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pith:7SRUL52J

pith:2026:7SRUL52JFDOACG2QDPOUYRQ4FB

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Deep Learning for MRI Slice Interpolation: The Critical Role of Problem Formulation

Shamit Savant

Reformulating MRI slice inputs from distant to adjacent slices improves interpolation far more than model complexity.

arxiv:2605.16476 v1 · 2026-05-15 · eess.IV · cs.CV · cs.LG

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Claims

C1strongest claim

By reformulating the interpolation task to use adjacent slices (i-1, i+1) rather than distant slices (i-2, i+2), I achieved a 58% improvement in SSIM performance across all deterministic architectures. The U-Net model achieved the best results with PSNR of 30.08 dB and SSIM of 0.898, representing a 10.1% improvement over linear interpolation baseline.

C2weakest assumption

The reported performance gains are attributable primarily to the choice of adjacent versus distant input slices rather than differences in training procedures, hyperparameter tuning, or dataset characteristics across the compared formulations.

C3one line summary

Reformulating the input to adjacent slices for deep learning MRI interpolation yields 58% SSIM gains and 10.1% improvement over linear baseline, with problem formulation outweighing architecture choice.

References

13 extracted · 13 resolved · 7 Pith anchors

[1] The cancer imaging archive (TCIA): Maintaining and operating a public information repository 2013 · doi:10.1007/s10278-013-9622-7

[2] Globus Team: Globus: Research data management.https://www.globus.org (2024), accessed: 2024-12-05 2024

[3] The Computer Journal52(1), 43–63 (2008).https://doi.org/10.1093/comjnl/bxm075,https://doi.org/10 2008 · doi:10.1093/comjnl/bxm075

[4] Denoising Diffusion Probabilistic Models 2020 · arXiv:2006.11239

[5] In: Proceedings of the AAAI Conference on Artificial Intelligence 2019 · doi:10.1609/aaai.v33i01.3301590

Receipt and verification

First computed	2026-05-20T00:02:23.960939Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

fca345f74928dc011b501bdd4c461c2868607a435a861e61d949297189b7c85e

Aliases

arxiv: 2605.16476 · arxiv_version: 2605.16476v1 · doi: 10.48550/arxiv.2605.16476 · pith_short_12: 7SRUL52JFDOA · pith_short_16: 7SRUL52JFDOACG2Q · pith_short_8: 7SRUL52J

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/7SRUL52JFDOACG2QDPOUYRQ4FB \
  | 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: fca345f74928dc011b501bdd4c461c2868607a435a861e61d949297189b7c85e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "35e26ffef64b1285ff23ffd123737f76308430bdb2125013a13e212898922c89",
    "cross_cats_sorted": [
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.IV",
    "submitted_at": "2026-05-15T16:04:13Z",
    "title_canon_sha256": "26cde9069cb0e70e27def1d03aebe135444cd3d6370255d76cef02b47e4a159c"
  },
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  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}