-Gaussian-mixture diffusion model as an image prior with a classical smoothness prior on the coil sensitivities. The proposed method is fast and robust to both contrast and anatomical distribution shifts as well as changing k-space trajectories. Finally, we propose a more expressive parameterization of the image prior which improves results in denoising and magnetic resonance image reconstruction.
That the product-of-Gaussian-mixture diffusion model provides a sufficiently general and effective prior for MRI images across varying contrasts and anatomies, and that a classical smoothness prior is adequate to model coil sensitivities without introducing reconstruction artifacts or requiring additional constraints.
A joint MRI reconstruction method using product-of-Gaussian-mixture diffusion models for the image prior and smoothness priors for coil sensitivities, with an improved parameterization for better denoising and reconstruction.
References
Formal links
Receipt and verification
| First computed | 2026-06-30T02:17:22.695263Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
822c28f2ad66c72cc6a0ce520de2a6cc2bba8065d337f170e855ef50ed0ccc68
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/QIWCR4VNM3DSZRVAZZJA3YVGZQ \
| 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: 822c28f2ad66c72cc6a0ce520de2a6cc2bba8065d337f170e855ef50ed0ccc68
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "9c6bef00da3e3cb93aaa644d00e4a8b2a85e6176bb1b6f7e3fdcd7558c16b7a7",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CV",
"submitted_at": "2026-05-11T14:20:30Z",
"title_canon_sha256": "b51cf58296a9e5f43ab27a184910cf425dc2a5b8d26cc4b4be1eb67dee8e8734"
},
"schema_version": "1.0",
"source": {
"id": "2605.10629",
"kind": "arxiv",
"version": 1
}
}