Automatic coil selection for streak artifact reduction in radial MRI.

Area of Science:

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

Background:

• Radial MRI can suffer from streaking artifacts originating from the field of view edges.
• These artifacts worsen in scanner regions with weakened gradient strengths, impacting image quality.
• Current methods for artifact reduction, like disabling outer coils or manual inspection, are insufficient or impractical.

Purpose of the Study:

• To develop an automated postprocessing method for identifying and excluding corrupted coil data in radial MRI.
• To improve image quality by mitigating streaking artifacts in dynamic contrast-enhanced MRI.

Main Methods:

• A novel postprocessing algorithm was developed to automatically detect streaking artifacts in individual coil images.
• The method identifies coils with high artifact levels for exclusion during image reconstruction.
• The technique was validated on in vivo dynamic contrast-enhanced MRI datasets using a 3D hybrid radial sequence.

Main Results:

• The automated method successfully identified coils contributing to streaking artifacts.
• Exclusion of identified coil data led to substantial improvements in image quality.
• Reconstructed images showed excellent agreement with those obtained using manual coil selection.

Conclusions:

• The proposed automated postprocessing method effectively reduces streaking artifacts in radial MRI.
• This strategy offers a practical and efficient solution for improving image quality in clinical settings.
• The findings support the routine use of this automated coil selection technique for enhanced MRI diagnostics.