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Updated: Nov 10, 2025
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Attenuation of motion artifacts in fMRI using discrete reconstruction of irregular fMRI trajectories (DRIFT)
David B Parker1, Pascal Spincemaille2, Qolamreza R Razlighi2
1Department of Biomedical Engineering, Columbia University, New York City, NY, USA.
Purpose:
Numerous studies report motion as the most detrimental source of noise and artifacts in fMRI. Current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment but fail to address within volume contamination and spin-history artifacts. Prospective motion correction can prevent spin-history artifacts but currently cannot update the gradients fast enough to remove k-space filling artifacts, calling for a hybrid approach to fully address these problems.
Theory And Methods:
Motion can be mathematically formulated into the MR signal equation to describe the motion artifacts at their origin in k-space. From these equations, it is demonstrated that different motions have different effects on the signal. A novel motion correction algorithm is designed from these equations to remove motion-induced artifacts directly in k-space, discrete reconstruction of irregular fMRI trajectory (DRIFT). This method is evaluated rigorously using fMRI simulations and data from a rotating phantom inside the scanner.
Results:
The results indicate that although some motion types have negligible effects on the MR signal, others produce catastrophic and lasting artifacts even after motion cessation. In simulation, DRIFT is able to remove motion artifacts in the absence of spin history. In a phantom scan, DRIFT significantly attenuates the motion artifacts in the fMRI data.
Conclusion:
Neither prospective nor retrospective motion correction methods could completely remove the motion artifacts from the fMRI data. However, DRIFT, as a retrospective technique, when combined with prospective motion correction, can eliminate a significant portion of motion artifacts.
Insights
Functional MRI (fMRI) motion artifacts are a major challenge. A new retrospective method, discrete reconstruction of irregular fMRI trajectory (DRIFT), effectively reduces motion-induced noise when combined with prospective correction.
Area of Science:
- Neuroimaging
- Medical Physics
- Biomedical Engineering
Background:
- Motion is a primary source of artifacts in functional MRI (fMRI) data.
- Existing retrospective and prospective motion correction techniques are insufficient to fully address motion-induced noise and artifacts.
Purpose of the Study:
- To develop and evaluate a novel k-space-based motion correction algorithm for fMRI.
- To address limitations of current retrospective and prospective motion correction methods.
Main Methods:
- Formulated motion artifacts mathematically within the MR signal equation.
- Developed a novel retrospective algorithm, discrete reconstruction of irregular fMRI trajectory (DRIFT), for direct k-space artifact removal.
- Evaluated DRIFT using fMRI simulations and phantom data.
Main Results:
- Certain motion types cause significant, lasting artifacts in fMRI data.
- DRIFT successfully removed motion artifacts in simulations without spin history.
- DRIFT significantly reduced motion artifacts in phantom scan data.
Conclusions:
- Neither prospective nor retrospective methods alone completely eliminate fMRI motion artifacts.
- Combining the novel retrospective DRIFT method with prospective motion correction significantly reduces motion artifacts.
