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Repurposing the Microsoft Kinect for Windows v2 for external head motion tracking for brain PET.

P J Noonan1, J Howard, W A Hallett

  • 1Department of Medicine, Imperial College London, Hammersmith, London, UK.

Physics in Medicine and Biology
|November 4, 2015
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Summary
This summary is machine-generated.

This study introduces a novel method using Microsoft Kinect v2 to accurately measure head motion during positron emission tomography (PET) scans, improving data quality for brain imaging studies.

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Computer Vision

Background:

  • Positison Emission Tomography (PET) imaging offers high spatial resolution for brain structure analysis.
  • Subject head motion during PET scans degrades data quality, especially in long dynamic studies or for patients with neurological disorders.
  • Current motion detection methods have limitations, and external tracking systems can be cumbersome.

Purpose of the Study:

  • To develop and evaluate a novel, non-invasive method for measuring rigid body head motion during PET brain imaging.
  • To leverage advances in computer vision, specifically the Microsoft Kinect v2, for real-time motion tracking.
  • To improve the accuracy and quality of PET brain imaging data by correcting for subject motion.

Main Methods:

  • Utilized the Microsoft Kinect v2 sensor and the KinectFusion algorithm for real-time (30 Hz) measurement of rigid body head motion.
  • Achieved spatial accuracy of less than 0.5 mm for motion measurements.
  • Detected non-rigid motion using KinectFusion's residual alignment energy data to discard unreliable motion.
  • Aligned motion data with PET listmode data using injected pulse sequences for motion correction.

Main Results:

  • Demonstrated a method capable of measuring rigid body head motion with high spatial accuracy (<=0.5 mm).
  • Real-time motion data acquisition at 30 Hz was achieved using the KinectFusion algorithm.
  • Successful alignment of motion data with PET listmode data for correction was shown in pilot clinical data.

Conclusions:

  • The Microsoft Kinect v2 system provides a viable and accurate method for measuring head motion in PET brain imaging.
  • This computer vision-based approach offers a promising solution for enhancing the quality of dynamic PET studies and aiding neurological patient examinations.
  • Real-time motion correction has the potential to significantly improve the diagnostic and research value of PET imaging data.