pith. sign in

arxiv: 2606.02961 · v1 · pith:WJUOMRAEnew · submitted 2026-06-01 · 📡 eess.IV

AtlasGS: Brain MRI Spatial Resolution Harmonization With Shared Gaussian Geometry

Yifan Gao , Peiran Xu , Yimeng He , Haoran Li , Ziyang Long , Yufeng Wang , Ju Dong Yang , Debiao Li This is my paper

Pith reviewed 2026-06-28 11:50 UTC · model grok-4.3

classification 📡 eess.IV
keywords brain MRIGaussian splattingsuper-resolutionmulti-contrast harmonizationspatial resolutionarbitrary view generationself-supervised reconstruction
0
0 comments X

The pith

A shared Gaussian scaffold learned from one isotropic MRI scan enables high-fidelity reconstruction of sparse-slice images across multiple modalities.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a two-stage Gaussian splatting approach that first builds an explicit subject-specific scaffold capturing anatomical geometry from a single high-resolution structural scan. In the second stage this scaffold is held fixed while appearance parameters are fitted to target modalities that were acquired with fewer slices. The method is evaluated on UK Biobank, GBM and ABCD data for through-plane super-resolution across T2-weighted, FLAIR, DWI and ASL contrasts, multiple degradation factors, and glioblastoma cases. A sympathetic reader would care because many clinical MRI exams contain inconsistent slice spacing; a reusable geometry model could turn such data into consistent, high-resolution references without new acquisitions.

Core claim

The paper establishes that an explicit, subject-specific Gaussian scaffold encoding anatomical geometry can be learned once from an isotropic structural scan and then reused to fit appearance parameters for target modalities acquired with sparse slices, producing state-of-the-art reconstructions, arbitrary-view images with structural consistency, and potential for self-supervised in-plane super-resolution.

What carries the argument

The subject-specific Gaussian scaffold, an explicit representation of anatomical geometry that is learned from one scan and held fixed while appearance is fitted for other modalities.

If this is right

  • State-of-the-art reconstruction fidelity is achieved on UK Biobank, GBM and ABCD datasets across degradation factors of ×3, ×5 and ×7 and in the presence of glioblastoma.
  • Arbitrary-view generation for target modalities maintains strong structural consistency with the original geometry.
  • Self-supervised in-plane super-resolution becomes feasible using the same fixed scaffold.
  • Explicit geometry-guided representations offer a flexible pathway for retrospective multi-contrast MRI harmonization and clinical reference construction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The separation of geometry from appearance could be tested on longitudinal scans to check whether a single scaffold remains valid when anatomy changes slowly over time.
  • The same two-stage pattern might apply outside the brain if an initial isotropic reference scan can be obtained for any anatomy.
  • Because the scaffold is explicit, it could be inspected or edited by clinicians to correct for known anatomical variants before appearance fitting.

Load-bearing premise

An anatomical geometry scaffold learned from one isotropic scan can be reused for other modalities without introducing structural distortions or loss of fidelity.

What would settle it

A side-by-side experiment on the same sparse-slice volumes that compares reconstructions produced by the shared scaffold against reconstructions produced by modality-specific scaffolds learned from scratch; if the shared-scaffold versions show measurably lower fidelity or visible anatomical mismatches, the central claim is falsified.

Figures

Figures reproduced from arXiv: 2606.02961 by Debiao Li, Haoran Li, Ju Dong Yang, Peiran Xu, Yifan Gao, Yimeng He, Yufeng Wang, Ziyang Long.

Figure 1
Figure 1. Figure 1: Overview of AtlasGS: shared Gaussian geometry framework for multi-contrast MRI spatial resolution harmonization. 1 Introduction Magnetic resonance imaging (MRI) spatial harmonization refers to establish￾ing a consistent anatomical representation across heterogeneous acquisition pro￾tocols, enabling reliable cross-contrast visualization, comparison, and measure￾ment [9,1,27]. In this paper, we focus on clin… view at source ↗
Figure 2
Figure 2. Figure 2: Axial, Coronal and Sagittal view of reconstructions results with comparative methods. 7x axial super-resolution from UKBB Flair. From left to right: Groundtruth, Linear, Cubic, MC-INR, SA-INR, MedGS, Ours. training. The explicit shared geometric space restores in-plane structural detail and enables zero-shot adaptation of volume appearance. Although geometry preservation is stable under anisotropic samplin… view at source ↗
Figure 3
Figure 3. Figure 3: Reconstruction results on GBM and ABCD datasets. GBM: green contour shows tumor regions, and error maps are given for reference. ABCD: UltraHigh denotes reconstructions generated at a sampling density matched to isotropic T1 resolution. References 1. Abbasi, S., et al.: Deep learning for the harmonization of structural MRI scans: a survey. Biomed. Eng. Online 23, 90 (2024). https://doi.org/10.1186/ s12938-… view at source ↗
read the original abstract

Splatting (GS)-based shared geometry framework adopts a two-stage training strategy, in which an explicit, subject-specific Gaussian scaffold encoding anatomical geometry is first learned from the isotropic structural scan and then reused to fit appearance for target modalities acquired with sparse slices. Experiments on the UK Biobank, GBM, and ABCD datasets for through-plane super-resolution across multiple modalities (T2-weighted, FLAIR, DWI, ASL), degradation factors ($\times 3$, $\times 5$, $\times 7$), and pathological abnormalities (glioblastoma) demonstrate state-of-the-art reconstruction fidelity. The shared Gaussian geometry enables arbitrary-view generation for target modalities with strong structural consistency and further shows potential for self-supervised in-plane super-resolution. This work establishes explicit geometry-guided representations as a novel, flexible, and interpretable pathway toward retrospective multi-contrast MRI harmonization and reliable clinical reference construction. Source code is available at: https://github.com/yfgao76/AtlasGS

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper introduces AtlasGS, a Gaussian Splatting (GS)-based framework for brain MRI spatial resolution harmonization. It employs a two-stage strategy: first learning an explicit, subject-specific Gaussian scaffold that encodes anatomical geometry from an isotropic structural (T1) scan, then freezing this scaffold and optimizing only appearance parameters (opacity, color) to fit target modalities acquired with sparse slices. Experiments on the UK Biobank, GBM, and ABCD datasets claim state-of-the-art through-plane super-resolution performance for T2-weighted, FLAIR, DWI, and ASL modalities across degradation factors of ×3, ×5, and ×7, including cases with glioblastoma pathology. The shared geometry is said to enable arbitrary-view generation with structural consistency and shows potential for self-supervised in-plane super-resolution. Source code is released at https://github.com/yfgao76/AtlasGS.

Significance. If the results hold, this work provides a novel explicit geometry-guided representation for retrospective multi-contrast MRI harmonization that is interpretable and flexible compared to implicit methods. The public code release supports reproducibility. The approach could aid clinical reference construction by enabling consistent structural scaffolds across modalities and views. Significance is tempered by the need to validate the core modality-invariance assumption for the fixed scaffold.

major comments (1)
  1. [Method (two-stage training) and Experiments (UK Biobank/GBM/ABCD results)] The two-stage training strategy (abstract and method description) fixes the Gaussian scaffold positions/scales learned from T1 while optimizing only appearance for other modalities. This assumption that anatomical geometry is modality-invariant at the scale of the primitives is load-bearing for the harmonization and SOTA fidelity claims, yet no ablation is reported that relaxes the fixed-geometry constraint (e.g., allowing small per-modality position/scale offsets or joint geometry-appearance optimization) to quantify any fidelity loss, especially on DWI/ASL or the GBM glioblastoma cases where tissue contrast and boundaries differ from T1.
minor comments (1)
  1. [Abstract] The abstract asserts state-of-the-art reconstruction fidelity without summarizing any quantitative metrics (e.g., PSNR, SSIM), baseline comparisons, error bars, or train/validation split details; these should be included in the abstract for a self-contained claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment regarding validation of the fixed-geometry assumption in our two-stage training strategy.

read point-by-point responses

  1. Referee: [Method (two-stage training) and Experiments (UK Biobank/GBM/ABCD results)] The two-stage training strategy (abstract and method description) fixes the Gaussian scaffold positions/scales learned from T1 while optimizing only appearance for other modalities. This assumption that anatomical geometry is modality-invariant at the scale of the primitives is load-bearing for the harmonization and SOTA fidelity claims, yet no ablation is reported that relaxes the fixed-geometry constraint (e.g., allowing small per-modality position/scale offsets or joint geometry-appearance optimization) to quantify any fidelity loss, especially on DWI/ASL or the GBM glioblastoma cases where tissue contrast and boundaries differ from T1.

    Authors: The fixed-geometry constraint is a deliberate and core design choice of AtlasGS: the subject-specific Gaussian scaffold is learned once from the isotropic T1 scan to capture anatomical structure, then frozen so that only appearance parameters are optimized for each target modality. This enforces the structural consistency that enables arbitrary-view synthesis and multi-modal harmonization. The modality-invariance assumption at the primitive scale is empirically supported by the reported state-of-the-art results on T2-weighted, FLAIR, DWI, and ASL across UK Biobank, ABCD, and the GBM dataset (which includes glioblastoma cases with altered tissue boundaries). These outcomes demonstrate that appearance-only optimization suffices for high-fidelity reconstruction even when contrast differs markedly from T1. An ablation that relaxes the fixed scaffold (e.g., per-modality offsets or joint optimization) would test a different method and incur substantial additional compute; we therefore did not perform it, as the existing cross-modality and cross-pathology results already validate the shared-geometry premise. revision: no

Circularity Check

0 steps flagged

No circularity: two-stage geometry-appearance optimization is a standard fitting procedure with independent validation

full rationale

The paper's core procedure learns a Gaussian scaffold from an isotropic T1 scan then optimizes only appearance parameters on sparse-slice targets. This is a conventional two-stage optimization on external multi-modal datasets (UK Biobank, GBM, ABCD) rather than any self-definitional loop, fitted-input-renamed-as-prediction, or load-bearing self-citation chain. No equations are presented that reduce the reported fidelity metrics to quantities defined by the same fitted values; the claimed arbitrary-view consistency and SOTA results are evaluated against held-out data and therefore remain falsifiable outside the fitting process itself.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the central modeling choice (a reusable Gaussian scaffold) is treated as a representational innovation rather than a new physical entity.

pith-pipeline@v0.9.1-grok · 5718 in / 1181 out tokens · 26149 ms · 2026-06-28T11:50:42.522103+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

27 extracted references · 19 canonical work pages · 2 internal anchors

  1. [1]

    Abbasi, S., et al.: Deep learning for the harmonization of structural MRI scans: a survey. Biomed. Eng. Online23, 90 (2024).https://doi.org/10.1186/ s12938-024-01280-6

    2024

  2. [2]

    Scientific Data9, 453 (2022)

    Bakas, S., et al.: The university of pennsylvania glioblastoma (UPenn-GBM) co- hort: advanced MRI, clinical, genomics, & radiomics. Scientific Data9, 453 (2022). https://doi.org/10.1038/s41597-022-01560-7

    work page doi:10.1038/s41597-022-01560-7 2022

  3. [3]

    Neural Comput.15(6), 1373–1396 (2003).https://doi.org/10

    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput.15(6), 1373–1396 (2003).https://doi.org/10. 1162/089976603321780317

    2003

  4. [4]

    Casey, B., et al.: The adolescent brain cognitive development (ABCD) study: Imaging acquisition across 21 sites. Dev. Cogn. Neurosci.32, 43–54 (2018). https://doi.org/10.1016/j.dcn.2018.03.001

    work page doi:10.1016/j.dcn.2018.03.001 2018

  5. [5]

    arXiv:2506.14432 (2026),https://arxiv.org/ abs/2506.14432

    Cerri, S., et al.: A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning. arXiv:2506.14432 (2026),https://arxiv.org/ abs/2506.14432

    Pith/arXiv arXiv 2026

  6. [6]

    In: MICCAI 2025

    Choi, Y., et al.: TESLA: Test-time reference-free through-plane super- resolution for multi-contrast brain MRI. In: MICCAI 2025. LNCS, vol. 15972, pp. 584–593. Springer Nature Switzerland (2025).https://doi.org/10.1007/ 978-3-032-05169-1_56

    2025

  7. [7]

    IEEE Trans

    Clough, J., et al.: A topological loss function for deep-learning based image segmen- tation using persistent homology. IEEE Trans. Pattern Anal. Mach. Intell.44(12), 8766–8778 (2022).https://doi.org/10.1109/TPAMI.2020.3013679

    work page doi:10.1109/tpami.2020.3013679 2022

  8. [8]

    Greenspan, H., Peled, S., Oz, G., Kiryati, N.: MRI inter-slice reconstruction using super-resolution. Magn. Reson. Imaging20, 437–446 (2002).https://doi.org/ 10.1016/S0730-725X(02)00511-8

    work page doi:10.1016/s0730-725x(02)00511-8 2002

  9. [9]

    Hu, F., et al.: Image harmonization: a review of statistical and deep learning meth- ods for removing batch effects and evaluation metrics for effective harmonization. 10 Y. Gao et al. NeuroImage274,120125(2023).https://doi.org/10.1016/j.neuroimage.2023. 120125

    work page doi:10.1016/j.neuroimage.2023 2023

  10. [10]

    Isensee,F.,etal.:AutomatedbrainextractionofmultisequenceMRIusingartificial neural networks. Hum. Brain Mapp.40(17), 4952–4964 (2019).https://doi.org/ 10.1002/hbm.24750

    work page doi:10.1002/hbm.24750 2019

  11. [11]

    Isensee, F., et al.: nnu-net: self-configuring biomedical image segmentation. Nat. Methods18(2), 203–211 (2021).https://doi.org/10.1038/s41592-020-01008-z

    work page doi:10.1038/s41592-020-01008-z 2021

  12. [12]

    3D Gaussian splatting for real-time radiance field rendering,

    Kerbl, B., et al.: 3D gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. (SIGGRAPH) (2023).https://doi.org/10.1145/3592433

    work page doi:10.1145/3592433 2023

  13. [13]

    arXiv:2511.16854 (2025).https://doi.org/10.48550/arXiv.2511.16854

    Khateri, M., et al.: MRI super-resolution with deep learning: A comprehensive survey. arXiv:2511.16854 (2025).https://doi.org/10.48550/arXiv.2511.16854

    work page doi:10.48550/arxiv.2511.16854 2025

  14. [14]

    Adam: A Method for Stochastic Optimization

    Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv:1412.6980 (2014).https://doi.org/10.48550/arXiv.1412.6980

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1412.6980 2014

  15. [15]

    org/10.1109/42.932749

    Lehmann,T.,Gönner,C.,Spitzer,K.:Addendum:B-splineinterpolationinmedical imageprocessing.IEEETrans.Med.Imaging20(7),660–665(2001).https://doi. org/10.1109/42.932749

    work page doi:10.1109/42.932749 2001

  16. [16]

    Liu, X., et al.: Deep unregistered multi-contrast MRI reconstruction. Magn. Reson. Imaging81, 33–41 (2021).https://doi.org/10.1016/j.mri.2021.05.005

    work page doi:10.1016/j.mri.2021.05.005 2021

  17. [17]

    Mahmoudzadeh, A., Kashou, N.: Interpolation-based super-resolution reconstruc- tion: effects of slice thickness. J. Med. Imaging (Bellingham)1(3), 034007 (2014). https://doi.org/10.1117/1.JMI.1.3.034007

    work page doi:10.1117/1.jmi.1.3.034007 2014

  18. [18]

    MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging

    Marzol, K., et al.: MedGS: Gaussian splatting for multi-modal 3D medical imaging. arXiv:2509.16806 (2025).https://doi.org/10.48550/arXiv.2509.16806

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2509.16806 2025

  19. [19]

    In: Greenspan, H., et al

    McGinnis, J., et al.: Single-subject multi-contrast MRI super-resolution via im- plicit neural representations. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14229, pp. 173–183. Springer, Cham (2023).https://doi.org/10. 1007/978-3-031-43993-3_17

    2023

  20. [20]

    In: ICCVW

    Molaei, A., et al.: Implicit neural representation in medical imaging: A com- parative survey. In: ICCVW. pp. 2381–2391 (2023).https://doi.org/10.1109/ ICCVW60793.2023.00252

    arXiv 2023

  21. [21]

    In: Wolterink, J., et al

    Remedios, S., et al.: Self-supervised super-resolution for anisotropic MR im- ages with and without slice gap. In: Wolterink, J., et al. (eds.) SASHIMI 2023. LNCS, vol. 14288, pp. 118–128. Springer, Cham (2023).https://doi.org/10. 1007/978-3-031-44689-4_12

    2023

  22. [22]

    arXiv:2412.16619 (2024).https://doi.org/10.48550/arXiv.2412.16619

    Shen, T., Liu, S., Feng, J., Ma, Z., An, N.: Topology-aware 3D gaussian splatting: Leveraging persistent homology for optimized structural integrity. arXiv:2412.16619 (2024).https://doi.org/10.48550/arXiv.2412.16619

    work page doi:10.48550/arxiv.2412.16619 2024

  23. [23]

    arXiv:2411.11024 (2024).https://doi.org/10.48550/arXiv.2411.11024

    Smolak-Dyżewska, W., et al.: VeGaS: Video gaussian splatting. arXiv:2411.11024 (2024).https://doi.org/10.48550/arXiv.2411.11024

    work page doi:10.48550/arxiv.2411.11024 2024

  24. [24]

    PLoS Med.12(3), e1001779 (2015).https://doi.org/10.1371/journal.pmed.1001779

    Sudlow, C., et al.: UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med.12(3), e1001779 (2015).https://doi.org/10.1371/journal.pmed.1001779

    work page doi:10.1371/journal.pmed.1001779 2015

  25. [25]

    In: CVPR Workshop (2025),https://github.com/kushalvyas/alpine, last accessed 2026/02/25

    Vyas, K., et al.: Alpine: distributed PyTorch library for implicit neural representa- tions. In: CVPR Workshop (2025),https://github.com/kushalvyas/alpine, last accessed 2026/02/25

    2025

  26. [26]

    Wang, X., et al.: SA-INR for arbitrary MRI slice-spacing reduction. Med. Image Anal.94, 103158 (2024).https://doi.org/10.1016/j.media.2024.103158

    work page doi:10.1016/j.media.2024.103158 2024

  27. [27]

    arXiv:2507.16962 (2025).https: //doi.org/10.48550/arXiv.2507.16962

    Yang, Q., Shomal-Zadeh, F., Gholipour, A.: MRI harmonization: a survey of ac- quisition, image-level, and feature-level methods. arXiv:2507.16962 (2025).https: //doi.org/10.48550/arXiv.2507.16962

    work page doi:10.48550/arxiv.2507.16962 2025