Using self-consistency for an iterative trajectory adjustment (SCITA).

Area of Science:

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Image Reconstruction

Background:

• Radial MRI trajectories are susceptible to deviations, leading to image artifacts.
• Existing correction methods often require additional calibration scans, increasing scan time.
• Accurate trajectory information is crucial for high-quality MRI reconstruction.

Purpose of the Study:

• To develop and validate an iterative method for correcting radial trajectory deviations.
• To eliminate the need for additional calibration scans in radial MRI.
• To improve the accuracy and efficiency of radial image reconstruction.

Main Methods:

• Utilized the oversampled center of k-space in radially acquired data.
• Developed an iterative algorithm applying the GRAPPA operator to shift projections and enforce consistency.
• Validated the method through numerical simulations, phantom studies, and in vivo 3T MRI acquisitions.

Main Results:

• The iterative technique reliably corrected trajectory errors in all tested scenarios.
• Artifact removal was as effective as methods relying on additional calibration data.
• Demonstrated successful application in simulations, phantom, and in vivo radial MRI.

Conclusions:

• The proposed iterative method offers an effective solution for trajectory error correction in radial MRI.
• This approach obviates the necessity for supplementary calibration scans.
• Enhances the feasibility of accurate radial imaging reconstruction.