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Updated: Sep 4, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures
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Brain PET motion correction using 3D face-shape model: the first clinical study.

Yuma Iwao1, Go Akamatsu1, Hideaki Tashima1

  • 1Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-ku, Chiba, 263-8555, Japan.

Annals of Nuclear Medicine
|July 19, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a novel motion correction method for brain PET scans using a 3D face-shape model. The technique effectively corrected head motion in volunteers, improving image quality for clinical applications.

Keywords:
3D face-shape modelBrain PETKinectMotion correctionRange-sensing camera

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Biomedical Engineering

Background:

  • Head motion during Positron Emission Tomography (PET) scans degrades brain image quality.
  • Current head fixation methods can be burdensome for patients.
  • Developing non-invasive motion correction is crucial for accurate brain imaging.

Purpose of the Study:

  • To develop and validate a novel motion correction method for brain PET scans.
  • To assess the effectiveness of a 3D face-shape model derived from Kinect and CT imaging for motion correction.
  • To evaluate the clinical applicability of the developed motion correction technique.

Main Methods:

  • A motion correction method was developed using a 3D face-shape model generated by a Kinect range-sensing camera and CT images.
  • Eight healthy male volunteers underwent head-fixed and head-moving PET scans.
  • PET images were analyzed visually and using Standard Uptake Value Ratios (SUVRs) in predefined regions of interest.

Main Results:

  • No significant visual or statistical differences in SUVRs were found between motion-corrected head-moving PET images and head-fixed PET images.
  • The inferior colliculus, a small brain nucleus, was successfully visualized in motion-corrected images, unlike in uncorrected images.
  • Test-retest experiments showed high correlation (r² = 0.995) for SUVRs, confirming spatial calibration accuracy.

Conclusions:

  • The developed motion correction method accurately corrects head motion in volunteer brain PET scans.
  • The technique, utilizing a 3D face-shape model, shows promise for clinical use.
  • This non-invasive approach enhances the reliability of brain PET imaging in patient studies.