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Updated: Jun 28, 2025
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
Published on: May 10, 2012
Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation
Bingbing Zhao1, Yichen Zhou1, Xiaopeng Zong1
1School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, People's Republic of China.
Purpose:
The effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat-navigator-based PMC in improving perivascular space (PVS) visibility in T2-weighted MRI.
Methods:
MRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T2-weighted NoPMC and PMC images. To simulate motion artifacts, k-space data at motion-perturbed positions were calculated from artifact-free images using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation's ability to reproduce motion-induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility.
Results:
In simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images (p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images (p ≥ 0.48), suggesting reduction of motion effects with PMC.
Conclusions:
The proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.
Insights
A new simulation method effectively demonstrates how prospective motion correction (PMC) improves perivascular space (PVS) visibility in MRI. This approach helps assess PMC
Area of Science:
- Magnetic Resonance Imaging (MRI)
- Medical Imaging Analysis
- Neuroimaging
Background:
- Prospective motion correction (PMC) is crucial for reducing motion artifacts in MRI.
- Evaluating PMC effectiveness typically requires comparing images with and without PMC (NoPMC), which is not feasible clinically.
- Perivascular spaces (PVS) are important biomarkers, but their visibility in T2-weighted MRI is often degraded by motion.
Purpose of the Study:
- To develop and validate a simulation approach for assessing fat-navigator-based PMC's ability to enhance PVS visibility in T2-weighted MRI.
- To demonstrate the utility of simulation in studying motion effects and PMC in the absence of NoPMC clinical data.
Main Methods:
- Simulated motion artifacts were generated using nonuniform Fourier transforms on artifact-free MRI data.
- The simulation's accuracy in reproducing blurring, ringing, and ghosting artifacts was validated against real NoPMC images.
- Perivascular space (PVS) volume fraction in white matter was used as a quantitative measure of PVS visibility.
Main Results:
- Simulated motion significantly degraded image sharpness, PVS volume fraction, and background noise.
- The simulation accurately reproduced artifacts observed in real NoPMC images, showing significant correlations.
- Correlations between simulated and real PMC images were reduced and nonsignificant, indicating effective motion reduction by PMC.
Conclusions:
- The developed simulation method is a viable tool for investigating motion artifacts and PMC's impact on PVS visibility.
- Prospective motion correction (PMC) can mitigate motion-induced systematic bias in PVS volume fraction measurements.
