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arxiv: 2606.09953 · v1 · pith:AOQTI6ZKnew · submitted 2026-06-08 · 📡 eess.IV · cs.AI· cs.LG

Deep Slice Interpolation for Reducing Through-Plane Anisotropy and Noise in Head CT

Luis Cort\'es Ferre , Miguel A. Guti\'errez-Naranjo , Marcin Balcerzyk This is my paper

Pith reviewed 2026-06-27 14:56 UTC · model grok-4.3

classification 📡 eess.IV cs.AIcs.LG
keywords deep learningCT slice interpolationanisotropy reductionimplicit denoisinghead CTstructural similaritymultiplanar reconstruction
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The pith

A deep learning system synthesizes intermediate CT slices to reduce through-plane anisotropy while adding implicit denoising.

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

The paper describes a neural network trained to generate new slices between existing axial CT images, effectively halving the spacing in the through-plane direction. This targets the common mismatch in head CT where in-plane resolution is high but slice thickness creates anisotropy that harms 3D views and volume estimates. Multiple loss combinations were tested, with all converged models beating classical interpolation and video methods on structural metrics. The same forward pass also produces denoised outputs, an effect that appeared on an external out-of-distribution scan.

Core claim

The system takes pairs of neighboring axial slices and outputs synthesized intermediate slices that halve effective through-plane spacing. All trained models surpass classical baselines and pretrained video interpolation networks on structural measures, with the MS-SSIM plus L1 combination giving the strongest overall profile. On an external head CT series the model reproduces the implicit denoising behavior predicted by the referenced theoretical analysis, indicating that both interpolation quality and the denoising side-effect are not limited to the training distribution.

What carries the argument

A convolutional network trained end-to-end with hybrid pixel-wise and multi-scale structural similarity losses to perform slice interpolation on anisotropic CT volumes.

If this is right

  • Multiplanar reformats and 3D visualizations gain resolution without additional scanning.
  • Volumetric measurements such as hematoma volume become more precise because voxel isotropy improves.
  • Downstream algorithms that expect near-isotropic input receive higher-quality data from the same acquisition.
  • Denoising occurs automatically during interpolation, removing the need for a separate noise-reduction step.
  • Patient-level bootstrap intervals and paired tests confirm the measured gains over baselines.

Where Pith is reading between the lines

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

  • The same architecture could be retrained on other anisotropic modalities such as certain MRI protocols to reduce slice spacing.
  • Routine clinical pipelines might adopt the method to improve existing low-dose or thick-slice acquisitions rather than increasing radiation exposure.
  • Larger external validation sets would be needed to confirm whether the single-case denoising observation scales to routine practice.
  • The documented instability of SSIM-family losses at small batch sizes points to a practical constraint for deployment on limited hardware.

Load-bearing premise

The implicit denoising and generalization seen on one external case will hold across wider clinical populations and scanner types.

What would settle it

Quantitative comparison of hematoma volume estimates computed from original versus interpolated volumes on a multi-center test set of head CT scans with known ground-truth volumes.

Figures

Figures reproduced from arXiv: 2606.09953 by Luis Cort\'es Ferre, Marcin Balcerzyk, Miguel A. Guti\'errez-Naranjo.

Figure 1
Figure 1. Figure 1: U-Net + EfficientNetV2-S architecture for CT slice interpolation. Top row: encoder (blue), left-to-right in decreasing spatial resolution. Bottom row: decoder (orange), right-to-left in increasing spatial resolution. Columns are aligned by spatial resolution: the leftmost column carries the 2-channel input [Ik, Ik+2] and the final decoder block fused with its 3 × 3 conv output projection (producing the sin… view at source ↗
Figure 2
Figure 2. Figure 2: Patch geometry shared by training-time sampling and test-time reconstruction. (a) Nine overlapping 256 × 256 crops whose top-left corners form a 3 × 3 grid at 64-pixel stride in the 512 × 512 slice; their union covers the central 384 × 384 band (solid blue outline). Translucent fills overlap so deeper color indicates regions covered by more crops; the dashed outline marks one representative crop (top-left … view at source ↗
Figure 3
Figure 3. Figure 3: Radially-averaged 1D noise power spectrum in uniform white-matter ROIs, averaged over 28 test patients (median pixel spacing 0.488 mm/px). Curves compare the acquired-slice NPS to the prediction NPS of each model on the same ROIs. Pixel-wise regression losses (L1, MSE, MS-SSIM+L1) suppress NPS across essentially the full frequency band; SSIMs shows a smaller but qualitatively similar reduction. The linear_… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative interpolation result at the mid-brain ventricular / basal-ganglia level. Patient ID_615f69e3, target triplet index 14; RSNA label any= 0. Model: SSIM loss (lr = 3×10−3 ). Detail cell: 2× zoom of a 160 × 160 basal-ganglia / ventricular ROI; the yellow rectangle marks the zoom origin. Brain window and radiological convention as described in the text. The posterior-right low-intensity halo is the … view at source ↗
Figure 5
Figure 5. Figure 5: Additional qualitative examples at mid-brain axial levels: hemorrhage case (top) and consecutive normal slice (bottom). Model, display window, zoom layout, and orientation follow [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Cross-method qualitative comparison on two mid-brain test slices. Top row of each panel: left input slice, ground truth, right input slice. Bottom row: our SSIMs model, RIFE, FILM, and mean baseline. RIFE and FILM produce plausible but structurally less accurate outputs, particularly near bone boundaries and hemorrhage margins; the mean baseline is visibly blurred. Brain window; radiological convention. 18… view at source ↗
Figure 7
Figure 7. Figure 7: Predictions and pixel-wise absolute error maps for four loss functions on the same test slice as [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Implicit denoising across 10 consecutive slices (hemorrhage patient). Column 1: original noisy acquired slice. Column 2: model prediction from neighboring slices. Column 3: absolute difference, showing predominantly noise-like texture removal. Gross hemorrhagic and anatomical structures remain visible, but diagnostic preservation requires reader validation. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Implicit denoising across 10 consecutive slices at normal (non-hemorrhage) axial levels from the same patient as [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Four consecutive reconstructed slices from HUVR-1 in the coarse-spacing region (k ≥ 18; ≈1.8 mm gap between non-overlapping 3.70 mm-thick slabs) and their model residuals. Leftmost column: acquired slice (brain window, center 44 HU, width 128 HU). Remaining columns: signed diff Iˆk −realk for each trained loss; red> 0, blue< 0; saturation ± the 99th-percentile absolute diff across all models and coarse-re… view at source ↗
Figure 11
Figure 11. Figure 11: Quantitative decomposition of the residual on HUVR-1. Top-left: per-slice mean signed residual in HU for each predictor, across the middle-slice range k ∈ [2, N−1]; the dashed vertical line marks the transition from the overlap region to the coarse-spacing region. Top-right: normalised spectrum |FFT|/total of the mean-centred residual restricted to the coarse-spacing region (k ≥ 18, L = 14), with the righ… view at source ↗
read the original abstract

Head computed tomography (CT) typically uses sub-millimeter in-plane resolution but 2-5 mm through-plane spacing, creating substantial anisotropy that degrades multiplanar reconstructions, volumetric measurements such as hematoma volume estimation, and downstream algorithms that assume near-isotropic voxels. We present a deep learning system that synthesizes intermediate CT slices from pairs of neighboring axial slices, halving the effective through-plane spacing. The system improves three-dimensional visualization while simultaneously producing inherently denoised outputs, yielding two complementary benefits from a single inference pass. To build a reliable system, we systematically evaluate pixel-wise losses, namely mean squared error (MSE) and mean absolute error (L1); structural-similarity losses, namely the structural similarity index (SSIM) and its multi-scale variant (MS-SSIM); and hybrid combinations. On a held-out test set, all converged models outperform classical interpolation baselines and pretrained video frame interpolation methods (RIFE, FILM) on all structural measures, with MS-SSIM+L1 offering the strongest balanced profile. We also document training instability in SSIM-family losses and identify partial remedies: the standard numerical fixes eliminate the dominant failure mode but leave residual divergence at smaller batch sizes. All results are reported with patient-level bootstrap confidence intervals and paired statistical tests. As an illustration, we apply the system to an out-of-distribution head CT series from Hospital Universitario Virgen del Roc\'io: the model synthesizes intermediate slices and exhibits on the real slices the implicit-denoising signature predicted by our theoretical analysis, supporting in a single external case that interpolation quality and implicit denoising are not confined to the training distribution.

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 / 2 minor

Summary. The paper introduces a deep learning system to synthesize intermediate axial slices in head CT scans, halving through-plane spacing to mitigate anisotropy. It systematically compares pixel-wise (MSE, L1) and structural (SSIM, MS-SSIM) losses plus hybrids, reporting that all converged models outperform classical interpolation and pretrained video methods (RIFE, FILM) on held-out test data across structural metrics, with MS-SSIM+L1 strongest. Results include patient-level bootstrap CIs and paired tests. An out-of-distribution external case from Hospital Universitario Virgen del Rocío is used to illustrate implicit denoising consistent with a referenced theoretical analysis.

Significance. If the held-out performance claims hold, the work targets a practical clinical limitation in CT by improving multiplanar reformats, volumetric estimates, and downstream isotropic-assuming algorithms, while offering incidental denoising as a byproduct. The systematic loss-function ablation and use of patient-level bootstrap confidence intervals with paired statistical tests are explicit strengths that enhance credibility of the outperformance results over baselines.

major comments (1)
  1. [Abstract] Abstract (final paragraph): the assertion that results on the single external OOD series 'support... that interpolation quality and implicit denoising are not confined to the training distribution' rests on one case exhibiting the predicted signature; this is insufficient to underwrite the generalization claim without additional OOD series or quantitative metrics comparing synthesized vs. real slices on that data.
minor comments (2)
  1. [Abstract] Abstract: model architecture, training protocol, and dataset sizes (train/validation/test split) are omitted, which limits immediate verification of the central performance claims even though statistical reporting is present.
  2. [Abstract] Abstract: the phrase 'Roc\'io' contains a LaTeX escape artifact and should render as 'Rocío'.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation of minor revision. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (final paragraph): the assertion that results on the single external OOD series 'support... that interpolation quality and implicit denoising are not confined to the training distribution' rests on one case exhibiting the predicted signature; this is insufficient to underwrite the generalization claim without additional OOD series or quantitative metrics comparing synthesized vs. real slices on that data.

    Authors: We agree that a single external case constitutes an illustration rather than robust evidence for generalization. We will revise the abstract to describe the OOD example as an illustration of the predicted implicit-denoising signature on out-of-distribution data, removing the phrasing that it 'supports' the claim that these properties are not confined to the training distribution. The revised text will emphasize the illustrative purpose without overstating generalizability. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results on held-out and external data are independent of training inputs

full rationale

The paper reports model performance via standard held-out test-set metrics and one external OOD series, with no equations, fitted parameters, or self-citations that reduce any claimed prediction or denoising signature to the training data by construction. The referenced theoretical analysis is invoked only to interpret the single external observation and does not serve as a load-bearing derivation for the quantitative outperformance results.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on standard deep-learning assumptions for image synthesis tasks without introducing new physical entities or many explicitly fitted parameters beyond typical training choices.

free parameters (1)
  • hybrid loss weights
    Combinations such as MS-SSIM+L1 require weighting coefficients whose values are not stated in the abstract.
axioms (1)
  • domain assumption Convolutional networks trained on paired neighboring slices can synthesize plausible intermediate medical images.
    Central premise of the interpolation system.

pith-pipeline@v0.9.1-grok · 5843 in / 1228 out tokens · 33239 ms · 2026-06-27T14:56:20.770241+00:00 · methodology

discussion (0)

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Reference graph

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