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Synthesizing 4D image phantoms spanning multiple respiratory cycles from heterogeneous data sources.

Elodie Lugez1

  • 1Computer Science, Toronto Metropolitan University, Toronto, Canada.

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|February 26, 2026
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Summary

Researchers developed a pipeline to create realistic 4D motion phantoms for validating medical imaging algorithms. These synthesized datasets provide crucial ground truth motion data for multiple respiratory cycles, addressing a scarcity in current resources.

Keywords:
4D numerical phantomground truth motion datamotion tracking validationmulti-respiratory-cycle motiontime-resolved 4D-CTvirtual 4D-MRI

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Algorithm Development

Background:

  • Realistic four-dimensional (4D) image datasets with ground truth (GT) motion information are essential for developing and validating motion tracking algorithms.
  • Existing datasets often lack sufficient duration, spanning only a single respiratory cycle, and are scarce for multi-cycle motion analysis.

Purpose of the Study:

  • To present a reproducible pipeline for synthesizing anatomically semi-realistic 4D motion phantoms with GT motion information.
  • To leverage heterogeneous and publicly available data sources for phantom generation.
  • To provide a resource for robust development, validation, and benchmarking of image-based motion tracking algorithms.

Main Methods:

  • Integration of 4D deformation vector fields (DVFs) from single-cycle datasets, static 3D images, and multi-cycle respiratory traces.
  • Harmonization of heterogeneous inputs via DVF alignment, spatial gap completion, and respiratory trace-guided DVF interpolation to create continuous motion fields.
  • Application of synthesized motion fields to static images to generate time-resolved 4D image sequences capturing multi-cycle respiratory motion.

Main Results:

  • Generation of three distinct phantom datasets (4D CT and 4D MR) with multi-organ GT segmentations and/or GT DVFs.
  • Quantitative validation showing physically plausible volumetric changes and significant correlation between synthesized and measured liver motion.
  • High qualitative realism scores (4.2/5) from expert assessment and comparable motion tracking performance to clinical sequences.
  • Public accessibility of generated phantoms, source code, and input data for reproducibility and customization.

Conclusions:

  • The developed pipeline successfully synthesizes 4D image phantoms capturing physiological motion over multiple respiratory cycles.
  • The openly shared resource addresses a critical gap, enabling more robust development and validation of motion tracking algorithms.
  • This work facilitates advancements in medical imaging research by providing a standardized evaluation setting.