Related Experiment Video
Updated: Oct 1, 2025
Movement Retraining using Real-time Feedback of Performance
Published on: January 17, 2013
Evaluating different methods of MR-based motion correction in simultaneous PET/MR using a head phantom moved by a
Eric Einspänner1,2, Thies H Jochimsen3, Johanna Harries4
1Clinic of Radiology and Nuclear Medicine, Magdeburg, Germany. eric.einspaenner@med.ovgu.de.
Background:
Due to comparatively long measurement times in simultaneous positron emission tomography and magnetic resonance (PET/MR) imaging, patient movement during the measurement can be challenging. This leads to artifacts which have a negative impact on the visual assessment and quantitative validity of the image data and, in the worst case, can lead to misinterpretations. Simultaneous PET/MR systems allow the MR-based registration of movements and enable correction of the PET data. To assess the effectiveness of motion correction methods, it is necessary to carry out measurements on phantoms that are moved in a reproducible way. This study explores the possibility of using such a phantom-based setup to evaluate motion correction strategies in PET/MR of the human head.
Method:
An MR-compatible robotic system was used to generate rigid movements of a head-like phantom. Different tools, either from the manufacturer or open-source software, were used to estimate and correct for motion based on the PET data itself (SIRF with SPM and NiftyReg) and MR data acquired simultaneously (e.g. MCLFIRT, BrainCompass). Different motion estimates were compared using data acquired during robot-induced motion. The effectiveness of motion correction of PET data was evaluated by determining the segmented volume of an activity-filled flask inside the phantom. In addition, the segmented volume was used to determine the centre-of-mass and the change in maximum activity concentration.
Results:
The results showed a volume increase between 2.7 and 36.3% could be induced by the experimental setup depending on the motion pattern. Both, BrainCompass and MCFLIRT, produced corrected PET images, by reducing the volume increase to 0.7-4.7% (BrainCompass) and to -2.8-0.4% (MCFLIRT). The same was observed for example for the centre-of-mass, where the results show that MCFLIRT (0.2-0.6 mm after motion correction) had a smaller deviation from the reference position than BrainCompass (0.5-1.8 mm) for all displacements.
Conclusions:
The experimental setup is suitable for the reproducible generation of movement patterns. Using open-source software for motion correction is a viable alternative to the vendor-provided motion-correction software.
Insights
Patient movement during PET/MR scans causes artifacts. This study demonstrates a phantom setup to evaluate motion correction strategies, showing open-source software effectively corrects PET/MR imaging artifacts.
Area of Science:
- Medical Imaging
- Neuroscience
- Biophysics
Background:
- Patient motion during simultaneous Positron Emission Tomography and Magnetic Resonance (PET/MR) imaging leads to artifacts, compromising image quality and diagnostic accuracy.
- Simultaneous PET/MR systems offer MR-based motion registration for PET data correction.
- Reproducible phantom-based motion is crucial for evaluating motion correction effectiveness.
Purpose of the Study:
- To establish a phantom-based experimental setup for reproducible motion generation in PET/MR imaging of the human head.
- To evaluate and compare different motion correction strategies using both vendor-provided and open-source software.
- To assess the effectiveness of motion correction on PET data validity.
Main Methods:
- An MR-compatible robotic system generated rigid movements of a head phantom.
- Motion was estimated and corrected using various tools, including SIRF with SPM, NiftyReg, MCFLIRT, and BrainCompass, leveraging both PET and simultaneous MR data.
- Correction effectiveness was quantified by analyzing the segmented volume, center-of-mass, and maximum activity concentration of an internal phantom component.
Main Results:
- Robot-induced motion caused volume increases ranging from 2.7% to 36.3%.
- Motion correction using BrainCompass reduced volume increase to 0.7-4.7%, while MCFLIRT achieved -2.8-0.4%.
- MCFLIRT demonstrated superior performance in center-of-mass accuracy (0.2-0.6 mm deviation) compared to BrainCompass (0.5-1.8 mm).
Conclusions:
- The developed experimental setup reliably generates reproducible motion patterns for PET/MR research.
- Open-source motion correction software provides a viable and effective alternative to vendor-specific solutions.
- This work validates a method for assessing motion correction efficacy in head PET/MR imaging.
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