Pith Number

pith:SJ6V5MBN

pith:2026:SJ6V5MBNR6EM4XXFBUYGCT2KV5

not attested not anchored not stored refs resolved

Layer Selection in Feature-Based Losses Affects Image Quality and Microstructural Consistency in Deep Learning Super-Resolution of Brain Diffusion MRI

David Lohr, Rene Werner

Choosing the shallowest VGG16 layer for feature-based losses avoids grid-like artifacts in super-resolved brain diffusion MRI and maintains microstructural consistency.

arxiv:2605.15895 v1 · 2026-05-15 · eess.IV · cs.CV

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{SJ6V5MBNR6EM4XXFBUYGCT2KV5}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

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

Deeper layers and combinations thereof resulted in grid-like artifacts in super-resolution DWIs, which persisted in diffusion parameters like quantitative and fractional anisotropy. No such artifacts were present when using the shallowest layer. Downstream analysis for this layer showed great consistency with the ground truth, even for 9-fold super-resolution.

C2weakest assumption

The ablation and isolation studies sufficiently isolate the effect of VGG16 layer depth from other training choices such as optimizer settings, data augmentation, or network capacity, so that the observed grid artifacts can be attributed primarily to layer selection.

C3one line summary

Deeper VGG16 layers in feature losses for diffusion MRI super-resolution introduce persistent grid artifacts in images and anisotropy maps, whereas the shallowest layer preserves consistency with ground truth at high upsampling factors.

References

41 extracted · 41 resolved · 3 Pith anchors

[1] Salat, D.H. (2014). Chapter 12 - Diffusion Tensor Imaging in the Study of Aging and Age-Associated Neural Disease. In Diffusion MRI (Second Edition), H. Johansen-Berg, and T.E.J. Behrens, eds. (Academ 2014 · doi:10.1016/b978-0-12-396460-1.00012-3

[2] Goveas, J., O'Dwyer, L., Mascalchi, M., Cosottini, M., Diciotti, S., De Santis, S., Passamonti, L., Tessa, C., Toschi, N., and Giannelli, M. (2015). Diffusion-MRI in neurodegenerative disorders. Magne 2015 · doi:10.1016/j.mri.2015.04.006

[3] Tournier, J.D. (2019). Diffusion MRI in the brain - Theory and concepts. Prog Nucl Magn Reson Spectrosc 112-113, 1–16. 10.1016/j.pnmrs.2019.03.001 2019 · doi:10.1016/j.pnmrs.2019.03.001

[4] Lerch, J.P., van der Kouwe, A.J.W., Raznahan, A., Paus, T., Johansen-Berg, H., Miller, K.L., Smith, S.M., Fischl, B., and Sotiropoulos, S.N. (2017). Studying neuroanatomy using MRI. Nature Neuroscienc 2017 · doi:10.1038/nn.4501

[5] Van Essen, Stephen M 2013 · doi:10.1016/j.neuroimage.2013.05.041

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:24.221464Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

927d5eb02d8f88ce5ee50d30614f4aaf4889be38990b985599e5301f7ebc5a42

Aliases

arxiv: 2605.15895 · arxiv_version: 2605.15895v1 · doi: 10.48550/arxiv.2605.15895 · pith_short_12: SJ6V5MBNR6EM · pith_short_16: SJ6V5MBNR6EM4XXF · pith_short_8: SJ6V5MBN

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/SJ6V5MBNR6EM4XXFBUYGCT2KV5 \
  | 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: 927d5eb02d8f88ce5ee50d30614f4aaf4889be38990b985599e5301f7ebc5a42

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ea135cec810960c662be5868ef04fbfaa72ed8a5b15ee567c67caaa693738c85",
    "cross_cats_sorted": [
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "eess.IV",
    "submitted_at": "2026-05-15T12:23:40Z",
    "title_canon_sha256": "4f39bcaebc394611bb316f3cb829e4ea857d5d22decc56b2456b15163081db61"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15895",
    "kind": "arxiv",
    "version": 1
  }
}