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No Reproducibility, No Progress: Rethinking CT Benchmarking.

Dmitry Polevoy1,2, Danil Kazimirov2,3, Marat Gilmanov2,3

  • 1Federal Research Center Computer Science and Control RAS, 119333 Moscow, Russia.

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This summary is machine-generated.

Reproducibility in X-ray computed tomography (CT) reconstruction is a challenge due to limited datasets and methods. This study proposes a framework with data models and checklists for reliable CT benchmarking.

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CT reconstruction methodsX-ray CT (xCT)benchmarkingcomputed tomography (CT)data preparationdatasetsdeep learning (DL)evaluation metricsmachine learning (ML)phantomsquality assessmentreproducibilityvirtual CT (vCT)

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Area of Science:

  • Medical Imaging
  • Scientific Computing

Background:

  • Reproducibility is crucial for scientific advancement but is a significant challenge in X-ray computed tomography (CT) reconstruction.
  • Current CT benchmarking is hindered by a lack of open datasets, incomplete resources, and non-transparent implementations of methods and metrics.

Purpose of the Study:

  • To analyze limitations in current CT benchmarking practices.
  • To propose a framework for improving reproducibility and applicability of CT datasets.
  • To establish a foundation for reliable and reproducible benchmarking pipelines in CT.

Main Methods:

  • Analysis of systemic limitations in current CT benchmarking.
  • Proposal of an extended data model and formalized schemes for data preparation and quality assessment.
  • Introduction of checklists for dataset construction and quality assessment, including virtual CT (vCT) integration.

Main Results:

  • Identified systemic limitations undermining reproducibility in CT reconstruction.
  • Developed formalized schemes and checklists to enhance dataset quality and applicability.
  • Highlighted the underutilization of virtual CT (vCT) for realistic data generation.

Conclusions:

  • The proposed framework offers a first step toward reproducible benchmarking in CT.
  • Enhanced data models, schemes, and checklists can improve transparency and rigor in evaluating CT reconstruction methods.
  • This work aims to facilitate the reliable adoption of CT methods in clinical and industrial settings.