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arxiv: 2605.00239 · v1 · submitted 2026-04-30 · ⚛️ physics.med-ph

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Resolution-Noise Characteristics of Common FDK Filter Kernels: A Practical Reference for Preclinical Cone-Beam Micro-CT

Falk L Wiegmann , Nancy L Ford
Authors on Pith no claims yet

Pith reviewed 2026-05-09 19:31 UTC · model grok-4.3

classification ⚛️ physics.med-ph
keywords FDK reconstructionmicro-CTfilter kernelsMTFnoise power spectrumcone-beam CTpreclinical imagingimage quality
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The pith

Sixteen FDK filter configurations in micro-CT produce MTF10 values from 0.93 to 2.35 lp/mm and integrated noise from 75,670 to 13,259 HU squared.

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

The paper tests four common filter kernels at four cutoff frequencies in the FDK algorithm and reconstructs the same cone-beam data sixteen ways. It quantifies the resulting changes in spatial resolution through the modulation transfer function, noise through the noise power spectrum, and detectability through a non-prewhitening index, plus qualitative views of a mouse lung specimen. The measured ranges demonstrate that filter choice can alter the smallest reliably detectable object size by a factor of three at typical contrasts. A reader would use the data as a lookup reference when selecting reconstruction parameters instead of default settings that are rarely justified in published studies.

Core claim

Across the sixteen configurations of ramp, Shepp-Logan, cosine, and Hamming kernels at cutoffs of 1.0, 0.8, 0.6, and 0.379 times Nyquist, MTF10 ranges from 0.93 to 2.35 lp/mm, integrated NPS from 75,670 to 13,259 HU squared, and the Rose criterion crossing diameter from 2.86 to 0.93 mm at 500 HU contrast and from 7.74 to 3.62 mm at 100 HU. The note supplies these values together with example images as a concise visual and quantitative reference for choosing FDK filter parameters in preclinical cone-beam CT.

What carries the argument

The sixteen combinations of four filter kernels and four cutoff frequencies, assessed via MTF, NPS, and NPW d prime on identical data from one scanner and one mouse lung specimen.

If this is right

  • Filter selection can be matched to a target resolution or noise level using the tabulated MTF10 and NPS numbers.
  • The Rose criterion diameters supply practical estimates of the smallest detectable feature size at two common contrast levels.
  • Qualitative differences visible in the mouse lung reconstructions illustrate how the quantitative metrics translate to real specimen images.
  • The data set allows groups to avoid default filter settings and instead pick configurations that balance their specific imaging needs.

Where Pith is reading between the lines

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

  • The reference could encourage labs to report the exact kernel and cutoff used in every micro-CT study, improving reproducibility across publications.
  • Extending the same evaluation to additional scanners would test whether the observed ranges are geometry-dependent or broadly applicable.
  • Task-based metrics beyond NPW d prime could be added in follow-up work to refine filter choice for particular detection problems such as tumor or vessel visualization.

Load-bearing premise

Measurements performed on the GE eXplore CT 120 scanner and a single mouse lung specimen adequately represent the resolution-noise trade-offs needed across the full range of preclinical micro-CT applications and scanner geometries.

What would settle it

Reconstructing the identical raw projections on a different preclinical cone-beam micro-CT scanner, computing the new MTF10 and integrated NPS values for all sixteen configurations, and checking whether they fall outside the reported ranges would show whether the reference holds.

Figures

Figures reproduced from arXiv: 2605.00239 by Falk L Wiegmann, Nancy L Ford.

Figure 1
Figure 1. Figure 1: FDK reconstruction pipeline for short-scan cone-beam micro-CT. The ramp filter step (highlighted) is the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Image quality metrics for all sixteen filter configurations on the image quality phantom (short-scan, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Spatial resolution as a function of filter configuration. (a) MTF curves for all sixteen configurations, with [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Mouse lung reconstructions (axial slice) for all sixteen filter configurations, displayed with identical [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original abstract

The ramp filter kernel and cutoff frequency are fundamental parameters of the Feldkamp-Davis-Kress (FDK) algorithm that determine the resolution and noise characteristics of the reconstructed image. Despite their importance, systematic evaluations of their combined effect on task-based image quality in preclinical micro-CT are scarce, and many studies do not report the filter configuration used. We reconstruct identical data from a GE eXplore CT 120 scanner using four filter kernels (ramp, Shepp-Logan, cosine, Hamming) at four cutoff frequencies (1.0, 0.8, 0.6, and $0.379\times$ Nyquist, matched to the detector-to-voxel size ratio) and evaluate each of the sixteen configurations using the modulation transfer function (MTF), noise power spectrum (NPS), and non-prewhitening detectability index (NPW $d'$). Qualitative assessment is performed on a mouse lung specimen. Across the sixteen configurations, $\mathrm{MTF}_{10}$ ranges from 0.93 to 2.35 lp/mm, integrated NPS from 75,670 to 13,259 $\mathrm{HU}^2$, and the Rose criterion crossing diameter from 2.86 to 0.93 mm at $\Delta C = 500$ HU and from 7.74 to 3.62 mm at 100 HU. This note presents the data as a concise visual and quantitative reference for groups selecting FDK filter parameters for preclinical cone-beam CT.

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

0 major / 3 minor

Summary. The manuscript evaluates sixteen FDK reconstruction configurations on identical data from a GE eXplore CT 120 preclinical scanner, using four filter kernels (ramp, Shepp-Logan, cosine, Hamming) at four cutoff frequencies (1.0, 0.8, 0.6, and 0.379×Nyquist). It reports the resulting ranges for MTF at 10% (0.93–2.35 lp/mm), integrated noise power spectrum (75,670–13,259 HU²), and Rose-criterion diameters (2.86–0.93 mm at ΔC=500 HU; 7.74–3.62 mm at 100 HU), together with qualitative images of a mouse lung specimen, positioning the work as a practical reference for filter selection in cone-beam micro-CT.

Significance. If the reported measurements hold, the paper supplies a directly measured, quantitative reference for a frequently under-documented aspect of preclinical micro-CT reconstruction. The empirical workflow using standard Fourier-domain metrics (MTF, NPS) and task-based detectability (NPW d′) on real scanner data is a clear strength, providing concrete numbers that groups can use without performing their own extensive parameter sweeps.

minor comments (3)
  1. Abstract and results: The integrated NPS range is presented from highest to lowest value. A table listing the exact MTF10, NPS, and d′ values for each of the 16 individual configurations would allow readers to identify the precise kernel/cutoff pair that meets their resolution-noise requirements.
  2. Methods: Additional detail on ROI selection, phantom used for MTF/NPS measurements, and the precise implementation of the 0.379×Nyquist cutoff (including how it was matched to detector-to-voxel geometry) would improve reproducibility of the reported numerical ranges.
  3. Discussion: A short statement clarifying that the results are specific to the GE eXplore CT 120 geometry and the tested specimen would prevent over-generalization while preserving the utility of the data as a reference for similar systems.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive review and recommendation to accept the manuscript. We are pleased that the systematic evaluation of the sixteen FDK filter configurations is recognized as supplying a useful quantitative reference for preclinical micro-CT reconstruction.

Circularity Check

0 steps flagged

No significant circularity; purely empirical reference data

full rationale

The paper performs standard FDK reconstructions on fixed scanner data (GE eXplore CT 120, mouse lung specimen) using four kernels at four cutoffs, then directly measures MTF10, integrated NPS, and NPW d' via established Fourier-domain methods. No derivations, predictions, fitted parameters renamed as outputs, or self-citation chains are present; the reported ranges (MTF10 0.93–2.35 lp/mm, NPS 75,670–13,259 HU², Rose diameters) are obtained by applying the filters and computing the metrics on the resulting images. The work is self-contained against external benchmarks and contains no load-bearing steps that reduce to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

This is an empirical reference study that applies standard reconstruction and image-quality metrics to existing scanner data. No new free parameters are fitted, no new entities are postulated, and the work rests on conventional domain assumptions of CT reconstruction.

axioms (2)
  • domain assumption The FDK algorithm provides a valid reconstruction of cone-beam projections under the scanner geometry used.
    Invoked as the reconstruction method for all 16 configurations.
  • domain assumption MTF, NPS, and NPW d' are appropriate and sufficient metrics for assessing task-based image quality in preclinical micro-CT.
    Used to generate the reported resolution, noise, and detectability results.

pith-pipeline@v0.9.0 · 5580 in / 1875 out tokens · 48874 ms · 2026-05-09T19:31:03.249838+00:00 · methodology

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Reference graph

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