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Quantitative validation of data-driven motion correction for brain PET using phantom with motion generator system.

Yuto Kamitaka1,2, Muneyuki Sakata3, Keiichi Oda1,4

  • 1Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan.

EJNMMI Physics
|December 16, 2025
PubMed
Summary

Data-driven motion correction (MC) improves brain PET image quality, especially with shorter frame durations for accurate motion estimation. However, correction accuracy decreases under low-radioactivity conditions.

Keywords:
Alzheimer’s diseaseAmyloid PETData-driven motion correctionDementiaHead motionImage quality[18F]FDG

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Area of Science:

  • Nuclear Medicine
  • Medical Imaging
  • Positron Emission Tomography (PET)

Background:

  • Head motion during brain PET scans degrades image quality and quantitative accuracy.
  • A data-driven motion correction (MC) method using ultrafast list-mode reconstruction has shown promise.
  • Evaluating MC with known motions and tracer distributions is crucial for clinical validation.

Purpose of the Study:

  • To quantitatively evaluate a data-driven MC method for brain PET.
  • To assess MC performance using a brain phantom with known tracer distributions.
  • To validate MC accuracy with a motion generator system for known, variable motions.

Main Methods:

  • A Hoffman 3D brain phantom was used with [18F]fluoro-2-deoxy-D-glucose (FDG) to simulate high (20 MBq) and low (3 MBq) radioactivity conditions.
  • A custom motion generator simulated rotations (z-axis: 15°, 30°; x-axis: 3°, 6°) and translations (z-axis: 5 mm, 10 mm).
  • Data-driven MC was applied with frame durations of 1, 2, 5, 10, and 20 s; estimated motions were compared to optical tracker data; %contrast and gray matter CV% were calculated.

Main Results:

  • Motion estimation was stable under high-radioactivity but less stable under low-radioactivity conditions, especially with shorter frame durations.
  • Longer frame durations led to underestimation of continuous motion.
  • Data-driven MC improved %contrast and gray matter CV% across all conditions; shorter frames better corrected rapid motions but increased noise in low-radioactivity scans.

Conclusions:

  • The data-driven MC method effectively enhances motion-affected brain PET image quality in both high- and low-radioactivity settings.
  • The method demonstrates broad applicability for improving PET image analysis.
  • Correction accuracy was found to be reduced under lower-radioactivity conditions.